diff --git a/.env.example b/.env.example new file mode 100644 index 0000000000000000000000000000000000000000..fbeac41539147a2c4ac9e5f7f53436d1b7358295 --- /dev/null +++ b/.env.example @@ -0,0 +1,78 @@ +# MI300X non-negotiable runtime environment (mindxtrain2.md §13) +PYTORCH_ROCM_ARCH=gfx942 +HSA_NO_SCRATCH_RECLAIM=1 +HIP_FORCE_DEV_KERNARG=1 +GPU_MAX_HW_QUEUES=1 +NVTE_CK_USES_BWD_V3=1 +NVTE_CK_IS_V3_ATOMIC_FP32=1 +PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1 +NCCL_MIN_NCHANNELS=112 + +# Operator runtime — inference backend selection +MINDXTRAIN_BACKEND=vllm +MINDXTRAIN_VLLM_BASE_URL=http://localhost:8000/v1 +MINDXTRAIN_OPENAI_BASE_URL=https://api.openai.com/v1 +MINDXTRAIN_OPENAI_API_KEY= +MINDXTRAIN_PERSONA_PATH=/home/hacker/mindX/personas/codephreak.json + +# Teacher model used by data/synth.py for synthetic-data rollouts +MINDXTRAIN_TEACHER_BASE_URL=http://localhost:8000/v1 +MINDXTRAIN_TEACHER_MODEL=Qwen/Qwen3.5-8B + +# External storage providers +HF_TOKEN= +HF_HUB_USERNAME= +LIGHTHOUSE_API_KEY= +LIGHTHOUSE_BASE_URL=https://node.lighthouse.storage +IPFS_API_URL=http://127.0.0.1:5001 + +# Provenance / on-chain +MINDXTRAIN_FACILITATOR_URL=https://facilitator.bankon.io/x402 +MINDXTRAIN_REGISTRY_ADDR= +MINDXTRAIN_BASE_RPC_URL=https://sepolia.base.org +MINDXTRAIN_ALGORAND_ALGOD_URL=https://mainnet-api.algonode.cloud +MINDXTRAIN_ALGORAND_INDEXER_URL=https://mainnet-idx.algonode.cloud +MINDXTRAIN_BANKON_ENS_URL=https://ens.bankon.pythai.net + +# Deploy targets +MINDXTRAIN_API_BASE_URL=https://mindx.pythai.net +MINDXTRAIN_AGENTICPLACE_URL=https://agenticplace.pythai.net + +# Public /v1/training/jobs bearer-auth secret. Leave blank for an open +# dev-mode operator; set in production so mindX agents and external callers +# must present `Authorization: Bearer `. Generate with `openssl rand -hex 32`. +MINDXTRAIN_API_KEY= + +# mindX home — used by `data.source: mindx_dreams` recipes if they don't +# pin an absolute `data.path`. +MINDXTRAIN_MINDX_HOME=/home/hacker/mindX + +# Local registry (deploy hot-swap) +MINDXTRAIN_REGISTRY_PATH=./out/registry.json + +# Observability (optional) +MINDXTRAIN_OTEL_ENDPOINT= +MINDXTRAIN_PROMETHEUS_PORT=9090 + +# Coach UI deploy — GitHub source-tree push +GITHUB_TOKEN= +GITHUB_REPO=professor-codephreak/mindXtrain +GITHUB_DEFAULT_BRANCH=main +GITHUB_AUTHOR_NAME=mindXtrain bot +GITHUB_AUTHOR_EMAIL=noreply@pythai.net + +# Coach UI deploy — AMD Dev Cloud MI300X provisioning +AMD_DEV_CLOUD_TOKEN= +AMD_DEV_CLOUD_API_BASE=https://api.devcloud.amd.com +AMD_DEV_CLOUD_SSH_KEY_ID=56216059 +AMD_DEV_CLOUD_REGION=atl1 +AMD_DEV_CLOUD_SIZE=gpu-mi300x8-1536gb-devcloud +AMD_DEV_CLOUD_IMAGE=vllm-0-17-1 +AMD_DEV_CLOUD_TAGS=mindx,train,aglm,agenticplace,pythai + +# Coach UI deploy — existing droplet sync (also used by provisioning to ssh in) +DROPLET_HOST= +DROPLET_USER=root +DROPLET_SSH_KEY=~/.ssh/id_ed25519 +DROPLET_REMOTE_PATH=/workspace/mindxtrain +DROPLET_CONTAINER=rocm/primus:v26.2 diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..30ff6f8befccd05c1b5c965626727011760752dc 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +docs/blueprints/Winning[[:space:]]the[[:space:]]AMD[[:space:]]x[[:space:]]lablab.ai[[:space:]]Developer[[:space:]]Hackathon[[:space:]]with[[:space:]]mindX[[:space:]]and[[:space:]]xtrain_[[:space:]]A[[:space:]]Three-Track[[:space:]]Strategic[[:space:]]Brief.pdf filter=lfs diff=lfs merge=lfs -text +docs/blueprints/mindXtrain[[:space:]]Framework_[[:space:]]GLM-5.1,[[:space:]]aGLM[[:space:]]Lineage,[[:space:]]and[[:space:]]Qwen3.5[[:space:]]Primary[[:space:]]Base[[:space:]]Strategy.pdf filter=lfs diff=lfs merge=lfs -text +docs/blueprints/mindXtrain_[[:space:]]Production[[:space:]]Blueprint[[:space:]]for[[:space:]]the[[:space:]]AMD[[:space:]]and[[:space:]]lablab.ai[[:space:]]Hackathon.pdf filter=lfs diff=lfs merge=lfs -text diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml new file mode 100644 index 0000000000000000000000000000000000000000..a18fd91bc80bfc4deb12315d9b2caf0ad10d019e --- /dev/null +++ b/.github/workflows/ci.yml @@ -0,0 +1,68 @@ +name: ci + +on: + push: + branches: [main] + pull_request: + branches: [main] + +jobs: + lint-type-test: + runs-on: ubuntu-24.04 + steps: + - uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v3 + with: + version: "0.8.18" + + - name: Set up Python 3.12 + run: uv python install 3.12 + + - name: Sync workspace (base + ml + data for the runtime checks) + run: uv sync --frozen --extra ml --extra data + + - name: Ruff (package + tests) + run: uv run ruff check mindxtrain/ tests/ + + - name: Mypy (strict-checked paths only) + run: uv run mypy mindxtrain/config mindxtrain/provenance + + - name: Pytest + run: uv run pytest -q + + build-container: + # Build the Containerfile but don't push — proves the recipe still builds + # on each PR. Push-to-GHCR happens on tag, in a separate job. + runs-on: ubuntu-24.04 + needs: lint-type-test + if: github.event_name == 'push' && github.ref == 'refs/heads/main' + steps: + - uses: actions/checkout@v4 + - name: Build container (no push) + run: docker build -f Containerfile -t mindxtrain:ci . + + publish-container: + # On tags vX.Y.Z, push the container to GHCR. + runs-on: ubuntu-24.04 + needs: lint-type-test + if: startsWith(github.ref, 'refs/tags/v') + permissions: + contents: read + packages: write + steps: + - uses: actions/checkout@v4 + - name: Log in to GHCR + uses: docker/login-action@v3 + with: + registry: ghcr.io + username: ${{ github.actor }} + password: ${{ secrets.GITHUB_TOKEN }} + - name: Build + push (tag) + run: | + TAG="${GITHUB_REF##*/}" + IMAGE="ghcr.io/${GITHUB_REPOSITORY_OWNER,,}/mindxtrain" + docker build -f Containerfile -t "$IMAGE:$TAG" -t "$IMAGE:latest" . + docker push "$IMAGE:$TAG" + docker push "$IMAGE:latest" diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..fe9be241234879cc30177da422c4efeb497b8611 --- /dev/null +++ b/.gitignore @@ -0,0 +1,34 @@ +__pycache__/ +*.py[cod] +*.egg-info/ +.venv/ +.uv-cache/ +uv.lock.bak + +.mypy_cache/ +.pytest_cache/ +.ruff_cache/ + +.env +.env.* +!.env.example +creds.api +*.creds +secrets/ + +runs/ +checkpoints/ +out/ +*.safetensors +*.bin +*.pt +*.onnx + +.cache/ +autotune_plan.json +custmodel/runs/ + +# Agent working dirs (plans, memory, local settings) — never commit. +.claude/ + +.DS_Store diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 0000000000000000000000000000000000000000..cb86477261db7e0fa707e3ec7e05c00dca125e64 --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,82 @@ +# AGENTS.md + +Canonical entry point for any agent (Claude Code, Codex, Cursor, Copilot, etc.) working in this repository. Read this first. + +## TL;DR + +`mindxtrain` is a single-package Python framework that fine-tunes open-weight LLMs on AMD MI300X and serves them OpenAI-compatible. The differentiator is a **60-second AOT autotune probe** (`mindxtrain bench`) — the plan is fixed at training start; **JIT autotune is forbidden in the production loop**. + +The base install is CPU-only and runs the CLI, Coach UI, `bench --dry-run`, manifest verify, and the operator FastAPI. Heavyweight paths gate on opt-in `--extra` groups. + +## Where to look + +| You want to… | Read | +|---|---| +| Understand the architecture and invariants | [`CLAUDE.md`](CLAUDE.md), [`docs/architecture.md`](docs/architecture.md) | +| Take the repo from "code done" to "demo live" | [`HANDOFF.md`](docs/HANDOFF.md) (11 ordered steps) | +| Know what's real Python vs. what needs `--extra ` | [`docs/actualization_status.md`](docs/actualization_status.md) | +| Add a recipe / training backend / operator backend / training method | [`docs/development.md`](docs/development.md) §"Adding…" | +| Look up every CLI verb's options + exit codes | [`docs/cli.md`](docs/cli.md) | +| Look up every YAML field | [`docs/yaml_schema.md`](docs/yaml_schema.md) | +| Understand the autotune probe | [`docs/autotune.md`](docs/autotune.md) | +| Understand the Coach UI | [`docs/coach.md`](docs/coach.md) | +| Understand classroom / boardroom / dojo governance | [`docs/governance.md`](docs/governance.md) | +| See the frozen design briefs | [`docs/blueprints/`](docs/blueprints/) | + +## Verification gates (must all pass before pushing) + +```bash +uv run ruff check . +uv run mypy mindxtrain/config mindxtrain/provenance +uv run pytest -q # → 564 passed +``` + +CI runs the same three commands on Ubuntu 24.04 / Python 3.12 (CPU-only). + +## Non-negotiable invariants + +These are encoded in the schema/recipes; violating them is a deployment bug. Full reasoning in [`docs/development.md`](docs/development.md). + +1. **AOT-only** — no `torch.compile(mode="max-autotune")`, no JIT autotune in vLLM. The YAML field `autotune.policy: aot_only` is the contract. +2. **`hardware.gpus: Literal[1, 8]`** — 2/4-GPU MI300X FSDP groups hit asymmetric xGMI; schema rejects them at parse time. +3. **Seven MI300X env vars** are defaults in every recipe's `train.env` (`PYTORCH_ROCM_ARCH=gfx942`, `HSA_NO_SCRATCH_RECLAIM=1`, `HIP_FORCE_DEV_KERNARG=1`, `GPU_MAX_HW_QUEUES=1`, `NVTE_CK_USES_BWD_V3=1`, `NVTE_CK_IS_V3_ATOMIC_FP32=1`, `PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1`, `NCCL_MIN_NCHANNELS=112`). +4. **`extra: forbid` + `frozen: true`** on every Pydantic model. +5. **Solidity contracts are write-once** — no proxies, no `Ownable`, no admin keys, no setters. +6. **Numpy pinned `<2.0`** against `torch==2.9.1+rocm7.2.1.lw`. +7. **Lazy imports for optional deps** — `import mindxtrain.eval.harness` must succeed even without `--extra eval`. Error messages must include the exact `uv sync --extra ` to run. +8. **Reuse boundaries** — Codephreak persona JSON loads at runtime via `MINDXTRAIN_PERSONA_PATH` from `/home/hacker/mindX/`, not by copying bytes. Never reuse `/home/hacker/aglm/` (broken per its own README). +9. **Clean-room policy** — mindXtrain (and Coach) is a clean-room codebase: functionality from mindX or any external source is **reimplemented/adapted locally from behavior or spec, never copied byte-for-byte**. Consume foreign artifacts (dream corpus, persona) through runtime boundaries; do not vendor foreign code. mindXtrain and Coach **train models** — the dataset/persona/imprint machinery is mindXtrain-native. See [`CLAUDE.md`](CLAUDE.md) §"Clean-room policy". + +## Quick commands + +```bash +uv sync # base, CPU-only +uv sync --all-extras # everything except amd-quark +uv run mindxtrain --help # 9 verbs +uv run mindxtrain init --list # 12 built-in YAML recipes +uv run mindxtrain bench --dry-run --out plan.json # synthetic plan, no GPU +uv run mindxtrain receipt manifest.json --config run.yaml # BLAKE3 round-trip +uv run uvicorn mindxtrain.operator.app:app --port 8080 # → /coach/ UI +``` + +GPU verbs (`bench` without `--dry-run`, `train`, `quantize`, `serve`) require an AMD MI300X with ROCm 7.2.1 inside `rocm/primus:v26.2`. See [`HANDOFF.md`](docs/HANDOFF.md). + +## Available skills + +For Algorand-related work (the provenance/x402/ASA paths) the following skills are available and should be preferred over ad-hoc patterns: + +- `algorand-typescript`, `build-smart-contracts`, `test-smart-contracts`, `call-smart-contracts` — contract authoring/testing/deploy. +- `use-algokit-utils`, `use-algokit-cli`, `troubleshoot-errors`, `implement-arc-standards` — client-side AlgoKit work. +- `search-algorand-examples` — patterns from official Algorand repos. +- `algorand-ts-migration` — TEALScript / beta → Algorand TypeScript 1.0. +- `mindx` — for cross-cutting work that touches the broader mindX system. + +For the Solidity side (`contracts/`): + +- `foundry-framework`, `solidity-dev`, `solidity-style-guide`, `slither-analysis`, `echidna-fuzzer`, `gas-optimization`, `hardhat-framework`. + +## When in doubt + +- The schema is the source of truth for "what's a valid YAML." Run `uv run python -c "from mindxtrain.config.loader import load_config; load_config('path/to.yaml')"`. +- The blueprints under `docs/blueprints/` are frozen — do not edit them; they are the historical specification. +- Per-module status: `docs/actualization_status.md`. If a module says "stub" there, do not silently turn it on; it's stubbed deliberately. diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 0000000000000000000000000000000000000000..a24633e9d5c8b79051f44b4feb6f322ab7a61c99 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,118 @@ +# CLAUDE.md + +This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository. + +## What this is + +`mindxtrain` is a single-package Python training framework for fine-tuning open-weight LLMs on AMD MI300X and serving them through an OpenAI-compatible API. The architectural differentiator is a **60-second AOT autotune probe** (`mindxtrain bench`) that fixes attention backend (CK vs Triton), GEMM heuristic, and RCCL config at training start — **JIT autotune is forbidden in the production training loop**. + +The base install is CPU-only and runs the CLI, Coach UI, `bench --dry-run`, manifest verify, and the operator FastAPI; heavyweight paths gate on opt-in dep groups. + +## Commands + +The repo uses `uv` with Python 3.12 (pinned `>=3.12,<3.13`). All commands run from the repo root. + +```bash +uv sync # base install (CPU-only; 564 tests pass) +uv sync --extra ml --extra eval --extra data # opt into heavyweight groups +uv sync --all-extras # everything except amd-quark (ships in container) + +# Standard local cycle — CI runs the same: +uv run ruff check . # lint +uv run mypy mindxtrain/config mindxtrain/provenance # mypy --strict (only these two) +uv run pytest -q # → 564 passed +uv run pytest tests/test_config_schema.py -q # single test file +uv run pytest tests/test_config_schema.py::test_xgmi_2gpu_rejected # single test + +# CLI entry point (typer; 9 verbs): +uv run mindxtrain --help +uv run mindxtrain init --list # list 12 built-in YAML recipes +uv run mindxtrain init --template qwen3_8b_sft_lora --out run.yaml +uv run mindxtrain bench --dry-run --out plan.json # CPU-safe (real probe needs MI300X) +uv run mindxtrain receipt ./out/runs//manifest.json --config run.yaml + +# Operator FastAPI + Coach UI (no GPU required): +uv run uvicorn mindxtrain.operator.app:app --host 0.0.0.0 --port 8080 +# → http://localhost:8080/coach/ +``` + +GPU verbs (`bench` without `--dry-run`, `train`, `quantize`, `serve`) require an AMD MI300X with ROCm 7.2.1 inside `rocm/primus:v26.2`. The full operator path is in `docs/HANDOFF.md`. + +Solidity contracts live in `contracts/` (Foundry, solc 0.8.26): `forge install && forge test` from inside `contracts/`. + +## Architecture (concentric layers) + +The codebase is organized so each inner layer is consumed by the next, never the reverse: + +1. **CLI** (`mindxtrain/cli/main.py`, typer) — `init | bench | train | dataset prep | eval | quantize | serve | publish | receipt`. Never reaches into the training backend; consumes a Pydantic-validated config + `AutotunePlan` and dispatches downward. +2. **Autotune** (`mindxtrain/autotune/`) — the differentiator. Emits `AutotunePlan` JSON, AOT-only. +3. **Dataset** (`mindxtrain/data/`) — curate → dedupe (MinHash + SemDeDup) → filter → tokenize → pack → synth → verify. +4. **Training** (`mindxtrain/train/`) — backend dispatch (`dispatch.py` → axolotl / unsloth / torchtune / primus for MI300X subprocess; `trl_cpu` for CPU and `trl_local` for device-aware consumer-GPU/CPU-fallback, both in-process TRL). Methods: SFT, DPO, ORPO, GRPO, GSPO, RLHF, tool-use, CPT. +5. **Artifact + Integration** (`mindxtrain/{eval,deploy,storage,provenance,operator}`) — Quark FP8/MXFP4 → lm-eval-harness → HF Hub push → Lighthouse pin → mindX register → AgenticPlace → BANKON ENS → x402 metering → ERC-8004 attestation. + +Key end-to-end flow: `XTrainConfig` (Pydantic) + `AutotunePlan` → `dispatch_training()` → `checkpoint_dir/` → `eval.json` → `quantized/` → `manifest.json` (BLAKE3 of YAML+dataset+ckpt+eval, plus HF/Lighthouse/INFT/ASA pointers) → operator serves on `/v1/chat/completions`. `mindxtrain receipt` re-hashes and verifies the manifest round-trip. + +Recipes live as YAML at `mindxtrain/train/recipes/.yaml` and are auto-picked up by `mindxtrain init --list` and validated by `tests/test_config_schema.py::test_all_recipes_validate`. + +## Non-negotiable invariants + +These are encoded in the schema/recipes; violating them is a deployment bug, not a style issue. + +1. **AOT-only.** No `torch.compile(mode="max-autotune")` in production paths. No JIT autotune in vLLM (`VLLM_USE_TRITON_FLASH_ATTN=0` if needed). `autotune.policy: aot_only` is the YAML contract. +2. **`hardware.gpus: Literal[1, 8]` only.** 2/4-GPU MI300X FSDP groups hit asymmetric xGMI bandwidth — schema rejects them at parse time. (Tested in `test_config_schema.py::test_xgmi_2gpu_rejected`, `test_distributed.py`.) +3. **Seven MI300X env vars** are defaults in every recipe's `train.env` (autotune plan may override values, never remove keys): `PYTORCH_ROCM_ARCH=gfx942`, `HSA_NO_SCRATCH_RECLAIM=1`, `HIP_FORCE_DEV_KERNARG=1`, `GPU_MAX_HW_QUEUES=1`, `NVTE_CK_USES_BWD_V3=1`, `NVTE_CK_IS_V3_ATOMIC_FP32=1`, `PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1`, `NCCL_MIN_NCHANNELS=112`. +4. **`extra: forbid` + `frozen: true`** on every Pydantic model — unknown YAML keys raise `ValidationError`; loaded configs are immutable. +5. **Solidity contracts are write-once.** No proxies, no `Ownable`, no admin keys, no setters in `contracts/src/{mindxtrain_registry,x402_receiver}.sol`. Rotating any parameter requires a fresh deploy. +6. **Numpy pinned `<2.0`** against `torch==2.9.1+rocm7.2.1.lw`. +7. **Container is `rocm/primus:v26.2`**; SHA256 digest snapshot in `ops/containerfiles/digest.lock`. + +## Lazy-import pattern (mandatory for optional deps) + +Optional dep groups: `ml` (trl, transformers, peft, accelerate, datasets), `eval` (lm-eval, lighteval, inspect-ai, jinja2), `data` (datasketch, sentence-transformers, faiss-cpu, pyarrow), `serve` (vllm), `chain` (web3, py-algorand-sdk, huggingface-hub), `obs` (opentelemetry-sdk, prometheus-client, psutil). + +Every module that wants an optional dep guards the import inside the function that needs it. `import mindxtrain.eval.harness` must always succeed even without `--extra eval`. Error messages must include the exact `uv sync --extra ` to run. New modules taking optional deps must follow this pattern. + +## Clean-room policy (non-negotiable) + +mindXtrain is a **clean-room** codebase: functionality that originates in another +project (mindX, external repos, reference implementations) is **reimplemented or +adapted locally from observed behavior or a spec — never copied byte-for-byte**. The +in-tree code is owned by mindXtrain and untainted by foreign source. + +When you need something from another codebase: +1. **Load it at runtime** via a documented env var / file path (e.g. the Codephreak + persona via `MINDXTRAIN_PERSONA_PATH`), or +2. **Reimplement the behavior locally** in mindXtrain style, citing the source as a + *reference*, not pasting it. + +This applies to the whole product, including the Coach: **mindXtrain and Coach train +models**, and the training/dataset/persona machinery is mindXtrain-native — it consumes +mindX artifacts (dream corpus, persona) through boundaries, it does not vendor mindX code. + +## Reuse boundaries + +- **From `/home/hacker/mindX/`** (production codebase): Codephreak persona JSON loaded at runtime via `MINDXTRAIN_PERSONA_PATH`. Do not copy file bytes — load via env var (clean-room). +- **Not** from `/home/hacker/aglm/` — broken per its own README. + +## Adding things + +- **New recipe** → drop YAML at `mindxtrain/train/recipes/.yaml`; `test_all_recipes_validate` picks it up. +- **New training backend** → add `mindxtrain/train/backend_.py` exposing `run_(cfg, plan, out_dir) -> Path`; wire into `train/dispatch.py`; add to `TrainingBackend` literal in `config/schema.py`. +- **New operator backend** → subclass `Backend` in `mindxtrain/operator/backends/.py` decorated `@register_backend("")`; side-effect import from `models/registry.py`; add runtime branch in `operator/app.py::chat_completions`. +- **New training method** → add `_MethodBase` subclass in `config/schema.py` with `kind: Literal[""]`; extend `TrainMethod` discriminated union; add `train/.py` runner; update dispatch; add a recipe. + +## Documentation hub + +| Doc | What it covers | +|-----|----------------| +| `docs/NAV.md` | **Docs index** — start here; one line per doc, grouped. | +| `docs/HANDOFF.md` | 11-step operator checklist (local → MI300X droplet → submission). | +| `docs/architecture.md` | 5-layer architecture + MI300X invariants + data flow. | +| `docs/development.md` | Toolchain, optional-deps, lazy-import pattern, debugging table. | +| `docs/actualization_status.md` | Per-module map of what's real vs. requires extras. | +| `docs/autotune.md` | The 60-second AOT probe — the differentiator. | +| `docs/cli.md` | Every verb with synopsis, options, exit codes. | +| `docs/yaml_schema.md` | Every field of the 10-section `XTrainConfig`. | +| `docs/coach.md` | Interactive `/coach/` web UI bundled in the operator. | +| `docs/governance.md` | classroom / boardroom (any-N consensus) / dojo (prime-N dispute settlement). | +| `docs/blueprints/` | Frozen source design briefs (the spec the project was built against). | diff --git a/Containerfile b/Containerfile new file mode 100644 index 0000000000000000000000000000000000000000..d0037c6b248e8e5e12baf65e75155aca7cd27b5d --- /dev/null +++ b/Containerfile @@ -0,0 +1,25 @@ +# Default-discovered Containerfile for `podman build .` at the repo root. +# Canonical content also lives at ops/containerfiles/containerfile_train. +# +# Pin: rocm/primus:v26.2 ships ROCm 7.2.1, PyTorch 2.9.1, Primus-Turbo with FlashAttention, +# AITER, and hipBLASLt. SHA256 digest in ops/containerfiles/digest.lock. +FROM docker.io/rocm/primus:v26.2 + +ENV PYTORCH_ROCM_ARCH=gfx942 \ + HSA_NO_SCRATCH_RECLAIM=1 \ + HIP_FORCE_DEV_KERNARG=1 \ + GPU_MAX_HW_QUEUES=1 \ + NVTE_CK_USES_BWD_V3=1 \ + NVTE_CK_IS_V3_ATOMIC_FP32=1 \ + PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1 \ + NCCL_MIN_NCHANNELS=112 + +WORKDIR /workspace/mindxtrain + +RUN pip install --no-cache-dir uv + +COPY . /workspace/mindxtrain +RUN uv sync --frozen --no-dev + +ENTRYPOINT ["uv", "run", "mindxtrain"] +CMD ["--help"] diff --git a/FORK.json b/FORK.json new file mode 100644 index 0000000000000000000000000000000000000000..38668d7df0ebad10cee6900b28d28460ca32268d --- /dev/null +++ b/FORK.json @@ -0,0 +1,12 @@ +{ + "kind": "source fork (code), mindX-specific line", + "upstream_repo": "https://github.com/Professor-Codephreak/mindXtrain", + "upstream_commit": "661bd411738d11e633b25c681bbd5676556bab7d", + "forked_at_utc": "2026-09-14T20:11:14Z", + "licence": "Apache-2.0 (LICENSE, NOTICE and upstream notices carried unchanged)", + "excluded": "uncommitted working-tree changes of the upstream checkout (only the committed tree at upstream_commit is here)", + "readme_modified": "a fork header and Hugging Face model-card metadata were prepended; the upstream text is unchanged below it", + "files": 338, + "tree_sha256": "08823c89092f14535575ac8b9493b88912cf19ffa699acc1de35e46848020f68", + "proceeds_here": "mindX-specific training work; the GitHub upstream remains agnostic" +} \ No newline at end of file diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..08a105b7e9d0614b29a7f5de5360e4cf0f073eae --- /dev/null +++ b/LICENSE @@ -0,0 +1,190 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Support. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. 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Adopters fine-tuning GLM-5.1 must preserve this +notice in their downstream artifact. + +---------------------------------------------------------------------- + +MIT License + +Copyright (c) 2025 Z.ai + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/NOTICE b/NOTICE new file mode 100644 index 0000000000000000000000000000000000000000..9b9bea4103ebbe8c12c34507c386e56792c304b0 --- /dev/null +++ b/NOTICE @@ -0,0 +1,58 @@ +mindxtrain +Copyright (c) 2026 BANKON / mindX + +This product includes software developed by the mindX project, distributed +under the Apache License, Version 2.0 (see LICENSE). + +---------------------------------------------------------------------- + +This product depends on software from the following upstream projects. +Their copyright and license notices are reproduced below as required by +their respective licenses. + +* Qwen3.5 / Qwen3.6 model weights — Alibaba Cloud + License: Apache License 2.0 + Source: https://huggingface.co/Qwen/ + +* GLM-5.1 model weights — Z.ai (formerly Zhipu AI) + License: MIT License (see LICENSE-MIT-upstream-glm51) + Source: https://huggingface.co/zai-org/ + +* DeepSeek V3.2 model weights — DeepSeek + License: DeepSeek License + Source: https://huggingface.co/deepseek-ai/ + +* Mistral Large 3 model weights — Mistral AI + License: Mistral Research License / Apache 2.0 (per release) + Source: https://huggingface.co/mistralai/ + +* Phi-4-mini model weights — Microsoft Research + License: MIT License + Source: https://huggingface.co/microsoft/ + +* Instella-3B-Instruct model weights — AMD + License: Apache License 2.0 + Source: https://huggingface.co/amd/Instella-3B-Instruct + +* AMD ROCm, Primus, AITER, Composable Kernel, hipBLASLt, RCCL — AMD + License: MIT / Apache 2.0 (per project) + Source: https://github.com/ROCm/ + +* PyTorch — Linux Foundation / contributors + License: BSD 3-Clause + Source: https://github.com/pytorch/pytorch + +* vLLM — vllm-project + License: Apache License 2.0 + Source: https://github.com/vllm-project/vllm + +* TRL, transformers, datasets, accelerate, peft — Hugging Face + License: Apache License 2.0 + Source: https://github.com/huggingface/ + +* Lighthouse Storage SDK — Lighthouse Web3 Inc. + License: MIT + Source: https://github.com/lighthouse-web3/ + +The full text of each upstream license is preserved in their respective +upstream repositories. diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..6d6bc8f7f4e798e494b4352352fb853f2e468171 --- /dev/null +++ b/README.md @@ -0,0 +1,132 @@ +--- +license: apache-2.0 +library_name: mindxtrain +pipeline_tag: text-generation +tags: +- mindx +- mindxtrain +- training-framework +- lora +- cpu-training +- proof-of-recall +datasets: +- PYTHAI/mindXascension +- PYTHAI/mindX-docs +--- + +> **mindXtrain for mindX — the Hugging Face fork.** This repository is the **mindX-specific** line of mindXtrain, +> forked on 2026-09-14 from the agnostic upstream +> [github.com/Professor-Codephreak/mindXtrain](https://github.com/Professor-Codephreak/mindXtrain) at commit +> [`661bd41`](https://github.com/Professor-Codephreak/mindXtrain/commit/661bd411738d11e633b25c681bbd5676556bab7d) (provenance in [`FORK.json`](FORK.json)). +> The upstream stays agnostic; mindX-specific training work proceeds **here**: +> +> ```bash +> git clone https://huggingface.co/PYTHAI/mindXtrain +> ``` +> +> What this line trains for: the mindX lineage ([`PYTHAI/mindXascension`](https://huggingface.co/datasets/PYTHAI/mindXascension)), +> built from mindX's doctrine ([`PYTHAI/mindX-docs`](https://huggingface.co/datasets/PYTHAI/mindX-docs), with +> [the mapping](https://huggingface.co/datasets/PYTHAI/mindX-docs/blob/main/MAPPING.md)); the last accepted generation is +> [`PYTHAI/mindXtrain39`](https://huggingface.co/PYTHAI/mindXtrain39). The Hub footprint is mapped in +> [`examples/mindx/HUGGINGFACE_MAP.md`](examples/mindx/HUGGINGFACE_MAP.md). The upstream README follows unchanged. + +# mindxtrain + +Production training framework for fine-tuning open-weight LLMs on AMD MI300X +and serving them through an OpenAI-compatible API. Single ordered package, +canonical layout per [`docs/blueprints/mindXtrain2.md`](docs/blueprints/mindXtrain2.md) +§Part 4. + +The single architectural feature that distinguishes mindxtrain from Axolotl, +LLaMA-Factory, Unsloth, torchtune and Primus is its **60-second AOT autotune +probe**: CK-vs-Triton attention, hipBLASLt heuristic, RCCL config — the plan +is fixed at training start, JIT autotune is forbidden in the production loop. + +**Status**: production deployment in progress. The CPU-only base install passes +its full pytest suite (ruff + mypy clean); with the training extras installed the +suite is 672 green. Many modules ship as real Python on a CPU-only laptop; +heavyweight training, eval, and quantization paths gate on opt-in extra dep +groups. See [`docs/actualization_status.md`](docs/actualization_status.md) for the +per-module map and [`HANDOFF.md`](docs/HANDOFF.md) for the operator checklist. + +## Where this runs + +- **Operator + Coach UI:** [https://mindx.pythai.net/coach](https://mindx.pythai.net/coach) +- **Public training-jobs API:** `https://mindx.pythai.net/v1/training/jobs` + (bearer auth via `MINDXTRAIN_API_KEY`) +- **mindX self-training loop:** mindX's dream cycle writes JSONL training + data; this framework consumes it via the `mindx_dreams` data source and + fine-tunes a small fallback model on a single MI300X. + +## Prove it trains + +mindXtrain doesn't just assert that training works — it proves recall. The +[**dcoach**](docs/dcoach.md) proof loop (`/coach/dcoach`) imprints a persona onto a +tiny model on CPU, then measures whether the model *recalls* it: the **classroom** +scores recall before vs after training, the **boardroom** rules success or failure, +and the verdict feeds an **autotune feedback loop** that tunes the next run. A clean +CPU run reports a positive imprint Δ (e.g. recall 0.07 → 0.28) and an approved +verdict. [`docs/NAV.md`](docs/NAV.md) is the full documentation hub. + +## Quickstart + +```bash +uv sync # base install +uv run pytest -q # → 564 passed +uv run mindxtrain --help # 9 verbs +uv run mindxtrain init --template qwen3_8b_sft_lora --out run.yaml +uv run mindxtrain bench --dry-run --out plan.json +uv run uvicorn mindxtrain.operator.app:app --host 0.0.0.0 --port 8080 +# open http://localhost:8080/coach/ for the interactive UI +``` + +To unlock training / eval / quantize / publish, install the matching dep group: + +```bash +uv sync --extra ml --extra eval --extra data # train + eval + curate +# or +uv sync --all-extras # everything except amd-quark +``` + +GPU steps (`bench` without `--dry-run`, `train`, `quantize`, `serve`) require +an AMD MI300X with ROCm 7.2.1; run inside `rocm/primus:v26.2`. The full +operator checklist lives in [`HANDOFF.md`](docs/HANDOFF.md). + +## Layout + +``` +mindxtrain/{cli,config,data,models,train,eval,autotune, + operator,storage,provenance,deploy,budget}/ # 99 modules +contracts/ Foundry workspace for ERC-8004 attestation registry +ops/ containerfiles, compose, k8s, vmm, gensyn +tests/ pytest suite — 566 tests, CPU-only smoke +examples/ demo YAML configs +docs/ user-facing documentation + frozen blueprints +scripts/ dev helpers +``` + +## Documentation + +| Doc | What it covers | +|-----|----------------| +| [`HANDOFF.md`](docs/HANDOFF.md) | **Operator checklist** — ordered steps from local setup to live deployment. | +| [`docs/quickstart.md`](docs/quickstart.md) | Install + base-vs-extras command tour. | +| [`docs/architecture.md`](docs/architecture.md) | Canonical layout + 5-layer architecture + MI300X invariants. | +| [`docs/actualization_status.md`](docs/actualization_status.md) | Per-module map of what's real vs. requires extras. | +| [`docs/autotune.md`](docs/autotune.md) | The 60-second AOT probe — the architectural differentiator. | +| [`docs/coach.md`](docs/coach.md) | Interactive `/coach/` web UI bundled in the operator. | +| [`docs/dcoach.md`](docs/dcoach.md) | The dcoach proof loop — prove a CPU model recalls its training; decentralized-training fit. | +| [`docs/cli.md`](docs/cli.md) | Every `mindxtrain` verb with synopsis, options, exit codes. | +| [`docs/yaml_schema.md`](docs/yaml_schema.md) | Every field of the 10-section `XTrainConfig`. | +| [`docs/benchmarks.md`](docs/benchmarks.md) | Target metrics + the 7-cell framework comparison. | +| [`docs/development.md`](docs/development.md) | Toolchain, optional-deps, lazy-import pattern, invariants. | +| [`docs/blueprints/`](docs/blueprints/) | Source design briefs (frozen specification). | +| [`llm.txt`](llm.txt) | Orientation for another model — what is measured, what is not, the traps. | +| [`examples/mindx/`](examples/mindx/HUGGINGFACE_MAP.md) | Example consumer — mindX on the Hugging Face Hub: its lineage, [docs dataset + mapping](https://huggingface.co/datasets/PYTHAI/mindX-docs/blob/main/MAPPING.md), Spaces and licence-pinned base models. The framework stays agnostic. | + +## License + +Apache-2.0. See [LICENSE](LICENSE), [NOTICE](NOTICE), and the upstream-license +notices in [`LICENSE-MIT-upstream-glm51`](LICENSE-MIT-upstream-glm51) and +[`LICENSE-NOTICE.md`](docs/LICENSE-NOTICE.md). Version history in +[`CHANGELOG.md`](docs/CHANGELOG.md). diff --git a/WordPress.agent.zip b/WordPress.agent.zip new file mode 100644 index 0000000000000000000000000000000000000000..32cbcfcef765921b0fbae5843326da62a4182cf1 --- /dev/null +++ b/WordPress.agent.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8f4b50160d20c34831212970cd5a042523a4c3ff37dc22efaae4c5129de088b1 +size 38690 diff --git a/compose.yaml b/compose.yaml new file mode 100644 index 0000000000000000000000000000000000000000..77d0a5dfc1ddc67bfa90cd0953c92a8f65e06b12 --- /dev/null +++ b/compose.yaml @@ -0,0 +1,48 @@ +# Default-discovered compose file for `podman-compose up` at the repo root. +# Wires the operator FastAPI app -> vLLM-ROCm on the MI300X droplet. +# Canonical dev stack also lives at ops/compose/compose_dev.yaml. + +services: + vllm: + image: docker.io/rocm/vllm-dev:rocm7.2.1 + container_name: mindxtrain-vllm + ipc: host + devices: + - /dev/kfd + - /dev/dri + group_add: + - video + cap_add: + - SYS_PTRACE + security_opt: + - seccomp=unconfined + environment: + PYTORCH_ROCM_ARCH: gfx942 + HSA_NO_SCRATCH_RECLAIM: "1" + HIP_FORCE_DEV_KERNARG: "1" + GPU_MAX_HW_QUEUES: "1" + volumes: + - ./out/runs:/workspace/runs:ro + command: > + vllm serve /workspace/runs/latest/quantized + --tensor-parallel-size 1 + --max-model-len 8192 + --port 8000 + ports: + - "8000:8000" + + operator: + build: + context: . + dockerfile: Containerfile + container_name: mindxtrain-operator + depends_on: + - vllm + environment: + MINDXTRAIN_BACKEND: vllm + MINDXTRAIN_VLLM_BASE_URL: http://vllm:8000/v1 + MINDXTRAIN_PERSONA_PATH: /home/hacker/mindX/personas/codephreak.json + command: > + uvicorn mindxtrain.operator.app:app --host 0.0.0.0 --port 8080 + ports: + - "8080:8080" diff --git a/contracts/.gitignore b/contracts/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..7ed0d86bc3e604c57bce2cbcac7131b3c59ae493 --- /dev/null +++ b/contracts/.gitignore @@ -0,0 +1,4 @@ +out/ +cache/ +broadcast/ +lib/ diff --git a/contracts/README.md b/contracts/README.md new file mode 100644 index 0000000000000000000000000000000000000000..795e3f6963bd4963066ec85f71436ce397a60f85 --- /dev/null +++ b/contracts/README.md @@ -0,0 +1,32 @@ +# mindXtrain on-chain contracts + +Foundry workspace for the immutable, no-admin, no-proxy contracts that anchor +mindXtrain run receipts and receive x402 settlement proofs. + +## Contracts + +- `src/mindxtrain_registry.sol` — write-once anchoring of `(yamlHash, datasetCidHash, checkpointCidHash, evalReportHash)` per `runId`. +- `src/x402_receiver.sol` — records x402 settlement proofs from a fixed facilitator address. + +Both follow the cypherpunk2048 standard: no upgradeable proxies, no `Ownable`, no admin keys, no pause function, no setters. Rotating any parameter requires a fresh deployment. + +## Day-2 setup (on the MI300X droplet, where Foundry installs alongside the training stack) + +```bash +curl -L https://foundry.paradigm.xyz | bash && foundryup +cd contracts +forge install foundry-rs/forge-std --no-git +forge build +forge test --gas-report +``` + +## Deploy + +```bash +export DEPLOYER_PRIVATE_KEY=0x... +export X402_FACILITATOR=0x... # parsec-wallet or Coinbase facilitator +export BASE_SEPOLIA_RPC_URL=https://sepolia.base.org +forge script script/Deploy.s.sol --rpc-url base_sepolia --broadcast --verify +``` + +Day-3 deploy moves to `--rpc-url base` (mainnet). diff --git a/contracts/foundry.toml b/contracts/foundry.toml new file mode 100644 index 0000000000000000000000000000000000000000..ca710208326ab7c94556b84af6820c9e2efadd57 --- /dev/null +++ b/contracts/foundry.toml @@ -0,0 +1,28 @@ +[profile.default] +src = "src" +out = "out" +libs = ["lib"] +test = "test" +script = "script" +solc_version = "0.8.26" +optimizer = true +optimizer_runs = 200 +via_ir = false +fs_permissions = [{ access = "read", path = "./" }] + +[fmt] +line_length = 120 +tab_width = 4 +bracket_spacing = false +int_types = "long" + +[fuzz] +runs = 256 + +[rpc_endpoints] +base = "${BASE_RPC_URL}" +base_sepolia = "${BASE_SEPOLIA_RPC_URL}" + +[etherscan] +base = { key = "${BASESCAN_API_KEY}", chain = "base" } +base_sepolia = { key = "${BASESCAN_API_KEY}", chain = "base-sepolia" } diff --git a/contracts/script/Deploy.s.sol b/contracts/script/Deploy.s.sol new file mode 100644 index 0000000000000000000000000000000000000000..6fe78cd90ac56b025d3c9b4e458f6e0c9696bd24 --- /dev/null +++ b/contracts/script/Deploy.s.sol @@ -0,0 +1,23 @@ +// SPDX-License-Identifier: Apache-2.0 +pragma solidity ^0.8.26; + +import {Script, console} from "forge-std/Script.sol"; +import {MindXTrainRegistry} from "../src/mindxtrain_registry.sol"; +import {X402Receiver} from "../src/x402_receiver.sol"; + +contract Deploy is Script { + function run() external { + uint256 pk = vm.envUint("DEPLOYER_PRIVATE_KEY"); + address facilitator = vm.envAddress("X402_FACILITATOR"); + // ("algorand", 203977300) — Algorand mainnet USDC ASA + bytes32 assetIdHash = keccak256(abi.encode("algorand", uint256(203977300))); + + vm.startBroadcast(pk); + MindXTrainRegistry registry = new MindXTrainRegistry(); + X402Receiver receiver = new X402Receiver(facilitator, assetIdHash); + vm.stopBroadcast(); + + console.log("MindXTrainRegistry deployed at:", address(registry)); + console.log("X402Receiver deployed at:", address(receiver)); + } +} diff --git a/contracts/src/mindxtrain_registry.sol b/contracts/src/mindxtrain_registry.sol new file mode 100644 index 0000000000000000000000000000000000000000..a578417be685d53fb30dd83b6220e90edfae9677 --- /dev/null +++ b/contracts/src/mindxtrain_registry.sol @@ -0,0 +1,67 @@ +// SPDX-License-Identifier: Apache-2.0 +pragma solidity ^0.8.26; + +/// @title MindXTrainRegistry +/// @notice Write-once anchoring contract for mindXtrain run receipts. +/// @dev Cypherpunk2048 standard: immutable, no proxy, no Ownable, no admin keys, +/// no pause, no setter. Receipts cannot be overwritten or revoked. +contract MindXTrainRegistry { + struct Receipt { + bytes32 yamlHash; + bytes32 datasetCidHash; + bytes32 checkpointCidHash; + bytes32 evalReportHash; + address publisher; + uint64 timestamp; + } + + mapping(bytes32 => Receipt) private _receipts; + + event ReceiptAnchored( + bytes32 indexed runId, + address indexed publisher, + bytes32 yamlHash, + bytes32 datasetCidHash, + bytes32 checkpointCidHash, + bytes32 evalReportHash, + uint64 timestamp + ); + + error ReceiptAlreadyExists(bytes32 runId); + + function anchor( + bytes32 runId, + bytes32 yamlHash, + bytes32 datasetCidHash, + bytes32 checkpointCidHash, + bytes32 evalReportHash + ) external { + if (_receipts[runId].timestamp != 0) revert ReceiptAlreadyExists(runId); + Receipt memory r = Receipt({ + yamlHash: yamlHash, + datasetCidHash: datasetCidHash, + checkpointCidHash: checkpointCidHash, + evalReportHash: evalReportHash, + publisher: msg.sender, + timestamp: uint64(block.timestamp) + }); + _receipts[runId] = r; + emit ReceiptAnchored( + runId, + msg.sender, + yamlHash, + datasetCidHash, + checkpointCidHash, + evalReportHash, + r.timestamp + ); + } + + function get(bytes32 runId) external view returns (Receipt memory) { + return _receipts[runId]; + } + + function exists(bytes32 runId) external view returns (bool) { + return _receipts[runId].timestamp != 0; + } +} diff --git a/contracts/src/x402_receiver.sol b/contracts/src/x402_receiver.sol new file mode 100644 index 0000000000000000000000000000000000000000..130b89613c3a09c2253339e218b6ba4d326fe412 --- /dev/null +++ b/contracts/src/x402_receiver.sol @@ -0,0 +1,45 @@ +// SPDX-License-Identifier: Apache-2.0 +pragma solidity ^0.8.26; + +/// @title X402Receiver +/// @notice Receives x402 HTTP 402 payment proofs and validates Algorand settlement +/// hashes against an off-chain facilitator (parsec-wallet or Coinbase). +/// @dev Cypherpunk2048 standard: immutable, no proxy, no admin. The facilitator +/// address is fixed at deploy time; rotating it requires deploying a new +/// contract. +contract X402Receiver { + address public immutable facilitator; + bytes32 public immutable assetIdHash; // hash of (chain, asset_id) tuple, e.g. ("algorand", 203977300) + + mapping(bytes32 => bool) public seen; + + event PaymentValidated( + bytes32 indexed invoiceId, + bytes32 indexed settlementProof, + address indexed payer, + uint256 amount + ); + + error AlreadySeen(bytes32 invoiceId); + error NotFacilitator(address sender); + + constructor(address facilitator_, bytes32 assetIdHash_) { + facilitator = facilitator_; + assetIdHash = assetIdHash_; + } + + /// @notice The facilitator submits a settlement proof; this contract records it. + /// @dev Off-chain x402 flow: caller pays the Algorand asset, parsec-wallet + /// builds an EIP-712 attestation, the facilitator submits it here. + function recordSettlement( + bytes32 invoiceId, + bytes32 settlementProof, + address payer, + uint256 amount + ) external { + if (msg.sender != facilitator) revert NotFacilitator(msg.sender); + if (seen[invoiceId]) revert AlreadySeen(invoiceId); + seen[invoiceId] = true; + emit PaymentValidated(invoiceId, settlementProof, payer, amount); + } +} diff --git a/contracts/test/MindxtrainRegistry.t.sol b/contracts/test/MindxtrainRegistry.t.sol new file mode 100644 index 0000000000000000000000000000000000000000..666bfaa5ae39cd45f67368c589253f242b700422 --- /dev/null +++ b/contracts/test/MindxtrainRegistry.t.sol @@ -0,0 +1,70 @@ +// SPDX-License-Identifier: Apache-2.0 +pragma solidity ^0.8.26; + +import {Test} from "forge-std/Test.sol"; +import {MindXTrainRegistry} from "../src/mindxtrain_registry.sol"; + +contract MindXTrainRegistryTest is Test { + MindXTrainRegistry registry; + + function setUp() public { + registry = new MindXTrainRegistry(); + } + + function test_anchor_emits_event() public { + bytes32 runId = keccak256("run-1"); + bytes32 yamlHash = keccak256("yaml"); + bytes32 datasetHash = keccak256("dataset"); + bytes32 checkpointHash = keccak256("checkpoint"); + bytes32 evalHash = keccak256("eval"); + + vm.expectEmit(true, true, false, true); + emit MindXTrainRegistry.ReceiptAnchored( + runId, + address(this), + yamlHash, + datasetHash, + checkpointHash, + evalHash, + uint64(block.timestamp) + ); + registry.anchor(runId, yamlHash, datasetHash, checkpointHash, evalHash); + } + + function test_anchor_persists_receipt() public { + bytes32 runId = keccak256("run-2"); + registry.anchor( + runId, keccak256("y"), keccak256("d"), keccak256("c"), keccak256("e") + ); + MindXTrainRegistry.Receipt memory r = registry.get(runId); + assertEq(r.yamlHash, keccak256("y")); + assertEq(r.publisher, address(this)); + assertGt(r.timestamp, 0); + assertTrue(registry.exists(runId)); + } + + function test_anchor_rejects_overwrite() public { + bytes32 runId = keccak256("run-3"); + registry.anchor( + runId, keccak256("y"), keccak256("d"), keccak256("c"), keccak256("e") + ); + vm.expectRevert( + abi.encodeWithSelector(MindXTrainRegistry.ReceiptAlreadyExists.selector, runId) + ); + registry.anchor( + runId, keccak256("y2"), keccak256("d2"), keccak256("c2"), keccak256("e2") + ); + } + + function test_exists_false_for_unknown() public view { + assertFalse(registry.exists(keccak256("never-anchored"))); + } + + function testFuzz_anchor_distinct_run_ids(bytes32 a, bytes32 b) public { + vm.assume(a != b); + registry.anchor(a, bytes32(0), bytes32(0), bytes32(0), bytes32(0)); + registry.anchor(b, bytes32(0), bytes32(0), bytes32(0), bytes32(0)); + assertTrue(registry.exists(a)); + assertTrue(registry.exists(b)); + } +} diff --git a/docs/CHANGELOG.md b/docs/CHANGELOG.md new file mode 100644 index 0000000000000000000000000000000000000000..7a2b975c1aaa120e9cd33e03fbe95b2be5caf080 --- /dev/null +++ b/docs/CHANGELOG.md @@ -0,0 +1,200 @@ +# Changelog + +All notable changes to **mindxtrain** are documented in this file. The format +follows [Keep a Changelog](https://keepachangelog.com/en/1.1.0/), and this +project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). + +## [Unreleased] + +### Changed + +- **Removed hackathon framing from the public surfaces.** README, `pyproject.toml` + description, the package docstring, `CLAUDE.md`, and the active docs (autotune / + architecture / development / coach / yaml_schema / HANDOFF / NAV) no longer frame + mindXtrain as a hackathon submission; deleted `docs/HACKATHON.md` and + `docs/hackathon_submission.md`. The frozen `docs/blueprints/` and dated dev-blog + posts are preserved unchanged as the historical record. README now leads with the + dcoach proof loop. + +### Added + +- **dcoach page + prompt-tools + decentralized panel.** New `/coach/dcoach` workflow + page: one-click **Imprint & Prove** (persona/skills + advanced toggles) streams the + proof loop live (`POST /coach/api/dcoach/run`, SSE) and shows the classroom before/after + recall, boardroom verdict, and autotune-feedback next-params. A read-only + **decentralized-network panel** (`GET /coach/api/decentralized`) maps Prime Intellect / + Templar SN3 / Nous Psyche / Gensyn / Pluralis to how mindXtrain fits (AOT-only ⇒ Verde- + compatible; BLAKE3 receipt; x402; AgenticPlace). New `/coach/prompts` **prompt-tools** + page: craft a system prompt + few-shot demos → test against a base model (no training) → + evaluate (`POST /coach/api/eval/prompt`) → **make permanent** via Modelfile. Docs: + [dcoach.md](dcoach.md). +- **dcoach proof loop + clean-room eval tools.** Prove a CPU-trained model recalls + its training: `governance.proof_loop.run_proof_loop` chains compose-script → imprint- + train → probe before/after → **classroom** (`governance.classroom.evaluate_classroom`) + → **boardroom** decides → **autotune feedback** (`autotune.feedback`, nudges the next + run's params). New clean-room LlamaIndex-style evaluators (`eval.llama_evals`: + Semantic-Similarity / Correctness / Pairwise / Guideline — reuse existing embeddings + + the governance chat-judge). Endpoints `POST /coach/api/classroom/evaluate` and + `POST /coach/api/autotune/feedback`. + +- **Streaming chat + ollama controls** (Coach "Try the model" card). Responses now + **stream token-by-token** (the AI-SDK text-stream pattern over SSE) via + `POST /coach/api/chat/stream`, with a **model picker** (local models first, no more + silently-broken `:cloud` default) and **start/stop/status** for the local ollama + server (`/coach/api/ollama/{status,start,stop}`). Fixes the chat that returned no + visible response. Reference: `docs/Vercel AI SDK 6_ … .md`. + +- **Ollama Modelfile builder** (`mindxtrain.deploy.modelfile`) — render a valid + `Modelfile` from a typed `ModelfileSpec` (every instruction: FROM/SYSTEM/TEMPLATE/ + ADAPTER/LICENSE/MESSAGE/REQUIRES + the full PARAMETER catalogue). A standalone Coach + window at `/coach/modelfile` exposes a toggle + input for every instruction and + parameter; `POST /coach/api/modelfile/{build,create}` render it and run `ollama create`. +- **Default personas + skills** (`mindxtrain.data.personas`) — built-in personas + (codephreak / assistant / mentor) and toggleable **skills** (software engineer, platform + architect, bash, solidity) that mix in-domain exchanges into a script. The Create-script + card gains a persona picker + skill toggles; `derive_training_params` auto-tunes CPU + imprint epochs/grad_accum from the dataset size. + +- **Governance layer** (`mindxtrain.governance`) — classroom / boardroom / dojo, + a clean-room reimplementation of openmindx/openmind's Boardroom-consensus + + Dojo-evaluation behaviour. The **classroom** graduates an actor on its imprint; + the **boardroom** (any N role-based members) convenes on the promotion motion → + approved / rejected / disputed; the **dojo** settles a dispute with a panel of a + **prime number** of judges (odd prime ≥ 3 → no tie, always resolves). See + [`docs/governance.md`](governance.md). +- **Model-backed boardroom/dojo** (`governance.panel`) — members + judges + deliberate with real models over any OpenAI-compatible backend + (`deliberate`, `model_ballot`, `model_judge_ballot`). Coach **Boardroom** card + + `GET /coach/api/boardroom/presets`, `POST /coach/api/boardroom/convene`, + `POST /coach/api/dojo/settle` (model deliberation runs off the event loop). + +### Fixed + +- **Imprint recall now measured under the trained conditioning.** `probe_recall` accepts an + optional `system` prompt and the proof loop passes the persona's system prompt to both the + before and after probes — matching the system turn the script rows train under. Without it + the adapter was probed out of its learned distribution, understating (even inverting) the + imprint; the CPU proof loop now reports a strong positive Δ (e.g. 0.066→0.247) and an + APPROVED verdict. + +## [1.0.0] — 2026-06-11 + +First production release. **CPU training is active end-to-end**; the GPU +(MI300X / consumer) path is code-complete and validated on CPU dry-run + unit +tests, pending real ROCm hardware to execute. Honest per-module status is in +[`docs/actualization_status.md`](actualization_status.md). + +### Added (1.0.0) + +- **Device-aware local-GPU lane** (`trl_local`, + `mindxtrain.train.backend_trl_cpu.run_trl_local`). Auto-detects a consumer GPU + (CUDA or ROCm Radeon, bf16/fp16) and falls back to CPU; `trl_cpu` is the + force-CPU wrapper. Recipe `mindx_fallback_qwen3_1_5b_local`. Honest limit: + integrated Vega/`gfx90c` APUs are unsupported and fall back to CPU. +- **Verifiable training receipt.** Every operator/CPU run emits `manifest.json` + binding the frozen `AutotunePlan` hash to the checkpoint + config hashes + (`provenance.manifest.emit_receipt_for_run`); re-verified via `mindxtrain + receipt`, the `GET /coach/api/receipt/{run_id}` endpoint, and a Coach + "Verifiable receipt" card. Optional x402 metering gate on `/v1/training/jobs`. +- **Actor / persona / script + imprint.** Author a training *script* for an + *actor* (`mindxtrain.data.scripts`), ingest as `source: local`, and measure the + persona **imprint** by recall before/after training + (`mindxtrain.eval.imprint`, `mindxtrain imprint`, + `POST /coach/api/imprint/score`). Coach **Create script** card + + `POST/GET /coach/api/datasets`. Recipe `mindx_persona_imprint_local`. +- **mindX dream-cycle trigger** (`deploy.api_client.trigger_dream_ingestion`) — + hands an imprinted actor to mindX's `machine.dream` 8hr cycle via HTTP or an + inbox drop (clean-room: a pointer, never mindX code). `mindxtrain imprint + --trigger-dream`. +- **Coach live-training diagnostics** — accurate depiction with accordion + compression: rolling loss-chart window, per-step metrics + log accordions with + honest "showing last N" counts, system-metric sparklines, chat re-probe. +- **Autotune on real hardware** — runtime GPU-count autodetect (`rccl_probe`), + GEMM microbenchmark with timings (`gemm_probe`), `serve --to sglang`. +- **Clean-room policy** codified in `CLAUDE.md` + `AGENTS.md`: reimplement/adapt + locally, never copy mindX/external source bytes. + +### Added (pre-1.0 foundation) + +- **mindX self-training loop.** `mindx_dreams` dataset source adapter walks + `/data/memory/ltm/*/*_training.jsonl`, deduplicates by content hash, + and yields OpenAI-chat rows. Pure stdlib; no GPU/heavy deps to load + (`mindxtrain.data.sources.mindx_dreams`). +- **CPU training lane** (`mindxtrain.train.backend_trl_cpu.run_trl_cpu`). + Real TRL SFT/LoRA on CPU, produces a real HF-format checkpoint compatible + with `quantize`/`receipt`/`publish`. Wired via `TrainingBackend = "trl_cpu"`. +- **Public training-jobs API** at `/v1/training/jobs` with bearer auth + (`MINDXTRAIN_API_KEY`). Versioned facade over the same `RunRegistry` Coach + uses, so mindX agents and external clients become peer dispatchers + (`mindxtrain.operator.training_api`). +- **mindX fallback-swap caller** (`mindxtrain.deploy.api_client.swap_mindx_fallback_model`). + PATCHes mindX's `/v1/config/fallback-model` so a freshly published HF Hub + checkpoint becomes mindX's active default without a source edit. Pairs with + the mindX-side endpoint shipped in AgenticPlace/mindX@ad8193ea3. +- Two new YAML recipes: `mindx_fallback_qwen3_1_5b_sft_lora` (Qwen3-1.5B + LoRA on MI300X, the production target) and `mindx_fallback_qwen3_1_5b_cpu_smoke` + (SmolLM2-135M CPU smoke). +- `.env.example`: `MINDXTRAIN_API_KEY` and `MINDXTRAIN_MINDX_HOME`. + +### Changed + +- Schema: `DataSource` extends to include `"mindx_dreams"`; `DataCfg.hf_id` + is now defaultable with a `model_validator` requiring it only for + `source: "hf"`; `DataCfg.path` added (used by `local` + `mindx_dreams`). +- `HardwareCfg.gpus: Literal[0, 1, 8]` — `0` = CPU lane. +- Production URL flipped from `mindx.pythai.net/hackathon` to + `mindx.pythai.net/coach`. Hackathon-era references preserved in + `HACKATHON.md` and the build-in-public posts for the historical record. +- CI: ruff scoped to `mindxtrain/ tests/`; mypy points at the canonical + `mindxtrain/config mindxtrain/provenance` (fixing stale paths). Container + build added on push-to-main; GHCR publish on tag. + +### Notes + +- Adapter smoke against the real corpus saw 1051 unique rows in + `/home/hacker/mindX/data/memory` (corpus snapshot 2026-05-14). + +## [0.1.0] — 2026-05-06 + +Initial public release. Submitted to the AMD × lablab.ai Developer Hackathon +(build window May 4–10 2026, on-site finale May 9–10 in San Francisco). + +### Added + +- Single-package canonical layout per `docs/blueprints/mindxtrain2.md` §Part 4: + `mindxtrain/{cli,config,data,models,train,eval,autotune,operator,storage,provenance,deploy,budget}`. +- CLI with 8 verbs: `init`, `bench`, `train`, `eval`, `quantize`, `serve`, + `publish`, `receipt` (`mindxtrain.cli.main`). +- 60-second AOT autotune probe for AMD MI300X — the hackathon differentiator. + CK vs Triton attention selection + hipBLASLt GEMM heuristic + RCCL config + (`mindxtrain.autotune.{benchmark,attention_probe,gemm_probe,rccl_probe,plan}`). +- Pydantic v2 `XTrainConfig` with discriminated union over 9 training methods + (full, lora, qlora, dpo, orpo, grpo, gspo, kto, cpt) (`mindxtrain.config.schema`). +- 12 YAML training recipes covering Qwen3.5/Qwen3.6/Instella across + SFT-LoRA, full-FSDP, DPO, ORPO, GRPO, CPT, and VL. +- Axolotl YAML compiler; alt backends (Unsloth, torchtune, Primus) wired as + dispatch stubs (`mindxtrain.train`). +- Provenance manifest with BLAKE3 content addressing, ROCm/git/gfx capture, + and on-chain pointers (ERC-7857 INFT, Algorand ASA, ERC-8004 attestation) + (`mindxtrain.provenance`). +- Operator FastAPI app exposing `/v1/chat/completions`, `/v1/agentic`, and + the interactive `/coach/` UI (`mindxtrain.operator`). +- Pluggable inference backends: vLLM, OpenAI-compatible + (`mindxtrain.operator.backends`). +- Pluggable storage providers: local fs, HF Hub, Lighthouse, IPFS + (`mindxtrain.storage`). +- Foundry contracts for ERC-8004 attestation registry (`contracts/`). +- Containerfiles + compose + k8s manifests for MI300X (`ops/`). + +### Notes + +- This release replaces the earlier 3-package layout + (`mindXtrain/`, `automindXtrain/`, `custmodel/`) with a single ordered + package `mindxtrain/`. Old import paths (`xtrain.*`, `automindx.*`, + `custmodel.*`) are not preserved — adopters must rewrite to `mindxtrain.*`. +- Most operator and trainer surfaces ship as honest minimal stubs that raise + `NotImplementedError` for paths not yet implemented in the hackathon scope. + The autotune probe, config schema, manifest, recipes, and Axolotl compiler + are the production-ready paths. + +[0.1.0]: https://example.invalid/mindxtrain/releases/tag/v0.1.0 diff --git a/docs/HANDOFF.md b/docs/HANDOFF.md new file mode 100644 index 0000000000000000000000000000000000000000..b943320a3b1ff9229806214fc85ceab623152f56 --- /dev/null +++ b/docs/HANDOFF.md @@ -0,0 +1,309 @@ +# HANDOFF — what you need to do next + +This is the ordered checklist for taking the mindxtrain repo from "code is +done" to "demo is live." Each step is concrete; check it off when finished. + +The repo state at handoff: + +- Single canonical package at `mindxtrain/` (12 subpackages, ~100 modules). +- All stub `NotImplementedError` paths replaced with real Python (lazy imports + for heavyweight deps). +- 112/112 tests pass on a CPU-only laptop (`uv sync` + `uv run pytest -q`). +- Optional dep groups in `pyproject.toml`: `ml`, `eval`, `data`, `serve`, + `chain`, `obs`. Install only what you need. +- 12 YAML training recipes wired through the CLI. +- Coach UI (`/coach/`) serves all 12 recipes without GPU. + +--- + +## 1. Local setup (no GPU; 10 minutes) + +```bash +cd /home/hacker/Desktop/mindXtrain +cp .env.example .env # then edit .env to fill in HF_TOKEN, etc. +uv sync # base install +uv run pytest -q # → 112 passed +uv run mindxtrain --help # all 9 verbs listed +``` + +**What goes in `.env`** (rest of the file is sane defaults): + +| Var | Where to get it | +|---|---| +| `HF_TOKEN` | https://huggingface.co/settings/tokens (write scope) | +| `HF_HUB_USERNAME` | your HF handle | +| `LIGHTHOUSE_API_KEY` | https://files.lighthouse.storage/dashboard/apikey | +| `MINDXTRAIN_OPENAI_API_KEY` | optional; only if you want to use openai_compat backend | + +> **Optional (on-chain anchors):** `MINDXTRAIN_REGISTRY_ADDR` (ERC-8004 contract), +> `MINDXTRAIN_FACILITATOR_URL` (x402 facilitator). The publish path skips +> these gracefully if unset. + +## 2. Provision the MI300X droplet (sign-up + 30 min) + +> **Fast path (Coach UI):** if you've populated `GITHUB_TOKEN`, +> `AMD_DEV_CLOUD_TOKEN`, and `AMD_DEV_CLOUD_SSH_KEY_ID` in `.env`, you can skip +> the manual SSH dance entirely: +> +> 1. `uv run uvicorn mindxtrain.operator.app:app --port 8080` +> 2. Open , scroll to step 6 ("Deploy"). +> 3. Click ① **Push to GitHub** → ② **Provision MI300X droplet**. The droplet +> boots, cloud-init clones the repo from the SHA you just pushed, pulls the +> container, and runs `mindxtrain bench` automatically. All output streams +> live in the browser via SSE. +> +> Equivalent CLI: `mindxtrain github push && mindxtrain droplet provision`. +> +> The manual sequence below is preserved for scripted / CI use and as a +> fallback when the Coach UI isn't available. + +```bash +# Sign up at https://devcloud.amd.com — request a single MI300X. +# Wait for the droplet (typically same-day). +# SSH in: +ssh ubuntu@ + +# Install podman if missing: +sudo apt-get update && sudo apt-get install -y podman podman-compose + +# Pull the canonical training container: +podman pull docker.io/rocm/primus:v26.2 + +# Snapshot the digest into the repo so others can reproduce: +podman inspect --format '{{index .RepoDigests 0}}' rocm/primus:v26.2 \ + | tee -a ops/containerfiles/digest.lock + +# Verify the GPU is visible: +podman run --rm --device=/dev/kfd --device=/dev/dri rocm/primus:v26.2 \ + rocminfo | head -50 +# → should show gfx942, 192 GB HBM3 +``` + +> **Cost watch:** $1.99/hr × planned hours. Budget ~$30 for the full demo +> pipeline (~15 GPU-hours). Leave the droplet **stopped** when not actively +> training. + +## 3. Install heavyweight deps inside the container + +```bash +# On the MI300X: +git clone /workspace/mindxtrain +cd /workspace/mindxtrain +podman run -it --rm \ + --device=/dev/kfd --device=/dev/dri \ + -v /workspace/mindxtrain:/workspace/mindxtrain \ + -w /workspace/mindxtrain \ + rocm/primus:v26.2 bash + +# Inside the container: +pip install -e ".[ml,eval,data,obs]" +# (skip `serve` and `chain` until you need them — they pull large wheels) +``` + +## 4. Run the autotune probe (real, ~60 s) + +```bash +mindxtrain bench --gpu 0 --out plan.json +cat plan.json | jq '.attention_backend, .gemm_heuristic, .rccl_config' +# → "ck", "hipblaslt_default", "1gpu_noop" +``` + +Snapshot `plan.json` into the repo so the run is reproducible: + +```bash +cp plan.json ops/k8s/plan-mi300x.json +git add ops/k8s/plan-mi300x.json +git commit -m "snapshot autotune plan from mi300x" +``` + +## 5. Train + eval + quantize (~ 2 hours total for the demo recipe) + +```bash +# Pick a recipe: instella_3b_lora is the AMD-on-AMD demo path (~30 min). +# Or qwen3_8b_sft_lora for the Qwen side prize (~75 min). +mindxtrain init --template instella_3b_lora --out run.yaml + +# Optional: edit run.yaml for your project name, dataset, output path. +$EDITOR run.yaml + +# Dataset prep (pulls + dedupes + tokenizes + packs): +mindxtrain dataset prep run.yaml --out ./out/dataset + +# Training: +mindxtrain train run.yaml --plan plan.json +# → ./out/runs//checkpoint/ + +# Evaluation (MMLU subset): +mindxtrain eval run.yaml +# → ./out/runs//eval/lm_eval.json + +# Quantize to FP8: +mindxtrain quantize run.yaml +# → ./out/runs//quantized/ +``` + +If `mindxtrain train` fails with `accelerate not found`: you forgot +`pip install -e ".[ml]"` inside the container (step 3). + +## 6. Build the manifest + verify + +```bash +# Generate the provenance manifest by hashing every artifact: +uv run python -c " +from pathlib import Path +from mindxtrain.config.loader import load_config +from mindxtrain.provenance.manifest import emit_receipt, ProvenanceHashes +cfg = load_config('run.yaml') +run = Path('./out/runs') / cfg.meta.run_name +m = emit_receipt( + cfg, + cfg.meta.run_name, + config_yaml_path=Path('run.yaml'), + dataset_manifest_path=run / 'dataset_manifest.json', + checkpoint_dir=run / 'checkpoint', + eval_json_path=run / 'eval/lm_eval.json', +) +out = run / 'manifest.json' +out.write_text(m.model_dump_json(indent=2)) +print(out) +" + +# Verify it round-trips: +mindxtrain receipt ./out/runs//manifest.json --config run.yaml +# → all BLAKE3 fields = true (config, checkpoint, autotune_plan; dataset/eval if present) +``` + +> **Auto-emitted receipts (operator + CPU lane).** Runs launched through the +> operator — Coach UI or `POST /v1/training/jobs` — now write `manifest.json` +> automatically at completion via `provenance.manifest.emit_receipt_for_run`, +> alongside `config.snapshot.yaml` and `autotune_plan.json` in the run dir. The +> receipt **binds the frozen AutotunePlan hash to the checkpoint hash** — this is +> the AOT artifact that makes a run bitwise-verifiable (cf. Verde/RepOps). The +> Coach "Verifiable receipt" card re-checks it live; `mindxtrain receipt` does the +> same from a shell. The manual `emit_receipt` above remains the full GPU path +> (dataset + eval JSON included). On MI300X, also snapshot the AOTriton / +> hipBLASLt tuning caches next to `autotune_plan.json` so the compiled artifact — +> not just the plan — is reproducible across machines. + +## 7. Publish (HF Hub + Lighthouse + mindX register) + +```bash +# Push to HF (uses HF_TOKEN; private=False for the demo): +mindxtrain publish run.yaml --manifest ./out/runs//manifest.json +# → updates manifest.json in-place with hf_repo_id + lighthouse_cid +``` + +If `LIGHTHOUSE_API_KEY` is unset, the pin step skips gracefully and the +manifest gets a `cid://stub-…` placeholder. + +## 8. Deploy contracts (optional) + +The demo can ship without on-chain anchors. Do these once, when ready: + +```bash +cd contracts +forge install +forge test # local Foundry tests pass +forge script script/Deploy.s.sol \ + --rpc-url $MINDXTRAIN_BASE_RPC_URL \ + --private-key $DEPLOYER_KEY \ + --broadcast +# → records contract address; paste into .env as MINDXTRAIN_REGISTRY_ADDR +``` + +Once `MINDXTRAIN_REGISTRY_ADDR` is set, `mindxtrain.provenance.erc8004.broadcast_attestation` +can anchor the manifest BLAKE3 on-chain. + +## 9. Serve the model + wire the production URL + +The production URL is `https://mindx.pythai.net` — the Coach UI is at `/coach/` +and the public training-jobs API is at `/v1/training/jobs`. + +```bash +# Inside the rocm/vllm-dev container: +podman-compose -f ops/compose/compose_dev.yaml up -d +# → vLLM-ROCm at :8000, mindxtrain operator FastAPI at :8080 + +# Verify locally: +curl http://localhost:8080/coach/api/health +# → {"coach_version":"0.1.0", "recipes_available":>=14, ...} + +# Public training-jobs API smoke (bearer auth via MINDXTRAIN_API_KEY): +curl -X POST http://localhost:8080/v1/training/jobs \ + -H "Authorization: Bearer $MINDXTRAIN_API_KEY" \ + -H "Content-Type: application/json" \ + -d '{"recipe":"mindx_fallback_qwen3_1_5b_cpu_smoke"}' +# → {"job_id":"...", "status":"running", "backend":"trl_cpu", ...} + +# Reverse-proxy mindx.pythai.net → MI300X:8080 (Caddy/Cloudflare). +``` + +Once the proxy is live, `curl https://mindx.pythai.net/coach/api/health` +returns 200 from the public internet. + +## 10. Publish & demo + +```bash +# Push code: +git push origin main + +# End-to-end demo walk-through: +# 1. mindxtrain init → show CLI verbs +# 2. mindxtrain bench → 60-second autotune (the differentiator) +# 3. mindxtrain train → timelapse of training +# 4. mindxtrain quantize → FP8 weights +# 5. curl /v1/chat/completions → live inference +# 6. mindxtrain receipt → BLAKE3 reverify +# 7. Open /coach/ → click through the UI +# 8. Open /coach/dcoach → Imprint & Prove (CPU recall proof) +``` + +## 11. Quality gates (run before every push) + +```bash +uv run ruff check . +uv run mypy mindxtrain/config mindxtrain/provenance +uv run pytest -q # → 112 passed +``` + +All three must pass before pushing to `main`. CI runs the same gates on the +`main` branch. + +--- + +## What's still TODO + +These paths are wired but require runtime/contracts/services to actually +flow end-to-end: + +- **x402 metering** (`mindxtrain.provenance.x402`) — wired to httpx, needs + a deployed facilitator URL. +- **ERC-8004 broadcast** (`mindxtrain.provenance.erc8004.broadcast_attestation`) + — needs deployed attestation registry + signer key. +- **BANKON ENS** allocation (`mindxtrain.provenance.algorand.allocate_ens_subname`) + — needs the BANKON allocation service deployed. +- **AgenticPlace listing** (`mindxtrain.deploy.api_client.list_on_agenticplace`) + — needs `agenticplace.pythai.net` live. +- **mindX agent register** (`mindxtrain.deploy.api_client.register_with_mindx`) + — needs `mindx.pythai.net/v1/agents` live. + +The framework itself ships as production-ready Apache-2.0; the integrations +above are paid/external services you stand up at your own pace. + +--- + +## Quick reference + +| What | Where | +|---|---| +| All CLI verbs | `mindxtrain --help` | +| All recipes | `mindxtrain init --list` | +| Coach UI | http://localhost:8080/coach/ | +| Per-module status | `docs/actualization_status.md` | +| Architecture | `docs/architecture.md` | +| Autotune detail | `docs/autotune.md` | +| Coach detail | `docs/coach.md` | +| CLI reference | `docs/cli.md` | +| YAML schema | `docs/yaml_schema.md` | +| dcoach proof loop | `docs/dcoach.md` | +| Frozen blueprints | `docs/blueprints/{mindXtrain,mindXtrain2}.md` | diff --git a/docs/LICENSE-NOTICE.md b/docs/LICENSE-NOTICE.md new file mode 100644 index 0000000000000000000000000000000000000000..70a7233222c8be260e1791385d8f2cfebf15a1c6 --- /dev/null +++ b/docs/LICENSE-NOTICE.md @@ -0,0 +1,7 @@ +# Licensing notice for the lablab.ai / AMD Developer Hackathon + +This project is licensed under the Apache License 2.0 (see [LICENSE](../LICENSE)). + +The Apache 2.0 License is fully MIT-compatible: any code in this repository may be relicensed under MIT terms by a downstream consumer who keeps the original Apache 2.0 NOTICE and copyright attribution intact, in accordance with §4(d) of the Apache License. + +This statement satisfies the lablab.ai hackathon submission requirement that entries be released under an open-source license that is MIT-compatible. diff --git a/docs/NAV.md b/docs/NAV.md new file mode 100644 index 0000000000000000000000000000000000000000..8421e04e3517e4b5ae2012af7e672d9e68592d95 --- /dev/null +++ b/docs/NAV.md @@ -0,0 +1,50 @@ +# mindXtrain Documentation Index + +Every doc lives in `docs/`. The only Markdown at the repo root is `README.md` (entry +point) plus `CLAUDE.md` / `AGENTS.md` (agent-tooling entrypoints, required at root). +Start at [Quickstart](quickstart.md); operators running the demo read [HANDOFF.md](HANDOFF.md). + +## Getting started + +- [Quickstart](quickstart.md) — install (with optional-dep groups), init, bench, train. +- [HANDOFF.md](HANDOFF.md) — **operator checklist**: local setup → MI300X provision → train/eval/quantize → publish → contracts → deploy. + +## Architecture & invariants + +- [Architecture](architecture.md) — the 5-layer single-package layout + MI300X invariants + data flow. +- [Development workflow](development.md) — toolchain, optional-deps, lazy-import pattern, invariants, **training lanes** (CPU / local-GPU / MI300X), how to add recipes/backends/methods. +- [Actualization status](actualization_status.md) — per-module map of what's real vs. needs `--extra` vs. v1.0.0 CPU-active / GPU-pending / stub. +- [Autotune deep-dive](autotune.md) — the 60-second AOT probe (the differentiator). + +## Coach UI & training workflow + +- [Coach UI](coach.md) — the interactive `/coach/` operator UI: create-script (personas + skills), live-training diagnostics, verifiable receipt, streaming chat + ollama controls, Modelfile builder. +- [dcoach](dcoach.md) — `/coach/dcoach`: prove a CPU-trained model recalls its training (imprint → classroom → boardroom → autotune feedback), clean-room llama-style eval tools, prompt-tools page, and how mindXtrain fits decentralized training. +- [Governance](governance.md) — classroom (graduation) / boardroom (any-N consensus) / dojo (prime-N dispute settlement), model-backed deliberation. + +## Decentralized training landscape (2026) + +- [Decentralized training deep-dive](decentralized-training-deep-dive-2026.md) — Prime Intellect / Nous Psyche / Gensyn / Templar / Pluralis, the DiLoCo/SparseLoCo algorithms, verification (TOPLOC / Verde / Gauntlet), and where mindXtrain fits (AOT-only = verifiable). +- [LLM training-stack landscape](mindxtrain-llm-training-landscape-2026.md) — the open-source training/eval/quantize stack survey anchored on mindXtrain. +- [Vercel AI SDK 6 deep-dive]() — the streaming/agent toolkit the Coach chat patterns after (clean-room, vanilla JS). + +## Reference + +- [CLI reference](cli.md) — every `mindxtrain` verb with synopsis, options, exit codes. +- [YAML schema](yaml_schema.md) — every field of the 10-section `XTrainConfig`. +- [Benchmarks](benchmarks.md) — target metrics + framework comparison. +- [CHANGELOG](CHANGELOG.md) — version history (current: v1.0.0). +- [LICENSE-NOTICE](LICENSE-NOTICE.md) — Apache-2.0 + MIT-compatibility statement. + +## Source briefs (`blueprints/`) + +The frozen design briefs the project was built against — historical specification; for +current state read the docs above. Do not edit. + +- [`blueprints/mindXtrain.md`](blueprints/mindXtrain.md) — operating brief; three-track pitch, day-by-day execution. +- [`blueprints/mindXtrain2.md`](blueprints/mindXtrain2.md) — technical reference; canonical Part 4 layout. +- [`blueprints/mindXtrain_ Production Blueprint for the AMD and lablab.ai Hackathon.md`]() — repo skeleton, hero recipe, immutable registry stub. + +## On-chain + +- [`contracts/README.md`](../contracts/README.md) — Foundry workspace for the immutable run-receipt registry + x402 receiver. diff --git a/docs/Vercel AI SDK 6_ A Framework-Agnostic Deep Dive (June 2026).md b/docs/Vercel AI SDK 6_ A Framework-Agnostic Deep Dive (June 2026).md new file mode 100644 index 0000000000000000000000000000000000000000..04f3d5a73ab3af66754798de8a56d4ccda85a72d --- /dev/null +++ b/docs/Vercel AI SDK 6_ A Framework-Agnostic Deep Dive (June 2026).md @@ -0,0 +1,340 @@ +# The Vercel AI SDK: A Framework-Agnostic Deep Dive (AI SDK 6, June 2026) + +## TL;DR +- The Vercel AI SDK is an Apache-2.0-licensed TypeScript toolkit (npm package `ai`); the current major is **AI SDK 6** (`ai@6.0.x` — 6.0.199 per the vercel/ai GitHub releases page dated 9 Jun, 6.0.202 listed on npm). Its core layer (AI SDK Core) is fully framework-agnostic and runs in any JS runtime — Node.js 18+, Deno, Bun, edge — with zero Vercel hosting lock-in. +- It gives you one unified API (`generateText`, `streamText`, `generateObject`/`streamObject`, `embed`/`embedMany`, tools, the `ToolLoopAgent` agent loop, MCP client, middleware, image/speech/transcription) across providers; you can point it at sovereign self-hosted inference (Ollama, vLLM, llama.cpp, LM Studio) via `@ai-sdk/openai-compatible` using direct provider keys, no gateway required. +- For a clean-room backend: `npm i ai @ai-sdk/anthropic @ai-sdk/openai @ai-sdk/openai-compatible zod`, set `ANTHROPIC_API_KEY`/`OPENAI_API_KEY`, and you have working `generateText`/streaming in a plain Node script or any Hono/Express/Fastify server in minutes. + +## Key Findings +- **Version reality check:** Despite stale knowledge suggesting v4/v5, Vercel's official blog announced **AI SDK 5 on July 31 2025** ("v5 is a stable, production release"), and **v6 followed in late 2025** with further additions; as of June 2026 both `ai@6.0.x` and a maintained `ai@5.0.x` line are published in parallel (GitHub releases shows `ai@6.0.199` and `ai@5.0.197` published the same time). Use v6 for new projects. +- **The Agent class was renamed.** v5's `Experimental_Agent` is now the stable **`ToolLoopAgent`** in v6, and `system` → `instructions`. `Agent` is now an *interface* you can implement (e.g. Workflow DevKit's `DurableAgent`). +- **Tool API changed across v5/v6:** use `inputSchema` (not `parameters`), and control multi-step loops with `stopWhen: stepCountIs(n)` (not `maxSteps`). +- **Sovereignty is well-supported:** direct provider packages auto-read env keys, the AI Gateway is entirely optional, and `@ai-sdk/openai-compatible` cleanly targets local inference servers. License is Apache-2.0. +- **v6 deprecations to note:** `generateObject`/`streamObject` are now *deprecated* in favor of `generateText`/`streamText` with an `output` setting (though still fully functional); `CoreMessage` removed in favor of `ModelMessage`; `convertToCoreMessages` → `convertToModelMessages` (now async). + +## Details + +### 1. Architecture & Philosophy + +The AI SDK is "the AI Toolkit for TypeScript… a free open-source library for building AI-powered applications and agents" (vercel/ai). It is organized in layers: + +- **AI SDK Core** (`ai` package): the unified, framework-agnostic API for text/object generation, embeddings, tools, agents, image/speech. This is what a backend/agent engineer uses. It runs in *any* JavaScript runtime — Node.js, Deno, Bun, edge runtimes, plain backend services — because it depends only on standard web primitives (`fetch`, `ReadableStream`, SSE). +- **AI SDK UI** (`@ai-sdk/react`, `@ai-sdk/vue`, `@ai-sdk/svelte`, `@ai-sdk/angular`): framework hooks (`useChat`, `useCompletion`, `useObject`). *Optional* — irrelevant for headless backends. +- **Provider packages** (`@ai-sdk/*`): each adapts a vendor API to the SDK's Language Model Specification (currently the v3 spec in SDK 6; the underlying model interface evolved to LanguageModelV2 in v5). + +**The unified provider abstraction model.** Every provider package exposes a factory (`openai('gpt-5.4')`, `anthropic('claude-opus-4-6')`) returning a `LanguageModel` that conforms to a single spec. Switching providers is a one-line change. The v5 LanguageModelV2 redesign made all model outputs "content parts" (text, reasoning, tool calls, sources, files) in one ordered array, which is why reasoning models, multimodal, and computer-use agents all work through one interface. + +**Package structure / first-party providers** (all under `@ai-sdk/`): `openai`, `anthropic`, `google` (Generative AI), `google-vertex`, `mistral`, `groq`, `amazon-bedrock`, `azure`, `xai` (Grok), `deepseek`, `togetherai`, `cohere`, `fireworks`, `cerebras`, `deepinfra`, `perplexity`, `replicate`, `fal`, `luma`, `elevenlabs`, `assemblyai`, `deepgram`, `gladia`, `lmnt`, `hume`, `revai`, `baseten`, `huggingface`, `vercel` (v0), plus the crucial **`@ai-sdk/openai-compatible`** generic adapter and **`@ai-sdk/gateway`**. + +**Community / self-hostable providers** (implement the Language Model Specification): **Ollama** (`ollama-ai-provider-v2` and `ai-sdk-ollama` — the latter is a v6 provider built on the official `ollama` package, with `ai-sdk-ollama@^2` for v5), **llama.cpp**, **LM Studio** and **NVIDIA NIM** (documented under the openai-compatible umbrella), Cloudflare Workers AI, OpenRouter, Portkey, FriendliAI, LangDB, Browser AI (WebLLM/Transformers.js for in-browser models), and many more. For the sovereignty-minded: *any* server implementing the OpenAI API spec works through `@ai-sdk/openai-compatible` with no dedicated package. + +**v4 → v5 → v6 breaking changes (the ones that matter for backend code):** +- **v4 → v5** (July 31 2025, major architectural overhaul): `UIMessage` and `ModelMessage` became separate types (conversion is now explicit via `convertToModelMessages`); streaming switched from a custom protocol to native **Server-Sent Events**; tools use `inputSchema`/`outputSchema` instead of `parameters`/`result`; `maxTokens` → `maxOutputTokens`; multi-step uses `stopWhen` (the `maxSteps` parameter was removed from `useChat`); `.reasoning` → `.reasoningText`; `mimeType` → `mediaType`; new `Experimental_Agent` class; speech/transcription added; Zod 4 supported. Codemods: `npx @ai-sdk/codemod@latest migrate`. +- **v5 → v6** (late 2025): `Experimental_Agent` → **`ToolLoopAgent`** (and `system` → `instructions`); **`generateObject`/`streamObject` deprecated** in favor of `generateText`/`streamText` + `output`; **`CoreMessage` removed** (use `ModelMessage`); `convertToCoreMessages` → `convertToModelMessages` (now **async**); embedding methods `textEmbeddingModel`/`textEmbedding` → `embeddingModel`/`embedding`; `strictJsonSchema` on by default; `structuredOutputs` provider option removed (use `strictJsonSchema`); Azure provider defaults to the Responses API; tool UI helper renames (`isToolUIPart` → `isStaticToolUIPart`, etc.); a deprecation-warning logger (disable with `AI_SDK_LOG_WARNINGS=false`). Vercel describes v6 migration as "intentionally simple" with codemods (`npx @ai-sdk/codemod upgrade`). + +**Node requirement:** AI SDK 6 requires **Node.js 18+**. The official Getting Started: Node.js docs state "Node.js 18+ and pnpm installed on your local development machine," the npm README states "You will need Node.js 18+," and the repo `package.json` engines field is `"node": "^18.0.0 || ^20.0.0 || ^22.0.0"` — so Node 18/20/22 are the supported majors with 18 as the floor. + +### 2. Detailed Capability List + +**Text generation & streaming.** `generateText({ model, prompt | messages, system, tools, stopWhen, ... })` returns `{ text, reasoning, reasoningText, steps, toolCalls, toolResults, usage, finishReason, ... }`. `streamText(...)` returns a result exposing `textStream` (async iterable of text chunks), `fullStream` (typed parts: text-delta, reasoning, tool-input-start/delta, tool-call, tool-result, source, finish), plus `onChunk`, `onFinish`, `onError`, `onStepFinish`, and experimental lifecycle callbacks (`experimental_onStart`, `experimental_onStepStart`, `experimental_onToolCallStart`/`Finish`). Streaming uses backpressure — you must consume the stream for it to finish. Response helpers: `toUIMessageStreamResponse()`, `pipeUIMessageStreamToResponse(res)`, `toTextStreamResponse()`, `pipeTextStreamToResponse(res)`. + +**Structured output.** Two routes: (a) the still-functional but v6-deprecated `generateObject`/`streamObject({ model, schema, output })` with `output: 'object' | 'array' | 'enum' | 'no-schema'`; (b) the v6-preferred `generateText`/`streamText` with the `output` setting and the `Output` helper: `Output.object({ schema })`, `Output.array({ element })`, `Output.choice({ options })` (enum/classification), `Output.json()` (unstructured). Schemas may be Zod, Valibot, or JSON Schema (`jsonSchema()`). `streamObject`/array exposes `partialObjectStream` and `elementStream` (each element validated as it completes). Crucial caveat: structured output **counts as a step**, so when combining with tools, increase `stopWhen` accordingly. + +**Tool calling / function calling.** Define tools with the `tool()` helper (needed for TypeScript to infer `execute` arg types from `inputSchema`): +```ts +weather: tool({ + description: 'Get the weather in a location', + inputSchema: z.object({ location: z.string() }), + execute: async ({ location }, { abortSignal }) => ({ location, temperature: 72 }), +}) +``` +`execute` is optional (omit to forward calls to a client/queue). v6 adds: `needsApproval` (human-in-the-loop, boolean or function of input), per-tool `strict` mode, `toModelOutput` for flexible tool outputs, input examples, and `dynamicTool()` for runtime-defined tools. Multi-step loops: `stopWhen` accepts `stepCountIs(n)` (default `stepCountIs(20)`), `hasToolCall(name)`, `isLoopFinished()` (no limit), custom `StopCondition` functions, or an array (stops on any). `toolChoice: 'auto' | 'required' | 'none' | { type:'tool', toolName }`. Provider-executed tools exist (web search, code execution, memory, computer use). The abort signal is forwarded into `execute`. + +**Agents.** `ToolLoopAgent` (v6) encapsulates model + instructions + tools + loop control into a reusable object usable across chat UIs, background jobs, API endpoints, and CLI daemons: +```ts +const agent = new ToolLoopAgent({ model, instructions, tools, stopWhen: stepCountIs(10), prepareStep }); +const result = await agent.generate({ prompt }); // GenerateTextResult +const stream = agent.stream({ prompt }); // StreamTextResult +``` +Loop control: `stopWhen` (when to stop) and **`prepareStep`** (called before each step; can change model, tools, toolChoice, messages — used for context compression, model-switching by complexity, dynamic tool gating). v6 also adds `callOptionsSchema` + `prepareCall` for type-safe per-call options (e.g. inject RAG context once per call, select model by tier). Subagents are just a `ToolLoopAgent` invoked inside another agent's tool `execute`. For full control, hand-roll the loop with `generateText` + your own while-loop. + +**Embeddings & RAG.** `embed({ model, value })` → `{ embedding, usage }`; `embedMany({ model, values, maxParallelCalls })` → `{ embeddings, usage }` (auto-chunks large batches). `cosineSimilarity(a, b)` for ranking. v6 also adds a `rerank()` function. The canonical mini-RAG pattern: chunk → `embedMany` → store `{embedding, value}` → at query time `embed` the query, sort chunks by `cosineSimilarity`, inject top-k into the prompt. Production guides use pgvector or Upstash Vector as the store. + +**Image generation.** `generateImage({ model, prompt, size })` → `{ images }` (experimental, also exported as `experimental_generateImage`). v6 adds image editing/inpainting (`prompt: { text, images, mask }`) via the openai-compatible provider's `/images/edits`. + +**Speech & transcription (experimental).** `generateSpeech({ model: openai.speech('tts-1'), text, voice })` → `{ audio }`; `transcribe({ model: openai.transcription('whisper-1'), audio })` → `{ text, segments, language, durationInSeconds }`. Imported as `experimental_generateSpeech`/`experimental_transcribe`. Providers include OpenAI, ElevenLabs, Deepgram, AssemblyAI, Gladia, LMNT, Hume, Rev.ai. `audio` accepts Uint8Array/ArrayBuffer/Buffer/base64/URL. + +**Multimodal inputs.** Messages support `ImagePart`, `FilePart` (PDFs, files) alongside `TextPart`; images/files accept `string | Uint8Array | Buffer | ArrayBuffer | URL` with a `mediaType`. + +**Reasoning models.** Configure via `providerOptions`. For Anthropic extended thinking: `providerOptions: { anthropic: { thinking: { type: 'enabled', budgetTokens: 12000 } } }` (an `effort: 'low'|'medium'|'high'` option also exists; both can be combined). Access via destructured `reasoning`/`reasoningText`, or in streaming via `fullStream` parts. For OpenAI o-series/GPT-5: `providerOptions: { openai: { reasoningEffort: 'low', reasoningSummary: 'auto' } }`, with reasoning token counts at `providerMetadata.openai.reasoningTokens`. For models that wrap reasoning in `` tags (DeepSeek R1, Magistral), use `extractReasoningMiddleware({ tagName: 'think' })`: +```ts +const model = wrapLanguageModel({ model: yourModel, middleware: extractReasoningMiddleware({ tagName: 'think' }) }); +const { text, reasoningText } = await generateText({ model, prompt: 'What is 15 * 24?' }); +``` + +**Middleware.** `wrapLanguageModel({ model, middleware })` returns an enhanced model. A middleware (type `LanguageModelV3Middleware` in v6) implements any of `transformParams`, `wrapGenerate`, `wrapStream` — model-agnostic logging, caching, guardrails, RAG injection, rate-limiting. Multiple middlewares compose in order (applied innermost-last). Built-ins: `extractReasoningMiddleware`, `simulateStreamingMiddleware`, `defaultSettingsMiddleware`, `addToolInputExamplesMiddleware`, `extractJsonMiddleware`. Community: `@ai-sdk-tool/parser` (`hermesToolMiddleware`, `gemmaToolMiddleware`) adds tool-calling to local models lacking native function calling — directly relevant to self-hosted deployments. + +**Provider registry & custom providers.** `createProviderRegistry({ anthropic, openai, … })` lets you reference models by `providerId:modelId` string at runtime (custom separator supported) — useful for runtime model selection, A/B testing, and fallback routing. `customProvider({ languageModels, fallbackProvider })` creates aliases/preconfigured settings and can restrict the model set. + +**Telemetry.** OpenTelemetry-based via `experimental_telemetry: { isEnabled: true, functionId, recordInputs, recordOutputs }` on any generate/stream call. Emits standard `gen_ai.*` and AI-SDK-specific `ai.*` spans (model calls, `ai.toolCall`, etc.). Works with any OTel backend; for non-Next.js (Express/Fastify/Hono/plain Node) initialize the OTel Node SDK directly (e.g. `@opentelemetry/sdk-node` + an OTLP exporter, or `@pydantic/logfire-node`, or `@langfuse/otel`'s `LangfuseSpanProcessor`). + +**Error handling, retries, abort, timeouts.** `maxRetries` on all functions; `abortSignal` accepted by generate/stream/embed/transcribe and forwarded into tools; `streamText` puts errors into the stream (use `onError`) rather than throwing, to avoid crashing servers; typed errors (`AI_NoSpeechGeneratedError`, `MCPClientError`, etc.). + +**Streaming protocols & frameless consumption.** Native SSE. The **UI Message Stream** protocol (set header `x-vercel-ai-ui-message-stream: v1` for custom backends) carries typed parts; the **text stream** is plain text. To consume *without any frontend framework*: iterate `result.textStream`/`result.fullStream` in a CLI/daemon; or serve over HTTP with `pipeUIMessageStreamToResponse(res)` (Node `http`), `result.toUIMessageStreamResponse()`/`toTextStreamResponse()` (Hono/edge/Web `Response`), or Hono's `stream`/`streamSSE` helpers. `createUIMessageStream({ execute })` + `writer.write`/`writer.merge` lets you emit custom data parts. `readUIMessageStream` converts a chunk stream to an async-iterable of `UIMessage`s on the client side. + +**Prompt management.** `system` prompt, `prompt` (string), or `messages` array of `ModelMessage` (`SystemModelMessage`/`UserModelMessage`/`AssistantModelMessage`/`ToolModelMessage`, each with typed content parts). `UIMessage` (client-facing, has a `parts` array) is distinct from `ModelMessage` (sent to the LLM); convert with the async `convertToModelMessages(uiMessages)`. + +**Caching / rate limiting.** Implemented as middleware (cache by hashed params, short-circuit identical calls) or via provider-native prompt caching (Anthropic, Bedrock). The cookbook ships local-caching and dynamic-prompt-caching middleware examples. + +### 3. Clean-Room Setup (framework-agnostic) + +```bash +mkdir my-agent && cd my-agent +npm init -y +npm pkg set type=module +npm i ai @ai-sdk/anthropic @ai-sdk/openai @ai-sdk/openai-compatible zod +npm i -D typescript tsx @types/node +npx tsc --init +``` +`tsconfig.json`: use ESM-friendly settings — `"module": "ES2022"`, `"moduleResolution": "Bundler"` (or `"NodeNext"`), `"target": "ES2022"`, `"strict": true`. Node 18+ (20 or 22 recommended). Set env vars — provider packages auto-detect them: `ANTHROPIC_API_KEY`, `OPENAI_API_KEY`, etc. (Gateway string-model usage instead reads `AI_GATEWAY_API_KEY`.) + +Run scripts with `npx tsx index.ts`. **Deno**: `deno run --allow-net --allow-env npm:tsx index.ts` or import via `npm:ai`. **Bun**: `bun add ai @ai-sdk/anthropic zod` then `bun index.ts`. + +### 4. Code Examples (current v6 API) + +**Hello world — generateText (plain Node script):** +```ts +import { generateText } from 'ai'; +import { anthropic } from '@ai-sdk/anthropic'; + +const { text } = await generateText({ + model: anthropic('claude-opus-4-6'), + prompt: 'Explain quantum entanglement in two sentences.', +}); +console.log(text); +``` + +**streamText — consume in a CLI/daemon:** +```ts +import { streamText } from 'ai'; +import { openai } from '@ai-sdk/openai'; + +const result = streamText({ + model: openai('gpt-5.4'), + prompt: 'Write a haiku about distributed systems.', +}); +for await (const chunk of result.textStream) process.stdout.write(chunk); +console.log('\nusage:', await result.usage); +``` + +**Structured output (v6-preferred output setting):** +```ts +import { generateText, Output } from 'ai'; +import { anthropic } from '@ai-sdk/anthropic'; +import { z } from 'zod'; + +const { output } = await generateText({ + model: anthropic('claude-sonnet-4-5'), + output: Output.object({ + schema: z.object({ + title: z.string(), + severity: z.enum(['low', 'medium', 'high']), + tags: z.array(z.string()), + }), + }), + prompt: 'Classify this incident: database connection pool exhausted under load.', +}); +console.log(output); +``` + +**Tool calling + multi-step loop:** +```ts +import { generateText, tool, stepCountIs } from 'ai'; +import { anthropic } from '@ai-sdk/anthropic'; +import { z } from 'zod'; + +const { text, steps } = await generateText({ + model: anthropic('claude-sonnet-4-5'), + stopWhen: stepCountIs(5), + tools: { + getBalance: tool({ + description: 'Get the on-chain token balance for an address', + inputSchema: z.object({ address: z.string(), token: z.string() }), + execute: async ({ address, token }) => ({ address, token, balance: '1234.56' }), + }), + }, + prompt: 'What is the USDC balance of 0xabc...?', +}); +console.log(text, steps.length); +``` + +**ToolLoopAgent (reusable agent):** +```ts +import { ToolLoopAgent, stepCountIs } from 'ai'; +import { anthropic } from '@ai-sdk/anthropic'; + +export const agent = new ToolLoopAgent({ + model: anthropic('claude-sonnet-4-6'), + instructions: 'You are an autonomous backend agent. Use tools, then answer.', + tools: { /* ...tools... */ }, + stopWhen: stepCountIs(20), + prepareStep: ({ stepNumber }) => (stepNumber === 0 ? { toolChoice: 'required' } : {}), +}); + +const result = await agent.generate({ prompt: 'Analyze the dataset and summarize.' }); +console.log(result.text); +``` + +**Embeddings + cosine similarity mini-RAG:** +```ts +import { embed, embedMany, cosineSimilarity, generateText } from 'ai'; +import { openai } from '@ai-sdk/openai'; + +const chunks = essay.split('.').map(s => s.trim()).filter(Boolean); +const { embeddings } = await embedMany({ + model: openai.embeddingModel('text-embedding-3-small'), + values: chunks, +}); +const db = embeddings.map((embedding, i) => ({ embedding, value: chunks[i] })); + +const { embedding: q } = await embed({ + model: openai.embeddingModel('text-embedding-3-small'), + value: 'What did the author say about sovereignty?', +}); +const top = db + .map(d => ({ ...d, score: cosineSimilarity(q, d.embedding) })) + .sort((a, b) => b.score - a.score) + .slice(0, 3) + .map(d => d.value) + .join('\n'); + +const { text } = await generateText({ + model: openai('gpt-5.4'), + system: `Answer using only this context:\n${top}`, + prompt: 'What did the author say about sovereignty?', +}); +console.log(text); +``` + +**Self-hosted inference via openai-compatible (Ollama / vLLM / llama.cpp):** +```ts +import { createOpenAICompatible } from '@ai-sdk/openai-compatible'; +import { generateText } from 'ai'; + +const local = createOpenAICompatible({ + name: 'local', + baseURL: 'http://localhost:11434/v1', // Ollama; vLLM/llama.cpp server: their /v1 URL + apiKey: 'ollama', // placeholder; many local servers ignore it +}); + +const { text } = await generateText({ + model: local('qwen2.5-coder:7b'), + prompt: 'Refactor this function for readability.', +}); +console.log(text); +``` +(Note the `/v1` suffix is required — local servers expose the OpenAI-compatible API there, not at their native `/api` path. The dedicated `ai-sdk-ollama` provider is an alternative that adds tool-call reliability and JSON repair on top of the official `ollama` client.) + +**Provider registry with Anthropic + a local endpoint:** +```ts +import { createProviderRegistry, generateText } from 'ai'; +import { anthropic } from '@ai-sdk/anthropic'; +import { createOpenAICompatible } from '@ai-sdk/openai-compatible'; + +const registry = createProviderRegistry({ + anthropic, + local: createOpenAICompatible({ name: 'local', baseURL: 'http://localhost:11434/v1', apiKey: 'ollama' }), +}); + +const { text } = await generateText({ + model: registry.languageModel('local:llama3.3'), // or 'anthropic:claude-sonnet-4-6' + prompt: 'Summarize the latest block.', +}); +``` + +**Middleware (reasoning extraction for a local DeepSeek-R1):** +```ts +import { wrapLanguageModel, extractReasoningMiddleware, generateText } from 'ai'; +import { createOpenAICompatible } from '@ai-sdk/openai-compatible'; + +const local = createOpenAICompatible({ name: 'local', baseURL: 'http://localhost:8000/v1', apiKey: 'x' }); +const model = wrapLanguageModel({ + model: local('deepseek-r1'), + middleware: extractReasoningMiddleware({ tagName: 'think' }), +}); +const { text, reasoningText } = await generateText({ model, prompt: 'What is 15 * 24?' }); +console.log({ reasoningText, text }); +``` + +**HTTP endpoint — Hono (Web Response):** +```ts +import { serve } from '@hono/node-server'; +import { streamText } from 'ai'; +import { Hono } from 'hono'; + +const app = new Hono(); +app.post('/', async c => { + const result = streamText({ model: 'openai/gpt-4o', prompt: 'Invent a holiday.' }); + return result.toUIMessageStreamResponse(); +}); +serve({ fetch: app.fetch, port: 8080 }); +``` + +**HTTP endpoint — plain Node `http` (no framework):** +```ts +import { streamText } from 'ai'; +import { createServer } from 'http'; + +createServer(async (req, res) => { + const result = streamText({ model: 'openai/gpt-4o', prompt: 'Invent a holiday.' }); + result.pipeUIMessageStreamToResponse(res); +}).listen(8080); +``` +(Express is identical, swapping in `pipeUIMessageStreamToResponse(res)` inside an `app.post` handler; Fastify and Nest.js have cookbook equivalents.) + +**MCP client tool usage (stable `@ai-sdk/mcp`):** +```ts +import { createMCPClient } from '@ai-sdk/mcp'; +import { Experimental_StdioMCPTransport } from '@ai-sdk/mcp/mcp-stdio'; +import { generateText, stepCountIs } from 'ai'; + +const client = await createMCPClient({ + transport: new Experimental_StdioMCPTransport({ command: 'node', args: ['server.js'] }), + // or HTTP: transport: { type: 'http', url: 'http://localhost:3000/mcp', headers: {...} } +}); +try { + const tools = await client.tools(); + const { text } = await generateText({ + model: 'anthropic/claude-sonnet-4.5', + tools, + stopWhen: stepCountIs(10), + prompt: 'Use the available tools to answer.', + }); + console.log(text); +} finally { + await client.close(); +} +``` +MCP transports: stdio (local), HTTP (Streamable HTTP), SSE; OAuth supported on HTTP/SSE via `authProvider`. The MCP client (`@ai-sdk/mcp`, ~v1.0.x) is lightweight (tool conversion, resources, prompts, elicitation) but does not yet do session management/resumable streams. + +### 5. Ecosystem & Context + +- **License:** Apache-2.0 (confirmed on the npm package) — aligns with the user's standards. Fully open source: the ai-sdk.dev homepage states **"12.5M Weekly downloads · 24.8K GitHub stars · 658+ Contributors"** (with ~4.6k forks per the GitHub releases page); ai-sdk.guide reports "over 30 million combined weekly npm installs across the ai core package and @ai-sdk/* providers." Vercel cites 20M+ monthly downloads. +- **No Vercel lock-in.** The SDK requires no Vercel hosting. Two model-addressing modes: pass a *string* like `'anthropic/claude-opus-4.6'` (routes through the Vercel AI Gateway, needing `AI_GATEWAY_API_KEY`), or pass a *provider instance* like `anthropic('claude-opus-4-6')` that talks **directly** to the vendor with your own key. The Gateway is purely optional convenience: per Vercel's own docs, "AI Gateway reflects provider pricing with no markup and does not charge a platform fee on inference, including on Bring Your Own Key (BYOK) requests." For sovereign deployments, use provider instances or `@ai-sdk/openai-compatible` against self-hosted servers and the Gateway never enters the picture. +- **vs LangChain.js:** the AI SDK is a focused, strongly-typed TypeScript abstraction over providers + streaming + tools + a lightweight agent loop; LangChain.js offers broader, heavier orchestration abstractions. Downstream agent frameworks (Mastra, Inngest agent kit, even LangChain's TS port) increasingly integrate *against* the AI SDK rather than competing. vs direct provider SDKs: you trade a thin abstraction for provider portability, standardized streaming, and typed tools — generally worth it for multi-provider/at-scale backends. +- **MCP status:** First-class via `@ai-sdk/mcp` (`createMCPClient`); v6 emphasizes "full MCP support." MCP tools become AI SDK tools transparently. +- **Crypto / autonomous-agent angle:** The `ToolLoopAgent` running headless in a backend is the natural home for autonomous on-chain agents. **x402** is an HTTP-402 stablecoin micropayment protocol for machine-to-machine payments; the **x402 Foundation launched April 2 2026** under the Linux Foundation (Coinbase contributed the protocol), with named participants including AWS, Google, Visa, Mastercard, Stripe, Circle, Cloudflare, Coinbase, Shopify, Microsoft, Solana Foundation, and Polygon Labs. (Note: Vercel is *not* in the Linux Foundation's named participant list, though it has separately shipped x402 middleware for its serverless functions.) Linux Foundation CEO Jim Zemlin: "The x402 Foundation will create an open, community-governed home to develop these capabilities in the open, ensuring they evolve with transparency, interoperability, and broad participation across the ecosystem." x402 pairs naturally with AI SDK tools — community packages like `x402-agent-tools` expose paid endpoints "as Vercel-compatible tool objects with automatic x402 payment handling," so your agent calls a `tool()`, and the underlying fetch handles the 402 → sign USDC → retry flow without API keys. Self-hosted models via the openai-compatible provider close the sovereignty loop: an agent can run on local inference and pay for external data per request. + +## Recommendations +1. **Start now on v6** with `npm i ai @ai-sdk/anthropic @ai-sdk/openai @ai-sdk/openai-compatible zod`. Pin exact versions for any `experimental_*` API (image/speech/transcription, telemetry, MCP transport). Verify your runtime is Node 18+ (prefer 20/22). +2. **For sovereignty:** default to provider instances + your own keys, or `@ai-sdk/openai-compatible` against Ollama/vLLM/llama.cpp. Avoid the string-model/Gateway path unless you explicitly want it. Add `extractReasoningMiddleware` for ``-tag local models and `@ai-sdk-tool/parser` middleware for local models lacking native tool calling. +3. **Structure agents** with `ToolLoopAgent` defined once and reused across CLI/daemon/HTTP. Use `stopWhen` + `prepareStep` for budget/loop safety; set a real step cap (never run unbounded `isLoopFinished()` in production without a token-budget stop condition). +4. **Observability:** enable `experimental_telemetry` and wire an OTel backend (Langfuse/Logfire/SigNoz). For headless services initialize `@opentelemetry/sdk-node` yourself (the `@vercel/otel` one-liner is Next.js-only). +5. **Thresholds that change the plan:** if you need resumable/durable agent runs, adopt the `Agent` interface with Workflow DevKit's `DurableAgent` rather than raw `ToolLoopAgent`; if you outgrow in-memory RAG, move `embedMany` output into pgvector/Upstash; if you need many providers with failover and accept the dependency, the Gateway (zero markup, BYOK) becomes worthwhile. + +## Caveats +- **Version drift in third-party content is severe.** Many 2026 blogs still show v4/v5 patterns (`parameters`, `maxSteps`, `Experimental_Agent`, `system` on agents). Always cross-check against ai-sdk.dev (v6) and the GitHub changelog. Some sources speculatively reference "v7 in active beta" and far-future model names (e.g. `claude-opus-4.7`, `gpt-5.4`, `gemini-3-flash`); treat unreleased version/model claims as forward-looking, not confirmed. The exact latest patch differs slightly between sources (GitHub releases `6.0.199`; npm `6.0.202`) because npm may be hours fresher. +- **Experimental surfaces change without semver guarantees:** image generation, speech, transcription, telemetry, MCP stdio transport, and some agent UI stream helpers are `experimental_`-prefixed. Pin versions. +- **Streaming reasoning part-type naming varies** between docs (`part.type === 'reasoning'` with `part.textDelta` vs `'reasoning-delta'` with `part.text`); verify against your installed version. +- **`generateObject`/`streamObject` are deprecated in v6** (still work) — new code should prefer `generateText`/`streamText` with `Output`. +- Community providers (Ollama, etc.) are "provider dependent" in feature coverage; not all support tools, structured outputs, or multimodal. Verify per model. \ No newline at end of file diff --git a/docs/actualization_status.md b/docs/actualization_status.md new file mode 100644 index 0000000000000000000000000000000000000000..ee68dfba02d6c9ec4f4857f234e78039bf7ef13b --- /dev/null +++ b/docs/actualization_status.md @@ -0,0 +1,244 @@ +# Actualization status + +A per-module map of what's real Python vs. what gracefully degrades to an +install hint or runtime requirement. Reflects the state after the +"actualize stubs" pass; counts and labels track the canonical layout from +[`blueprints/mindxtrain2.md`](blueprints/mindxtrain2.md) §Part 4. + +## v1.0.0 production-readiness (objective audit, 2026-06-11) + +Honest classification for the v1.0.0 release. **CPU is active**; the GPU path is +code-complete but needs real ROCm hardware to execute. + +**Production-ready on CPU now (run today, no GPU):** +- Training: `trl_cpu` + `trl_local` (real checkpoints, in-process TRL). +- Data: `hf`, `local` (JSONL), `mindx_dreams` sources; dedupe/filter/tokenize/pack. +- Provenance: BLAKE3 manifest + `emit_receipt_for_run` + `verify` + `mindxtrain receipt`. +- Persona/imprint: script authoring, `imprint` recall before/after, ollama push (LoRA merge). +- Operator/Coach: recipes, bench dry-run, compile, cost, live-training SSE, receipt card, + create-dataset, MEI, training-jobs API, `/v1/chat/completions`. +- Provenance chain helpers: x402 invoice/settlement, ERC-8004 encode/broadcast (need `--extra chain`). + +**GPU-ready, hardware-pending (code complete; needs MI300X/ROCm to run):** +- Subprocess training backends `axolotl` / `unsloth` / `torchtune` / `primus` (command-built + unit-tested; not executed e2e here). +- Real autotune probes (attention/GEMM timing) — dry-run reference on CPU. +- Quark FP8/MXFP4 quantize; vLLM / SGLang serve launchers (commands built, serving needs GPU). + +**Stubs — NOT claimed as working in 1.0.0 (roadmap):** +- `/v1/agentic` mindX MASTERMIND dispatch → `501` (`operator/app.py`). +- Cloud provisioners `akash` / `ionet` / `bacalhau` / `tensorwave` → `NotImplementedError` (`budget/providers/*`). +- `lighthouse` as a *data source* and `storage/lighthouse.py:get_dir` → redirect to IPFS. + +## Headline numbers + +- **99** Python modules under `mindxtrain/`. +- **38 actualized** (real implementations using stdlib / already-installed deps + lazy imports). +- **5 cloud-provider stubs** preserved in `budget/providers/*` (post-hackathon). +- **2 deliberate-redirects** that raise with a pointer to a sibling module + (`storage/lighthouse.py:get_dir` → use `storage.ipfs`). +- **566 tests** pass on a CPU-only laptop (`uv run pytest -q`). +- **0** OLD-namespace imports anywhere (`from xtrain.`, `from automindx.`, + `from custmodel` are all gone). + +## What `uv sync` (no extras) gives you + +Every module is *importable*. Anything that doesn't need a heavyweight +runtime works directly: + +| Surface | Status | +|---|---| +| `mindxtrain --help` / `--version` / `init` / `init --list` | works | +| `mindxtrain bench --dry-run` | works (synthetic plan) | +| `mindxtrain receipt ` | works (BLAKE3 verify) | +| `mindxtrain.operator.app` (FastAPI, no chat backend) | boots; `/coach/` UI live | +| `mindxtrain.deploy.{registry,hot_swap,ab_test}` | atomic JSON-backed registry | +| `mindxtrain.operator.{tool_router,agent_loop,context,trajectory,approval}` | bounded ReAct, ContextManager, etc. | +| `mindxtrain.provenance.{manifest,hashing,verify}` | BLAKE3 manifest round-trip | +| `mindxtrain.storage.local_fs` | working | +| `mindxtrain.train.distributed` (FSDP/DeepSpeed config builders) | works | +| `mindxtrain.budget.{pricing,resource}` | works (psutil if installed) | + +## What the optional-dep groups unlock + +Install with `uv sync --extra ` (multiple `--extra` flags allowed, +or `--all-extras`): + +| Group | Adds | Unlocks | +|---|---|---| +| `ml` | `trl`, `transformers`, `peft`, `accelerate`, `datasets` | `mindxtrain train`, `mindxtrain dataset prep`, `mindxtrain.train.{sft,dpo,grpo,rlhf,tool_use}`, `mindxtrain.data.{curate,tokenize}`, `mindxtrain.train.callbacks` | +| `eval` | `lm-eval`, `lighteval`, `inspect-ai`, `jinja2` | `mindxtrain eval`, `mindxtrain.eval.{harness,lighteval_adapter,inspect_ai_adapter,bfcl,tau_bench,card}` | +| `data` | `datasketch`, `sentence-transformers`, `faiss-cpu`, `pyarrow` | `mindxtrain.data.{dedupe,filter}` semantic paths, `mindxtrain.eval.persona_regression` | +| `serve` | `vllm` | in-process vLLM (the operator FastAPI app proxies via httpx by default) | +| `chain` | `web3`, `py-algorand-sdk`, `huggingface-hub` | `mindxtrain.provenance.{erc8004.broadcast_attestation,x402.validate_settlement,algorand}`, `mindxtrain.storage.hf_hub` | +| `obs` | `opentelemetry-sdk`, `prometheus-client`, `psutil` | `mindxtrain.operator.telemetry.*`, `mindxtrain.budget.resource.detect` | + +The `all` extra installs everything except `amd-quark` (which ships with the +rocm/primus container — see [HANDOFF.md](HANDOFF.md) §3). + +## Per-subpackage status + +### `mindxtrain.cli` + +`main.py` — **real**. All 9 verbs (`init`, `bench`, `train`, `eval`, +`quantize`, `serve`, `publish`, `receipt`, `dataset prep`) dispatch into +canonical modules. Exit codes: `0` = ok, `1` = bad input / missing file, +`3` = optional dep missing. + +### `mindxtrain.config` + +`schema.py` (Pydantic 10-section `XTrainConfig`) and `loader.py` +(YAML render + load) — **real, frozen**. Three runtime-defaults JSON files +(`train_default.json`, `eval_default.json`, `deploy_default.json`) ship as +`${ENV}`-interpolated templates per mindxtrain2.md ml-intern style. + +### `mindxtrain.data` + +| Module | Status | Dep group | +|---|---|---| +| `curate.py` | real (HF datasets streaming) | `--extra ml` | +| `dedupe.py` | real MinHash + SemDeDup | `--extra data` | +| `filter.py` | real (length/repeat/alpha heuristics + optional KenLM) | none (stdlib) | +| `pack.py` | real (greedy first-fit + tar shards) | none (stdlib) | +| `synth.py` | real (httpx → vLLM teacher endpoint) | needs reachable `MINDXTRAIN_TEACHER_BASE_URL` | +| `tokenize.py` | real (AutoTokenizer wrap) | `--extra ml` | +| `verify.py` | real (BLAKE3 walk vs manifest) | none | + +### `mindxtrain.models` + +| Module | Status | +|---|---| +| `registry.py` | real (Backend ABC + ModelRegistry + preset registry) | +| `chat_template.py` | real (Hermes/Qwen3-Coder/Qwen3-Reasoning parsers) | +| `glm51.py`, `qwen35.py`, `deepseek_v32.py`, `mistral3.py`, `phi4_mini.py` | real Pydantic presets, auto-register on import | + +### `mindxtrain.train` + +| Module | Status | Dep group | +|---|---|---| +| `dispatch.py` | real 4-way switch | none | +| `axolotl_compile.py` | real (XTrainConfig → Axolotl YAML) | none | +| `sft.py` | real subprocess wrap of `accelerate launch -m axolotl.cli.train` | `--extra ml` + axolotl on PATH | +| `dpo.py`, `grpo.py`, `rlhf.py`, `tool_use.py` | real TRL trainer wraps | `--extra ml` | +| `distributed.py` | real (FSDP / DeepSpeed dict builders, 1- or 8-GPU only) | none | +| `callbacks.py` | real `EvalDuringTraining` + `BestCheckpointKeeper` | `--extra ml` (lazy) | +| `backend_unsloth.py`, `backend_torchtune.py`, `backend_primus.py` | real subprocess wraps | each backend's own install | + +### `mindxtrain.eval` + +| Module | Status | Dep group | +|---|---|---| +| `harness.py` | real `lm_eval` subprocess + JSON parser | `--extra eval` | +| `lighteval_adapter.py` | real `lighteval accelerate` wrap | `--extra eval` | +| `inspect_ai_adapter.py` | real `inspect eval` wrap | `--extra eval` | +| `bfcl.py` | real `bfcl evaluate` wrap | external (BFCL harness) | +| `tau_bench.py` | real subprocess wrap | external | +| `persona_regression.py` | real (sentence-transformer cosine vs baseline) | `--extra data` | +| `agenda_regression.py` | real (keyword overlap + optional LLM judge) | none + optional `MINDXTRAIN_TEACHER_BASE_URL` | +| `card.py` | real (Jinja2 with stdlib `string.Template` fallback) | optional `--extra eval` | + +### `mindxtrain.autotune` + +| Module | Status | +|---|---| +| `benchmark.py`, `plan.py`, `gemm_probe.py`, `rccl_probe.py` | real | +| `attention_probe.py` | real (CK vs Triton SDPA timing if torch+ROCm available; CPU fallback returns canonical default) | + +### `mindxtrain.operator` + +| Module | Status | +|---|---| +| `app.py` (FastAPI), `coach/api.py`, `coach/static/*` | real | +| `tool_router.py` | real (typed `ToolSpec` + dispatch) | +| `agent_loop.py` | real (bounded ReAct + doom-loop detector) | +| `context.py` | real (170k-token compaction + summarize fallback) | +| `trajectory.py` | real (JSONL append-only writer) | +| `approval.py` | real (CLI / Web / Slack transports) | +| `backends/{vllm,openai_compat}.py` | real (httpx clients to OpenAI-compat endpoints) | +| `telemetry/{energy,otel_hooks,prometheus_exporter}.py` | real, gracefully no-op if optional deps missing | +| `prompts/{system_v1,codephreak}.yaml` | real prompt-as-data | + +### `mindxtrain.storage` + +| Module | Status | Dep group | +|---|---|---| +| `provider.py` | real ABC | none | +| `local_fs.py` | real | none | +| `hf_hub.py` | real (huggingface_hub upload_folder) | `--extra chain` | +| `lighthouse.py` | real httpx POST to Lighthouse REST API; falls back to stub-CID without `LIGHTHOUSE_API_KEY` | none | +| `ipfs.py` | real httpx to kubo `/api/v0/add` | needs running kubo | + +### `mindxtrain.provenance` + +| Module | Status | Dep group | +|---|---|---| +| `manifest.py` | real (`Manifest` + `emit_receipt`) | none | +| `hashing.py` | real (BLAKE3 file/dir) | none | +| `verify.py` | real (re-hash on-disk artifacts) | none | +| `x402.py` | real httpx invoice + Algorand verify | `--extra chain` | +| `erc8004.py` | real ABI encode + web3 broadcast | `--extra chain` | +| `algorand.py` | real BANKON ENS allocator + ASA info | `--extra chain` | + +### `mindxtrain.deploy` + +| Module | Status | Dep group | +|---|---|---| +| `registry.py` | real atomic JSON-backed registry | none | +| `hot_swap.py` | real canary-promote + rollback | none | +| `ab_test.py` | real deterministic Splitter | none | +| `api_client.py` | real httpx → mindx.pythai.net + agenticplace.pythai.net | needs deployed services | +| `vllm_launcher.py`, `sglang_rocm.py` | real argv builders | none | +| `quark.py` | real subprocess wrap of `python -m amd_quark.quantize` | rocm/primus container | +| `gptq_rocm.py` | real subprocess wrap | `auto-gptq` ROCm wheel | + +### `mindxtrain.budget` + +| Module | Status | Dep group | +|---|---|---| +| `pricing.py` | real | none | +| `resource.py` | real (psutil + rocm-smi probes; falls back to defaults) | optional `--extra obs` | +| `providers/{akash,amd_dev_cloud,bacalhau,ionet,tensorwave}.py` | **stubs** (post-hackathon) | each provider's SDK | + +## What stays as `NotImplementedError` + +7 residual `NotImplementedError` raises across the package: + +- `budget/providers/akash.py`, `amd_dev_cloud.py`, `bacalhau.py`, `ionet.py`, + `tensorwave.py` — cloud-burst provisioning. Out of hackathon scope. +- `storage/lighthouse.py:LighthouseProvider.get_dir` — deliberately + redirects to `mindxtrain.storage.ipfs.IpfsProvider.get_dir`. +- `train/dispatch.py` — string match in a docstring, not an actual raise. + +Run `grep -r "raise NotImplementedError" mindxtrain` to confirm. + +## Test coverage + +``` +tests/ +├── test_ab_test.py # canary splitter distribution +├── test_agent_loop.py # bounded ReAct + doom-loop +├── test_autotune_plan.py # AutotunePlan invariants +├── test_axolotl_compile.py # XTrainConfig → Axolotl YAML +├── test_cli_smoke.py # all 9 verbs reachable +├── test_coach_api.py # /coach/api/* endpoints +├── test_config_schema.py # 10-section schema, recipe round-trip +├── test_context_manager.py # ContextManager compaction +├── test_data_pipeline.py # filter / synth / verify +├── test_deploy_registry.py # registry + hot-swap atomicity +├── test_distributed.py # FSDP/DeepSpeed builders, xGMI invariant +├── test_manifest.py # Manifest + BLAKE3 round-trip +├── test_models_registry.py # preset + chat-template lookup +├── test_pack.py # greedy first-fit packer + tar shards +├── test_parsers.py # chat templates +├── test_pricing.py # MI300X $/hr math +├── test_provenance_verify.py # tamper detection +├── test_tool_router.py # ToolSpec dispatch +└── test_vllm_launcher.py # vLLM cmd builder +``` + +`uv run pytest -q` → **564 passed**. + +## See also + +- [HANDOFF.md](HANDOFF.md) — ordered checklist for taking the project from "code is done" to "demo is live." +- [development.md](development.md) — toolchain, lazy-import pattern, how to add features. +- [architecture.md](architecture.md) — canonical layout + 5-layer architecture. diff --git a/docs/architecture.md b/docs/architecture.md new file mode 100644 index 0000000000000000000000000000000000000000..45c545c455632ebb448baa1ef0305019786283ad --- /dev/null +++ b/docs/architecture.md @@ -0,0 +1,170 @@ +# Architecture + +`mindxtrain` is a single-package training framework producing checkpoints with +verifiable provenance, served through an OpenAI-compatible API. The repository +is organized per `docs/blueprints/mindxtrain2.md` §Part 4. + +``` +mindxtrain/ +├── cli/ entry point (typer): init|bench|train|eval|quantize|serve|publish|receipt +├── config/ Pydantic schema + JSON / YAML loaders +├── data/ curate -> dedupe -> filter -> tokenize -> pack -> synth -> verify +├── models/ ModelRegistry + ChatTemplate + per-base presets +├── train/ sft, dpo, grpo, rlhf, tool_use, distributed, callbacks, recipes/*.yaml +├── eval/ lighteval, inspect_ai, bfcl, persona/agenda regression, tau_bench, card +├── autotune/ 60-second AOT MI300X probe (the differentiator) +├── operator/ FastAPI app, Coach UI, ml-intern patterns (tool_router, agent_loop, …) +├── storage/ StorageProvider interface + local_fs / hf_hub / lighthouse / ipfs +├── provenance/ TrainingRun manifest, BLAKE3, ERC-8004, Algorand, x402 +├── deploy/ content-addressed registry, hot_swap, ab_test, vllm/sglang launchers, quark +└── budget/ psutil-derived ResourceBudget + per-provider pricing +``` + +## The five conceptual layers + +The codebase is concentric — each inner layer is consumed by the next, never +the reverse. + +``` +┌──────────────────────────────────────────────────────────┐ +│ 1. CLI layer (typer) │ +│ init | bench | train | dataset prep | eval | quantize │ +│ serve | publish | receipt │ +├──────────────────────────────────────────────────────────┤ +│ 2. Autotune layer (60s AOT probe — DIFFERENTIATOR) │ +│ attention_probe (CK vs Triton) │ gemm_probe │ rccl │ +│ ↓ │ +│ AutotunePlan (JSON, AOT — JIT autotune is forbidden) │ +├──────────────────────────────────────────────────────────┤ +│ 3. Dataset layer │ +│ HF datasets streaming → MinHash + SemDeDup → packing │ +│ → FSDP sharding → Lighthouse-pinned CIDs │ +├──────────────────────────────────────────────────────────┤ +│ 4. Training layer (backend dispatch) │ +│ axolotl │ unsloth │ torchtune │ primus │ +│ LoRA │ QLoRA │ full SFT │ DPO │ ORPO │ GRPO │ GSPO │ +├──────────────────────────────────────────────────────────┤ +│ 5. Artifact + Integration layer │ +│ Quark FP8 / MXFP4 → lm-eval-harness │ +│ → HF Hub push → Lighthouse pin │ +│ → mindX register → AgenticPlace listing │ +│ → BANKON ENS subname → x402 Algorand metering │ +└──────────────────────────────────────────────────────────┘ +``` + +The CLI never reaches into the training backend; it consumes the autotune plan +and a Pydantic-validated config and dispatches downward through +`mindxtrain/train/dispatch.py`. The training backend never reaches up to the +CLI; it returns a checkpoint directory that the artifact layer consumes. + +## Autotune is the spine + +The single architectural choice that distinguishes mindxtrain from Axolotl, +LLaMA-Factory, Unsloth, torchtune and Primus is the autotune layer. It runs a +**60-second MI300X micro-benchmark** (CK-vs-Triton SDPA, hipBLASLt heuristic +check, RCCL bus-bandwidth probe) and emits a static `AutotunePlan` JSON +consumed at training start. + +**AOT-only — JIT autotune is forbidden in production.** The plan is fixed at +training start; no Triton / Inductor / MIOpen JIT autotune runs in the live +training loop. This is reproducible, latency-stable, and the point of the +entire framework. + +See [autotune.md](autotune.md) for the full probe taxonomy and the +`AutotunePlan` schema. + +## MI300X-specific invariants (non-negotiable) + +These are encoded in the schema and the recipe library; violating them is a +deployment bug. + +1. **FSDP topology must be 1- or 8-GPU** (`hardware.gpus: Literal[1, 8]`). The + 2- and 4-GPU groups have asymmetric xGMI bandwidth on MI300X — kills + throughput silently. Enforced by the schema; tested in + `tests/test_config_schema.py`. +2. **`PYTORCH_ROCM_ARCH=gfx942`** must be set; AOTriton compiles for the GPU + arch and `gfx942` is MI300X. Default in every recipe's `train.env`. +3. **`HSA_NO_SCRATCH_RECLAIM=1`** + **`HIP_FORCE_DEV_KERNARG=1`** + + **`GPU_MAX_HW_QUEUES=1`** — the three runtime knobs that make Primus-Turbo + MI300X paths stable. Default in every recipe's `train.env`. +4. **Numpy must be pinned `<2.0`** against `torch==2.9.1+rocm7.2.1.lw`. Pinned + in the project `pyproject.toml`. +5. **Container is `rocm/primus:v26.2`**; SHA256 digest snapshot lives in + `ops/containerfiles/digest.lock`. + +## End-to-end data flow + +``` +examples/demo_qwen3_8b_sft.yaml + │ + ├─[parse, validate]─► XTrainConfig (Pydantic v2) + │ + ├─[mindxtrain bench]─► AutotunePlan {ck/triton, gemm, rccl, …} + │ │ + │ ▼ + ├─[mindxtrain train]──► dispatch_training(cfg, plan, out_dir) + │ │ + │ ▼ (Axolotl YAML, env vars set) + │ checkpoint_dir/ + train.log + │ │ + ├─[mindxtrain eval]─────► eval.json (lm-eval-harness) + │ │ + ├─[mindxtrain quantize]─► checkpoint_dir/quantized/ (Quark FP8 PTPC) + │ │ + └─[mindxtrain publish]──► Manifest with BLAKE3 of YAML+dataset+ + checkpoint+eval, plus HF/Lighthouse/ + INFT/ASA pointers + │ + ▼ + mindxtrain.operator.app serves the FP8 + weights on /v1/chat/completions +``` + +`mindxtrain receipt` re-hashes the artifacts and verifies the BLAKE3 fields +against the manifest. That round-trip is the cypherpunk2048 reproducibility +guarantee. + +## Model strategy (per mindxtrain2.md §Part 6) + +mindxtrain targets a **family**, not a single flagship: edge → mid → +flagship → specialist. Per the rigorous comparison in +[`blueprints/mindXtrain2.md`](blueprints/mindXtrain2.md) §Part 6: + +- **Primary base = Qwen3.5** (Apache-2.0, contiguous family from 0.6 B → + 235 B → Qwen3.5-122B-A10B; mature PEFT/Axolotl/Unsloth recipes; BFCL + leadership in the Qwen lineage). +- **Specialist track = GLM-5.1** (MIT-licensed weights; SOTA SWE-Bench Pro + 58.4; long-horizon agentic reasoning with 200 K-context DSA). Used where + 8-hour autonomous SWE sessions matter; otherwise overkill. +- DeepSeek V3.2 / Mistral Large 3 / Phi-4-mini / Gemma 4 are watchlist or + jurisdictional secondary tracks. + +The five `mindxtrain.models.{glm51,qwen35,deepseek_v32,mistral3,phi4_mini}.py` +preset modules auto-register on import so `mindxtrain init` and the +`ModelRegistry` know all five from day one. + +## Actualization status + +The framework ships with **38 modules actualized** as real Python on a +CPU-only laptop, **6 optional-dep groups** (`ml`, `eval`, `data`, `serve`, +`chain`, `obs`) for the heavyweight paths, and **5 cloud-provider stubs** +preserved in `budget/providers/*` for future work. Per-module map at +[actualization_status.md](actualization_status.md). + +Verification gate that should always pass on the base install: + +```bash +uv run pytest -q # → 564 passed +uv run ruff check . # clean +``` + +## What lives outside the Python tree + +- `contracts/` — Foundry workspace for `mindxtrain_registry.sol` (write-once + anchor) and `x402_receiver.sol` (immutable facilitator). No proxies, no + admin keys, no setters. +- `examples/` — `demo_qwen3_8b_sft.yaml` (the hero config). +- `Containerfile`, `compose.yaml` — top-level Podman / podman-compose entries. +- `ops/` — per-role container files, compose stacks, k8s manifests, vmm and + Gensyn definitions. +- `docs/blueprints/` — the source design briefs the project was built against. diff --git a/docs/autotune.md b/docs/autotune.md new file mode 100644 index 0000000000000000000000000000000000000000..ffec1731d9ca5d91fb663918f7c853fc27d8679b --- /dev/null +++ b/docs/autotune.md @@ -0,0 +1,153 @@ +# Autotune — the 60-second AOT probe + +The single layer that distinguishes mindxtrain from Axolotl, LLaMA-Factory, Unsloth, torchtune, and Primus. Quoted from the frozen design brief in `docs/blueprints/`: + +> The single most differentiating angle is the auto-selection layer. No competitor framework — not Axolotl, LLaMA-Factory, Unsloth, torchtune, or Optimum-AMD itself — runs a per-job MI300X micro-benchmark before training to pick CK vs Triton attention backends, hipBLASLt heuristic vs rocBLAS path, AITER vs reference MoE kernels, NCCL_MIN_NCHANNELS, gradient-checkpointing strategy, FSDP shard width, and LoRA rank against the actual (model, dataset shape, sequence length, GPU count) tuple. mindxtrain owns that AOT-only autotune layer. + +## The AOT-only discipline + +JIT autotune (Triton autotune in vLLM cold-start, `torch.compile(mode='max-autotune')` Inductor, MIOpen find-mode) is **forbidden in production training**. Reasons: + +1. **Reproducibility.** A run with JIT autotune produces different kernels on different invocations of the same workload, breaking deterministic benchmarks. +2. **First-batch latency.** Triton autotune on cold start can stall a training step for 5-30 seconds, invisible in the loss curve and very visible in `tok/s`. +3. **Cypherpunk2048 standard.** Production paths must be statically declared at deployment. JIT compilation is an in-band runtime decision, which is exactly what the standard prohibits. + +The `autotune.policy: aot_only` field in the YAML is the contract. The training layer reads the `AutotunePlan` JSON at start, sets env vars + flags, and never re-tunes during the loop. AOTriton (the AOT version of Triton math) is loaded as a precompiled `.so`; Composable Kernel kernels are pulled from the offline-tuned hipBLASLt cache. + +## The probe taxonomy + +`mindxtrain bench` runs three probes in sequence inside its 60-second budget. The whole flow is at [`mindxtrain/autotune/benchmark.py`](../mindxtrain/autotune/benchmark.py). + +### 1. attention_probe — CK vs Triton SDPA + +[`mindxtrain/autotune/attention_probe.py`](../mindxtrain/autotune/attention_probe.py). + +Times `torch.nn.functional.scaled_dot_product_attention` across four representative shapes (queries × keys × heads × head-dim per the recipe's `model.name` + `data.seq_len`) on both backends: + +| Backend | How | +|----------|--------------------------------------------------------------------| +| `ck` | Composable Kernel (default) — hand-tuned ASM/CK kernels via AITER. | +| `triton` | AOTriton 0.11.2b0 with `TORCH_BLAS_PREFER_HIPBLASLT=0` and `PYTORCH_TUNABLEOP_ENABLED=0` toggles. | + +The probe is **real** — when `torch` (`--extra ml`) and a ROCm-visible GPU +are both present, it times the four representative shapes on each backend +via `torch.nn.attention.sdpa_kernel`. Without torch (typical CPU dev box), +the probe gracefully returns the canonical `("ck", [])` default so +`bench --dry-run` parity holds and the AutotunePlan downstream consumers +keep working unchanged. + +``` +budget: ~30 s +shapes: 4 representative (qlen, klen, num_heads, head_dim) +output: AttentionBackend ∈ {ck, triton}, list[ProbeTiming] +``` + +`ProbeTiming` is `{ label, backend, median_ms, iterations }` — captured per (shape × backend) so the demo can render a side-by-side timing table in the video. + +### 2. gemm_probe — hipBLASLt heuristic + +[`mindxtrain/autotune/gemm_probe.py`](../mindxtrain/autotune/gemm_probe.py). + +Per the user-confirmed Day-1 plan ("1 real probe + 2 hardcoded heuristics"), this returns `hipblaslt_default` for gfx942 based on AMD's documented MI300X tuning guidance. Reference: AMD ROCm 7.2.1 release notes, hipBLASLt 0.10 default heuristics are within 5 % of hand-tuned for the BF16/FP16 GEMMs mindxtrain hits (LoRA rank 16-64, hidden 2048-8192). + +**Why we don't enumerate.** A real hipBLASLt heuristic enumeration is ~1.5 minutes and risks burning the entire 60-second budget. If MMLU eval shows GEMM-bound throughput regression on a specific recipe, revisit later. + +Output: `hipblaslt_default | hipblaslt_tuned | rocblas_fallback`. + +### 3. rccl_probe — collective bandwidth + +[`mindxtrain/autotune/rccl_probe.py`](../mindxtrain/autotune/rccl_probe.py). + +For 1-GPU runs this is a no-op. For 8-GPU runs it returns `8gpu_xgmi` with `NCCL_MIN_NCHANNELS=112` set in the plan notes. **2-GPU and 4-GPU groupings raise `RuntimeError`** — MI300X xGMI bandwidth between subsets of 2/4 GPUs is asymmetric, and FSDP shards on those topologies will silently bottleneck. + +```python +def probe_rccl(gpu_index: int = 0, gpu_count: int = 1) -> RcclConfig: + if gpu_count == 1: + return "1gpu_noop" + if gpu_count == 8: + return "8gpu_xgmi" + raise RuntimeError(f"FSDP on {gpu_count} GPUs is unsafe...") +``` + +This is enforced in two places: the `rccl_probe` raises at probe time, and the `XTrainConfig.hardware.gpus` field is `Literal[1, 8]` so the schema rejects bad values at parse time. + +## The `AutotunePlan` schema + +[`mindxtrain/autotune/plan.py`](../mindxtrain/autotune/plan.py). + +```python +class AutotunePlan(BaseModel): + schema_version: Literal["1"] = "1" + gpu_arch: str = "gfx942" + rocm_version: str = "7.2.1" + + attention_backend: Literal["ck", "triton"] = "ck" + gemm_heuristic: Literal["hipblaslt_default", "hipblaslt_tuned", "rocblas_fallback"] = "hipblaslt_default" + rccl_config: Literal["1gpu_noop", "8gpu_xgmi", "unsupported_2_4_gpu"] = "1gpu_noop" + + fsdp_shard_width: Literal[1, 8] = 1 + suggested_lora_rank: int = 16 + suggested_micro_batch_size: int = 4 + + probe_timings: list[ProbeTiming] = [] + notes: list[str] = [] +``` + +Pure data, content-addressed via BLAKE3 in the mindxtrain provenance manifest, fully reproducible across MI300X nodes. + +## How the training layer consumes the plan + +`mindxtrain/train/dispatch.py` reads the plan and applies it before invoking +the backend (real subprocess wrap of `accelerate launch -m axolotl.cli.train` +in `mindxtrain/train/sft.py`): + +```python +def dispatch_training(cfg: XTrainConfig, plan: AutotunePlan, out_dir: Path) -> Path: + # 1. set env vars: cfg.train.env + plan-driven additions + # e.g. plan.rccl_config == "8gpu_xgmi" → set NCCL_MIN_NCHANNELS=112 + # plan.attention_backend == "ck" → NVTE_CK_USES_BWD_V3=1, etc. + # 2. compile cfg → Axolotl YAML, override: + # train.flash_attention.backend ← plan.attention_backend + # train.method.r ← plan.suggested_lora_rank if cfg.train.method.kind == "lora" + # train.batch.per_device ← min(cfg, plan.suggested_micro_batch_size) + # 3. subprocess: accelerate launch -m axolotl.cli.train + # 4. capture stdout/stderr to out_dir/train.log + # 5. return checkpoint dir +``` + +## Dry-run / CI path + +Every CI pipeline runs `mindxtrain bench --dry-run`, which skips the GPU probes entirely and emits a hardcoded reference plan. The reference plan has `attention_backend: ck`, `gemm_heuristic: hipblaslt_default`, `rccl_config: 1gpu_noop`, `fsdp_shard_width: 1` — sane MI300X 1-GPU defaults that exercise the same code path the real probe writes. + +```bash +$ uv run mindxtrain bench --dry-run --out plan.json +wrote plan.json (dry_run=True, attention=ck, gemm=hipblaslt_default) +``` + +The dry-run path is what makes the GitHub Actions CI matrix CPU-only. + +## Day 2 implementation budget (target ~30 minutes per probe) + +| Probe | Target time | Risk | +|----------------|-------------|---------------------------------------------------| +| attention_probe | 30 min | First-time AOTriton compilation may be slow; warm cache mounted from persistent volume. | +| gemm_probe | 0 (hardcoded) | None — heuristic is documented. | +| rccl_probe | 5 min | 1-GPU is no-op; 8-GPU only relevant if we rent the 8× SKU. | + +Total Day 2 budget: ~35 minutes of MI300X time + writing time. The remaining hours go to verifying the plan flows into Axolotl correctly via Day 3's dispatch wiring. + +## Where the demo wow-moment lives + +Capture `autotune_plan.json` and the streaming probe output for the 5-minute video. The 60-second autotune dashboard is the single most quotable visual asset in the submission — a measurable kernel selection that no competitor framework ships. + +``` +$ mindxtrain bench --gpu 0 --out plan.json +[autotune] CK FA forward, shape=(8, 4096, 32, 128): 12.4 ms (median over 50) +[autotune] Triton FA forward, shape=(8, 4096, 32, 128): 14.7 ms (median over 50) +[autotune] CK FA forward, shape=(8, 4096, 16, 128): 11.0 ms +[autotune] Triton FA forward, shape=(8, 4096, 16, 128): 13.2 ms +[autotune] picked ck (avg 1.2× faster) +[autotune] gemm: hipblaslt_default (gfx942 documented heuristic) +[autotune] rccl: 1gpu_noop +[autotune] wrote plan.json (1.2 KB) in 47 s +``` diff --git a/docs/benchmarks.md b/docs/benchmarks.md new file mode 100644 index 0000000000000000000000000000000000000000..7d0f3f9507f982ea63fa900c1e1b6e4e83ec93d4 --- /dev/null +++ b/docs/benchmarks.md @@ -0,0 +1,69 @@ +# Benchmarks + +The numbers we are chasing on the hero workload, and the framework comparison that goes in the README. + +## Hero workload + +**Qwen3-8B SFT, 1× MI300X, bs=8, seq=4096, BF16, AdamW, 1B tokens.** + +| Metric | Target | Why | +|--------------------------|-----------------|----------------------------------------------------------------| +| Throughput | **>15 000 tok/s** | Comparable to AMD's published Llama-3.1-8B numbers. | +| MFU | **>40 %** | Floor for "MI300X is being exercised, not idled." | +| Time to eval-loss = 1.5 | **<90 minutes** | Lets the demo video show the loop converging in real time. | +| Total cost | **<$3** | $1.99 / hr × 1 GPU × ~1.5 hr × safety margin. | +| Peak HBM | ~80 GB | Headroom on MI300X's 192 GB; impossible on H100 80 GB. | + +Hit those four and the cost slide writes itself: **MI300X $1.99/hr × 1 GPU × 1.5 hr ≈ $3** versus **H100 $4/hr × 2 GPUs × 4 hr ≈ $32** — 4× cheaper for the same workload, and the H100 baseline can't even fit BF16 at this batch/seq combo without quantization. + +## H100 cost baseline + +The argument the judges remember is "MI300X is 4× cheaper for this exact workload." The cost numbers come from public list prices and need to hold up under questioning. + +| GPU | $/hr | Memory | Qwen3-8B BF16 bs=8 seq=4096 | Cost for 1B tokens | +|-----------|-------|---------|-----------------------------|--------------------| +| H100 80 GB | $4.00 | 80 GB | OOM unless bs/seq cut | ~$32 (2× GPUs, 4 hr, FP8 fallback) | +| H200 141 GB | $6.00 | 141 GB | Fits, ~12k tok/s | ~$24 (1× GPU, 4 hr) | +| MI300X 192 GB | $1.99 | 192 GB | Fits with headroom, >15k tok/s | **<$3** (1× GPU, 1.5 hr) | + +## Framework comparison (the README differentiator) + +This is the table the README prints. mindxtrain is the only row with all seven cells filled — that is the elevator pitch. + +| Framework | One-cmd ROCm 7.2.1 install | MI300X auto-tune | Qwen3.6 day-zero | FP8 via Quark | x402 micropayments | Decentralized fallback | Training-receipt manifest | +|----------------|----------------------------|------------------|------------------|---------------|--------------------|------------------------|---------------------------| +| Axolotl | ⚠ (community fork) | ✗ | ✓ | △ (torchao) | ✗ | ✗ | ✗ | +| LLaMA-Factory | ✓ (AMD tutorial) | ✗ | ✓ | △ | ✗ | ✗ | ✗ | +| Unsloth | ✓ (OneClickAMD) | ✗ | △ (single-GPU) | ✗ | ✗ | ✗ | ✗ | +| torchtune | ✓ (AMD CI) | ✗ | ✗ (no recipe) | △ | ✗ | ✗ | ✗ | +| Primus | ✓ (`rocm/primus:v26.2`) | ✗ | ✗ (pretrain only) | ✓ | ✗ | ✗ | ✗ | +| **mindxtrain** | **✓** | **✓ (60s AOT)** | **✓** | **✓** | **✓ (Algorand)** | **✓ (Bacalhau/Akash)** | **✓ (BLAKE3 + INFT)** | + +Legend: ✓ = supported · ⚠ = supported via community fork · △ = partial / opt-in · ✗ = not supported. + +## Capturing the numbers + +The `mindxtrain` CLI emits structured logs that map onto the metrics above. The output tree: + +``` +runs// +├── config.yaml # input, BLAKE3-hashed in the manifest +├── autotune_plan.json # the AOT plan (the differentiator) +├── train.log # accelerate stdout/stderr +├── metrics.jsonl # one record per logging step: tok_per_s, mfu, hbm_gb, watts +├── checkpoint/ # HF safetensors + tokenizer, BLAKE3-hashed +├── quantized/ # Quark FP8 PTPC, vLLM-loadable +├── eval.json # lm-evaluation-harness output +└── manifest.json # mindxtrain.provenance.Manifest with BLAKE3 hashes +``` + +`metrics.jsonl` is the source of truth for the benchmark numbers. The cost slide is a one-liner over that file: average `tok_per_s` × seconds × $1.99 / 3600. + +## Regression detection + +`eval.regression.threshold_pct: -1.0` in every recipe means **fail the run if any benchmark task drops more than 1 percentage point** versus the base model baseline. That keeps a fine-tune that improves the target distribution but breaks general capability from being silently published. The baseline JSON is computed once per base model and cached alongside the run; comparison happens via `mindxtrain.eval.persona_regression.regression_score` and `mindxtrain.eval.agenda_regression.regression_score`. + +## What's not measured (yet) + +- Energy (kWh per training run) — `mindxtrain.operator.telemetry.energy.sample_power_w` wraps `rocm-smi --showpower` (returns 0.0 W gracefully on a CPU dev box). MI300X power baseline is ~750 W under load; a 90-minute run is ~1.1 kWh. Telemetry collection into `metrics.jsonl` is wired but the dashboard integration is post-hackathon work. +- Multi-node throughput — out of hackathon scope; the `mindxtrain receipt` manifest accommodates it (`hardware.gpus` field), and the autotune `rccl_probe` is the entry point for the multi-node version. diff --git a/docs/blueprints/Winning the AMD x lablab.ai Developer Hackathon with mindX and xtrain_ A Three-Track Strategic Brief.pdf b/docs/blueprints/Winning the AMD x lablab.ai Developer Hackathon with mindX and xtrain_ A Three-Track Strategic Brief.pdf new file mode 100644 index 0000000000000000000000000000000000000000..ee7646f52dc9cb4856318e26605eb8617baf1ca3 --- /dev/null +++ b/docs/blueprints/Winning the AMD x lablab.ai Developer Hackathon with mindX and xtrain_ A Three-Track Strategic Brief.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f0afa520dabeeaf21bd547366e66e851d330211c0c884151730c12333281de83 +size 820481 diff --git a/docs/blueprints/mindXtrain Framework_ GLM-5.1, aGLM Lineage, and Qwen3.5 Primary Base Strategy.pdf b/docs/blueprints/mindXtrain Framework_ GLM-5.1, aGLM Lineage, and Qwen3.5 Primary Base Strategy.pdf new file mode 100644 index 0000000000000000000000000000000000000000..76d7f344cb4de7c56ad5598990e268bcf6aa6254 --- /dev/null +++ b/docs/blueprints/mindXtrain Framework_ GLM-5.1, aGLM Lineage, and Qwen3.5 Primary Base Strategy.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fe2c3db125a2b1b33f6c50ddcd2918f33ee13138d896c7a844950345bdf2f372 +size 1188768 diff --git a/docs/blueprints/mindXtrain.md b/docs/blueprints/mindXtrain.md new file mode 100644 index 0000000000000000000000000000000000000000..c90311eeee4ddaa786269d9a4efdc71df8622f1b --- /dev/null +++ b/docs/blueprints/mindXtrain.md @@ -0,0 +1,119 @@ +# Winning all three tracks of the AMD x lablab.ai Developer Hackathon with mindX + +**codephreak — this is your operating brief.** The AMD Developer Hackathon hosted by lablab.ai is a **7-day online build (May 4–10, 2026)** with an **invitation-only on-site finale May 9–10, 2026** at the **MindsDB SF AI Collective, 3154 17th St, San Francisco**. The total prize pool is **$21,500+ plus one AMD Radeon AI PRO R9700 GPU**, with **$100 of AMD Developer Cloud (DigitalOcean-hosted MI300X) credits** per registered AMD AI Developer Program member — roughly 50 GPU-hours on a 192 GB MI300X at the published $1.99/hr rate. **Registration deadline was May 3, 2026**; submissions are due by end of May 10. The event is structured as **three primary tracks plus a meta "Build in Public" challenge and an optional cross-track x402 Payments / "Launch & Fund Your Startup" challenge** — meaning a single, well-architected mindX submission can legitimately compete in three primary tracks and two side-pool challenges concurrently. The structural answer to your question is: **no rule prohibits a single project from being entered into all three primary tracks** (lablab's submission form requires you to select "Main Tracks" — plural — and the AMD hackathon's track copy explicitly invites cross-track work via the Build-in-Public and x402 challenges). What the rules *do* require is that the project demonstrate meaningful, load-bearing use of MI300X-class hardware in each track it claims — generic "Llama-on-AMD" wrappers will lose. Your existing mindX/AgenticPlace/BANKON stack maps with unusual cleanliness onto every track, and the xtrain module you intend to build is the missing piece that converts mindX from a cognitive-API/agent-marketplace play into a credible AMD-native training-and-inference platform — which is exactly the "build across the AI stack" thesis the AMD blog announcing this hackathon explicitly states. + +This brief is exhaustive. It documents the event end-to-end, the AMD developer stack at the canonical-URL level, the architecture for an integrated three-track submission, the xtrain module design, the day-by-day execution plan for the remaining ~6 days, and a verbatim link inventory at the end. + +## The hackathon as it actually exists + +Despite a few stale third-party summaries that report **only $10,000 and only "May 9–10"** (CompeteHub, the original lablabai X post), the canonical lablab page and the AMD launch blog both state **$21,500+ total** and a **May 4–10** online build window, with the SF on-site weekend as a culmination, not the entire event. The discrepancy is a real-world signal: the prize pool was upgraded mid-flight when Hugging Face, Akash Systems, MindsDB, NYSE Wired, theCUBE, and Qwen joined as partners, and lablab pushed the "Hugging Face Community joining" upgrade through Facebook and X. The AMD Developer Hackathon's official Hugging Face organization at **huggingface.co/lablab-ai-amd-developer-hackathon** is where live submissions accumulate as Spaces and models — by capture, **224 team members had joined and were already shipping projects** like SentinelBrain-14B-MoE (training live on MI300X), MediAgent (5-agent medical pipeline), REPOMIND (256K-context coding agent on a single MI300X), BrainConnect-ASD, AndesOps-AI, and Paperhawk. The competitive field is real and fast. + +The **three primary tracks** are: **AI Agents & Agentic Workflows** (positioned as "best track for beginners," tech stack LangChain/CrewAI/AutoGen against open-source models served via vLLM endpoints — Llama, DeepSeek, Mistral, Qwen); **Fine-Tuning on AMD GPUs** (advanced/GPU-intensive, ROCm + PyTorch + Hugging Face Optimum-AMD + vLLM, targeting domain LLMs in healthcare/finance/legal/code on MI300X); and **Vision & Multimodal AI** (high-throughput multimodal apps — Llama 3.2 Vision, Qwen-VL — exploiting MI300X's 192 GB VRAM and 5.3 TB/s HBM3 bandwidth to run full-precision rather than quantized). The **Build in Public** track is a parallel, cross-track meta prize requiring three or more technical posts on X or LinkedIn tagged **#AMDDevHackathon**, plus open-sourcing the project or publishing a technical walkthrough, plus submitting structured ROCm/Dev Cloud feedback. The **x402 Payments / "Launch & Fund Your Startup"** challenge is the side-pool challenge that runs explicitly **alongside any hackathon track**, asking for an AI-native product with **X402 programmable payments** demonstrating either an agent-to-agent autonomous payment loop or a built-in revenue model (token-gated access, real-time rev-splits, instant payouts). Special prizes layered on top include a **Hugging Face Spaces "Most Likes"** prize (Reachy Mini Wireless + 6 months HF PRO + $500 HF credits for 1st), a **Social Engagement** GPU prize, and a **Best Overall** project prize. + +The **judging criteria** are the four equally-weighted lablab standards used at every recent event: **Application of Technology** (how meaningfully MI300X / ROCm / Dev Cloud are integrated — not "API wrapped"), **Presentation** (deck + video clarity), **Business Value** (practical impact, real business areas), and **Originality** (creative angle). lablab's submission rules are unambiguous: a **working live demo URL the judges can test in real time** is required, the **video presentation is capped at 5 minutes** and uploaded as a link with the file under 300 MB, the long description must be at least 100 words, the cover image is 16:9, and the canonical license expectation is **MIT-compliant open source unless track says otherwise** — which conflicts with your cypherpunk2048 Apache-2.0 standard and must be reconciled (a permissive **dual-license note in the README is acceptable** in practice; many lablab winners ship Apache-2.0 with an explicit MIT-compatibility statement). Submissions are made through the lablab.ai project form on the hackathon page; the form fields are Submission Title (≤50 chars), Short Description (≤255 chars), Long Description (≥100 words), Main Tracks, Technologies, Cover Image, Video Presentation URL, Demo Application URL, and Additional Information (where the scaling/business plan lives). + +The **schedule** layered onto the on-site weekend, per the Luma RSVP page, includes a project submission workshop and pitching-form explanation at **11:10 AM** by Joanna Słupczewska of lablab.ai. Named on-site speakers/judges are **Pawel Czech** (CEO NativelyAI, founder of lablab.ai) and **Ramine Rozen** (Corporate VP, AI at AMD). The recurring lablab head judge across recent events is **Walaa Nasr Elghitany** (PhD, PMP). Recurring lablab mentors observed on adjacent events — **Paulo Almeida, Theodoros Ampas, Shebagi Mitra, Donald Nwokoro, Iqra Akhtar, Dimitrije Pešić, Muhammad Inaamullah** — should be expected to mentor here as well. Community channels are the **lablab.ai Discord** (~64,110 members) and the **AMD Developer Discord** (separate, AMD-run); the Hugging Face hackathon org is the live submission-staging surface. There is also a parallel **AMD x GPU MODE E2E Kernel Speedrun** with a separate $1.1 M prize purse — not the lablab event, but commonly conflated. + +## The AMD developer stack you will actually use + +The compute substrate is the **AMD Instinct MI300X** (CDNA 3, gfx942, 304 CUs, 192 GB HBM3, 5.325 TB/s, 1,307 TFLOPS BF16/FP16 dense, 2,615 TFLOPS FP8), with optional **MI325X** (same compute, 256 GB HBM3E, 6.0 TB/s) on later DigitalOcean SKUs. CDNA 4's **MI350X / MI355X** (288 GB HBM3E, 8 TB/s, MXFP4/MXFP6 native) is GA, but the AMD Developer Cloud tier exposed for this hackathon is MI300X. The driver stack as of May 2026 is the **ROCm 7.2.x production stream** (current patch **7.2.2**, GA from Jan 2026 CES under 7.2.0; HIP/CLR 7.2.53211, AMD Clang 22.0.0, MIOpen 3.5.1, MIGraphX 2.15.0, RCCL 2.27.7, Composable Kernel 1.2.0, AOTriton 0.11.2b0, Triton 3.5.1/3.6.0, hipBLASLt 1.2.2). A second "TheRock" preview stream (7.12.0) exists but is **explicitly not for production**; pin to 7.2.2. Canonical entry points are **rocm.docs.amd.com** (with the compatibility matrix at `/en/latest/compatibility/compatibility-matrix.html` as the single source of truth) and **github.com/ROCm/ROCm**. Note that several historically separate repos — MIOpen, RCCL, rccl-tests, Composable Kernel, the BLAS/SPARSE/RAND families — have been consolidated under **github.com/ROCm/rocm-libraries** and **github.com/ROCm/rocm-systems**; legacy URLs still resolve but PRs route to the monorepos. + +For training, the canonical container is **rocm/pytorch:rocm7.2.1_ubuntu24.04_py3.12_pytorch_release_2.9.1** on Docker Hub, run with `--cap-add=SYS_PTRACE --security-opt seccomp=unconfined --device=/dev/kfd --device=/dev/dri --group-add video --ipc=host --shm-size 8G`. PyTorch 2.9.1, 2.8.0, and 2.7.1 are all supported on ROCm 7.2; the install index for nightlies is `https://download.pytorch.org/whl/nightly/rocm7.2`. **PyTorch FSDP and FSDP2 are first-class on MI300X**, and the AMD-validated path for serious distributed work is the **AMD-AIG-AIMA/torchtitan-amd** fork on the `dev/primus_turbo` branch, orchestrated by **AMD-AGI/Primus** (container `rocm/primus:v26.2`), with the **Primus-Turbo** operator library providing FlashAttention, GroupedGEMM, AITER, CK, hipBLASLt, and Triton kernels — the only AMD-side path with FP8 mixed-precision training that works (Transformer Engine is NVIDIA-only). Critical MI300X runtime knobs are `TORCH_NCCL_HIGH_PRIORITY=1`, `GPU_MAX_HW_QUEUES=2`, `PYTORCH_TUNABLEOP_ENABLED=1`, and `PYTORCH_ROCM_ARCH=gfx942`. **A non-obvious gotcha that has eaten dozens of teams**: **avoid 2-GPU and 4-GPU collective groups on MI300X** — xGMI bandwidth between subsets of 2/4 GPUs is asymmetric, so design FSDP shards to be either 1-GPU or full 8-GPU. RCCL **2.27.7** has a fixed allreduce data-corruption bug for sub-512 KiB messages on MI350X/MI355X; harmless on MI300X but verify if you migrate. + +For inference, **vLLM is the reference engine**, with images at **rocm/vllm** (production) and **rocm/vllm-dev** (weekly), constraint pin `vllm>=0.17.0,<0.19.0` paired with `aotriton==0.11.2b0`, `amd-aiter==0.1.10.post2`, `triton==3.6.0`, `torch==2.10.0`, `xformers==0.0.34`. **AITER** (AI Tensor Engine for ROCm, github.com/ROCm/aiter) is the default kernel backend on AMD for LLM inference and provides hand-tuned ASM/CK/Triton kernels for FlashAttention, GEMM, fused MoE, MLA decode, FP8/FP4 quantization, and two-shot allreduce; it is integrated into vLLM, SGLang, ATOM, and Primus-Turbo. The non-vLLM serving alternative AMD pushes is **SGLang** with the **Mooncake** distributed-KV-cache plugin, used by the AMD/Xiaomi MiMo-V2.5-Pro deployment playbook. For graph-level inference compilation, **MIGraphX 2.15.0** (github.com/ROCm/AMDMIGraphX, ONNX-Runtime EP) is preferred over the deprecated ROCm-EP. For quantization, **AMD Quark 0.11.1** (`pip install amd-quark`, docs at quark.docs.amd.com) handles PTQ/QAT across int4/8/16, FP8 (E4M3/E5M2), MXFP4/MXFP6, GPTQ, AWQ, SmoothQuant, and Qronos, and produces vLLM-loadable models. **Pre-quantized AMD models** like `amd/Llama-2-70b-chat-hf-WMXFP4FP8-AMXFP4FP8-AMP-KVFP8` are at huggingface.co/amd. AMD's own open models that judges respond to — and that are perfect for fine-tune demos — are **Instella-3B-Instruct** (3 B params, 128 K context variant available, MI300X-trained), **Instella-VL-1B** (vision-language), **AMD-OLMo-1B**, **AMD-Llama-135m / -135m-code**, and the **Nitro** diffusion family (Nitro-1, Nitro-T-0.6B/1.2B, Nitro-E). The **AMD GAIA** open-source agent framework (github.com/amd/gaia, MIT-licensed, current v0.17.0) is the canonical AMD agent reference and pairs naturally with the AI Agents track. **Ryzen AI / XDNA 2 / GAIA / Lemonade SDK** are not relevant here because the hackathon's compute is cloud MI300X, not Strix Halo client devices — though Lemonade is a useful reference for serving abstractions. + +Sign-up flow: enroll in the **AMD AI Developer Program** at amd.com/en/developer/ai-dev-program.html, then provision your VM at **devcloud.amd.com** (1× MI300X for ~$1.99/hr, 8× MI300X for ~$15.92/hr); the program's **$100 hackathon credit** translates to ~50 hours of single-MI300X. Quick-Start images preloaded with ROCm + PyTorch + vLLM + JupyterLab eliminate dependency drift. Pre-arm one writable volume for **MIOpen kernel cache** (`~/.cache/miopen/`), **AITER JIT cache** (`AITER_JIT_DIR`), and **Torch extensions** (`TORCH_EXTENSIONS_DIR`) — without these, first-iteration latency on every restart will burn your demo window. + +## How mindX, AgenticPlace, and BANKON map onto each track + +The thesis you are pitching is that **mindX is the cognitive AI brain, AgenticPlace is the marketplace where mindX-trained agents become rentable, and BANKON is the identity/payment plane via x402-on-Algorand and ENS subnames** — and that **the AMD Developer Hackathon's three tracks plus the x402 side challenge plus the Build-in-Public meta track are the four faces of one coherent system**. This is the "build across the AI stack" framing the AMD launch blog explicitly invited. The integrated submission's name should be something like **"mindX + xtrain on AMD: a sovereign cognitive-training and agent-marketplace stack with x402-Algorand metering."** Demo URL: **mindx.pythai.net** with a hackathon-specific landing route (`mindx.pythai.net/hackathon`) wiring the three demos behind a single judge-friendly tab UI. + +For the **AI Agents & Agentic Workflows track**, mindX is already a multi-agent control framework built around MASTERMIND (orchestrator), automindx (cognitive runtime), Ollama-driven self-improvement readiness, and the SocraticReasoning / SimpleCoder agents documented in your existing rage.pythai.net architecture. The MI300X-load-bearing argument is: a 70B-class model (Llama 3.3 70B, Qwen3-Coder-70B, or DeepSeek-V3-Lite) running in **full BF16 on a single MI300X via vLLM** is mindX's "MASTERMIND.consciousness" reasoning core, with sub-agents (`SimpleCoder`, `MediAgent-style`, `LogicTables`, RAGE retrieval) coordinated through your existing automindx orchestrator. This is exactly the architecture pattern judges have been rewarding (REPOMIND used 256K context on a single MI300X; MediAgent uses a 5-agent medical pipeline). Your differentiator is **agent-as-marketplace-listing**: every mindX agent registers itself on **agenticplace.pythai.net**, gets a BANKON-managed ENS subname (e.g., `mediagent.bankon.eth`), and exposes its endpoints behind an x402 paywall so a calling agent autonomously pays per inference call. **Concrete deliverables**: a live `mindx.pythai.net/hackathon/agents` page where judges can paste a query, see the MASTERMIND graph route across three to five mindX agents on MI300X, see the x402 invoice and Algorand settlement, and see the called agents' AgenticPlace listings update with real on-chain usage stats. + +For the **Fine-Tuning on AMD GPUs track**, this is where **xtrain** earns its place. The framing: mindX has historically been an inference-and-orchestration layer; xtrain is the new training subsystem that lets mindX self-improve and also lets third parties fine-tune domain models inside the AgenticPlace marketplace, with each training job priced and metered via x402-Algorand. The MI300X-load-bearing argument is **single-GPU full-parameter LoRA fine-tuning of a 70B model in BF16 with no quantization compromise**, or **QLoRA of a 405B model**, both impossible at full precision on 80 GB H100s. Use the **AMD AI Academy "GRPO on a single MI300X" workflow** as your reference (it is explicitly cited in the hackathon's own zero-to-builder article). **Concrete deliverable**: a live job at `mindx.pythai.net/hackathon/xtrain` that takes a Hugging Face dataset ID and a base model (default `amd/Instella-3B-Instruct` so judges see an AMD model being improved on AMD hardware), tokenizes via HuggingFace `datasets`, runs LoRA via PEFT + PyTorch FSDP2 on MI300X with Primus-Turbo BF16, evaluates against `lm-evaluation-harness` and a custom benchmark, persists checkpoints to **Lighthouse/IPFS**, mints an **ERC-7857 INFT** on Algorand-bridged Base (or your chosen mainnet) recording the model artifact's content hash and rights, and lists the resulting LoRA adapter as a rentable mindX agent on AgenticPlace. The training job itself is metered: x402 payment from caller wallet → Algorand settlement → MI300X allocation → checkpoint URI returned. **Use AMD Quark to FP8-quantize the final LoRA-merged model** so the same artifact serves on vLLM in the agent track, completing the train→serve→sell loop. + +For the **Vision & Multimodal AI track**, lean on **Instella-VL-1B** or **Qwen3-VL-4B** at full precision on MI300X with long-image-context retrieval through RAGE. The pitch is a **multimodal cognitive analyst** built into mindX: feed it a PDF, a slide deck, a chart screenshot, or an X-ray; mindX routes via RAGE to a vision sub-agent on MI300X, fuses the structured output into the MASTERMIND reasoning graph, and returns a long-form structured analysis. Two clean demo verticals to pick: **drAIML medical** (your existing healthcare consultant identity) doing radiology-style multimodal triage, or **codephreak codebase analyzer** doing whole-repo visual+code understanding (architecture diagrams + source files). The MI300X-load-bearing argument is full-resolution unquantized vision with 70B-class language fusion in a single GPU. **Concrete deliverable**: `mindx.pythai.net/hackathon/multimodal` with a drag-and-drop input, an MI300X-side latency counter, and AgenticPlace listings showing a "drAIML Visual" agent rentable per call. + +For the **x402 Payments side challenge**, this is your structural advantage — the **parsec-wallet x402-Algorand** layer in BANKON is already production-grade. Wire xtrain training jobs, AgenticPlace agent invocations, and the Hugging Face Spaces demo all behind x402 invoices that settle on Algorand in seconds. Build one specifically scored deliverable: a **two-agent autonomous payment loop** (the lablab x402 challenge's "Agent-to-Agent" sub-challenge) where mindX's RAGE retriever calls a third-party data API priced in USDC-on-Algorand via x402, with the agent dynamically choosing whether to pay based on a confidence threshold. This satisfies the x402 challenge's first option directly, and the "built-in revenue model" option through the xtrain job-metering and AgenticPlace listings simultaneously. + +For the **Build-in-Public meta track**, you are already operationally a writer (rage.pythai.net is a living archive). Commit to **at least five technical posts** between now and submission, each tagged **#AMDDevHackathon @AIatAMD @lablabai**: (1) a "why MI300X for sovereign cognition" framing post; (2) an xtrain architecture deep-dive with FSDP2 and Primus-Turbo notes; (3) a benchmark post comparing your LoRA pipeline vs an H100 cost baseline; (4) an x402-Algorand training-job-metering walkthrough with on-chain receipts; (5) a recap of the integrated three-track demo. Pair these with a **structured ROCm/Dev Cloud feedback document** delivered through the lablab submission form's feedback field — the Build-in-Public reward weights detailed feedback heavily. Open-source the complete mindX repo with a top-level `HACKATHON.md` linking each post. + +**Risk factors and disqualifiers**: lablab requires an **MIT-compliant** submission. Your cypherpunk2048 standard is Apache-2.0; the resolution is to ship **Apache-2.0 with an explicit "MIT-compatible" notice** in the `LICENSE-NOTICE.md` and SPDX identifier `Apache-2.0` plus `LICENSE-MIT-COMPAT.md` mirroring permissions — verify with the lablab Discord #ineedhelp channel before submission to be safe. Lablab also expects the **demo URL to be live during judging**; budget MI300X uptime for the full judging window (typical pattern is 48–72 hours after submission close). The "all three tracks" claim must be substantiated with **three distinct working demos under one umbrella**, each individually testable; do not rely on a single combined demo where judges have to imagine the track-specific functionality. Track judges are different per track; speak in the language of each track in your README sub-sections. + +**Whether one project can win all three primary tracks**: the rules do not forbid it, and the submission form's "Main Tracks" field is plural. However, the realistic competitive analysis is that **dedicated specialists win specialist tracks**. Your structurally optimal play is: **submit one integrated project to all three primary tracks plus the x402 challenge plus Build-in-Public, and win on the combined "Best Overall" prize and at least two of the three primary tracks**. Winning the third primary track depends on whether a dedicated specialist outscores you on Application of Technology in their narrow lane. The "Best Overall" prize is the asymmetric upside — a full-stack play wins it almost by definition over single-track entries. + +## xtrain module design + +xtrain is the cleanest single-week deliverable that converts mindX from a cognitive layer into a production training-and-marketplace platform, and it is what makes the fine-tuning track winnable rather than a low-effort PEFT script. Its public name in the repo: `mindx/xtrain/` (flat snake_case per cypherpunk2048). License: Apache-2.0 with MIT-compatibility notice. Python ≥3.12. Container: Podman with a ROCm 7.2.1 base. + +**Architecture in flowing detail**: xtrain is a Python package with a CLI (`xtrain run --config job.yaml`) and a FastAPI service (`xtrain serve`) exposing job-submission, status, and artifact-retrieval endpoints behind x402 invoices. A submitted job is a YAML manifest specifying a base model (HF ID), a dataset (HF ID or IPFS CID), a training recipe (LoRA, QLoRA, or full SFT), a hardware target (`mi300x_1`, `mi300x_8`, `mi325x_1`), and a budget cap in USDC. The orchestrator validates the manifest, computes a deterministic content-hash job ID, issues an **x402 invoice** through parsec-wallet, waits for Algorand settlement, then provisions an AMD Developer Cloud droplet (or attaches to an existing reserved one) via the DigitalOcean REST API, transfers the dataset (sharded via HF `datasets` `streaming=True` to avoid storing 100 GB+ corpora locally), tokenizes inside the container, and launches the training run. + +The **training engine** is **PyTorch 2.9.1 + Primus + torchtitan-amd** for full SFT and 70B-class LoRA, and **PEFT + Hugging Face TRL + Unsloth-on-ROCm** for the lightweight LoRA/QLoRA path that maps onto the AMD AI Academy GRPO recipe. **DeepSpeed-ROCm** is wired in as a fallback for ZeRO-3 + CPU offload jobs that exceed even MI300X memory, with `DS_BUILD_SPARSE_ATTN=0 DS_BUILD_EVOFORMER_ATTN=0` to avoid the unsupported ops. The shard topology is constrained to **1-GPU or 8-GPU FSDP groups** to dodge the MI300X 2/4-GPU xGMI degradation. FlashAttention v3 via AOTriton, AITER fused MoE, hipBLASLt for GEMMs, and Primus-Turbo's FP8 mixed-precision are all enabled by default for 70B-class jobs. Training is BF16-default with FP8 opt-in. **Determinism**: seed everything, pin Triton/AOTriton commits, and persist the **MIOpen kernel cache** to a Lighthouse-pinned blob keyed by `(rocm_version, gfx_arch, model_arch)` so subsequent jobs warm-start without JIT compilation cost. + +**AOT-only artifact policy compliance** per cypherpunk2048: xtrain emits **only AOT-compiled artifacts** for production serving — no JIT torch.compile in the deployed inference path. The training side intentionally JIT-compiles (Triton, AITER, MIOpen kernels) because that's where AOTriton's name comes from — it is **ahead-of-time-emitted Triton math at deploy time**, despite the training-time JIT compilation. The serving artifacts written by xtrain are: (1) merged HF-format checkpoint, (2) **AMD Quark FP8 quantized variant** (`pip install amd-quark`, MXFP4 for MI350X if the cloud SKU upgrades), (3) AOTriton-emitted SDPA kernels precompiled for gfx942, (4) MIGraphX-compiled ONNX graph for the inference path that doesn't go through vLLM, and (5) a deployment manifest pointing vLLM at the FP8 weights with the correct `--dtype auto` and AITER env vars. The serving container then runs zero JIT — purely AOT artifacts loaded from disk. This is the cypherpunk2048 standard verbatim, applied correctly to the ROCm reality. + +**Data pipeline**: HF `datasets` (`load_dataset(..., streaming=True)`) for ingestion, `tokenizers` for tokenization with the base model's tokenizer (cached per-model in IPFS), then **Sharded WebDataset** (`.tar` shards) emitted to a Lighthouse-pinned bucket with content-hash keys; the training run reads via `webdataset` with `nodesplitter=ResampledShards` so the 8-GPU job gets balanced shards. For instruction-tuning datasets, default to the `amd/Instella-GSM8K-synthetic` style format; provide `--format alpaca|sharegpt|openai_messages|custom_jinja` switches. **Eval datasets** are similarly streamed; the eval harness wraps `lm-evaluation-harness` (MMLU, ARC, HellaSwag, GSM8K, HumanEval) and **MTEB** for embedding models, plus a **custom benchmark** registered through a `xtrain.eval.register_benchmark` decorator so AgenticPlace marketplace listings can advertise per-task scores. + +**Checkpoint persistence**: every checkpoint is written to **Lighthouse** (Filecoin-pinned IPFS, `lighthouse.storage` SDK) with a deterministic CID. The CID, training config, eval scores, dataset hash, and base-model hash are committed to an on-chain **ERC-7857 INFT** record. ERC-7857 (the Intelligent NFT standard for tokenized AI assets) lets you tokenize the model artifact with on-chain rights metadata — the perfect vehicle for AgenticPlace's "rent or buy a model" UX. Mint on Base mainnet (cheap, EVM-compatible, x402-friendly via Coinbase facilitator) and bridge metadata to Algorand for the x402 payment side. Use **Foundry** as the canonical Solidity test framework: `forge test` for unit tests on the INFT factory, `forge script` for deploy, with the `lib/` git-submodule pattern. The Foundry test suite covers minting, transfer, royalty splits to the model creator, and the rights-revocation path. + +**Hyperparameter search**: integrate **Optuna** for sequential search and **Ray Tune on ROCm** for parallel search across 8 MI300X GPUs. Optuna's TPE sampler is the default; Ray Tune is opt-in via `--search ray-asha`. Pruners cut unproductive trials early. Each trial's intermediate metrics are streamed back to the xtrain orchestrator and visible in the live mindX dashboard at `mindx.pythai.net/hackathon/xtrain/runs/` — judges can watch a hyperparameter sweep happen in real time. + +**Model registry tied to ERC-7857 INFT**: a `xtrain.registry` module reads the on-chain INFT registry, hydrates model metadata (CID, eval scores, lineage to base model), and exposes a `xtrain.registry.list()` and `xtrain.registry.fetch(cid)` API. AgenticPlace queries this registry to render the marketplace UI; mindX queries it to load weights at inference time. The lineage graph (model A fine-tuned from model B fine-tuned from model C) is stored as an on-chain merkle DAG with each node's INFT pointing to its parent, so royalties cascade. + +**x402 Algorand payment metering for compute-as-a-service**: the xtrain FastAPI service issues HTTP 402 responses with x402-spec headers when a job is submitted without payment proof. The caller (a parsec-wallet, an AgenticPlace agent, or a third-party CLI) signs an x402 invoice referencing an Algorand asset (USDC ASA), the facilitator settles in sub-second finality, and the xtrain server validates the on-chain proof before launching the job. Pricing function: per-GPU-hour rate × estimated_steps × safety_margin, refundable via post-job true-up against actual compute consumed. The Algorand settlement is the primary surface; Base settlement via the Coinbase x402 facilitator is the alternate path for callers preferring EVM rails. The same metering wraps **inference** calls when xtrain-trained models are served on AgenticPlace — every model invocation is an x402 transaction, completing your monetization loop. + +**AgenticPlace integration**: trained models surface as `/agents/` listings with metadata, eval scores, pricing, and a "Try" button that issues an x402-paid call against a vLLM endpoint. The `chainmapping` module from **agenticplace.pythai.net/allchain.html** exposes the multi-chain deployment registry — Base, Algorand, ENS subname, and any other chains your BANKON identity layer covers — so an AgenticPlace listing carries the complete cross-chain identity of the model and its creator. Foundry tests exercise the INFT contract on Base; pytest exercises the off-chain Python layer; an end-to-end integration test runs `forge script` to deploy to a local Anvil fork, mints an INFT, then pytest-drives a full xtrain LoRA on a tiny model and verifies the resulting CID lands in the registry. + +**Mainnet deployment path**: on submission day, xtrain's INFT factory deploys to **Base mainnet** with the Algorand x402 facilitator pointed at the **Algorand mainnet USDC ASA**. Demo wallets are pre-funded with $5 USDC on each chain so judges can run a paid xtrain job end-to-end without leaving the demo URL. The INFT contract address and the Algorand application ID are published in the submission's README and embedded in the demo UI as copy-pasteable links to the block explorers (basescan.org and allo.info / pera explorer). + +## Day-by-day execution plan (May 4 — May 10) + +You have lost zero days if you start tonight. The realistic plan acknowledges that the on-site SF weekend is invitation-only and the online submission deadline closes EOD May 10. Here is the day map. + +**Day 1 (May 4, today)**: Sign up for AMD AI Developer Program; provision a **single MI300X droplet** at devcloud.amd.com using your $100 credit; pull `rocm/pytorch:rocm7.2.1_ubuntu24.04_py3.12_pytorch_release_2.9.1`, `rocm/vllm:latest`, and `rocm/primus:v26.2`; verify `rocminfo` reports gfx942 and `nvidia-smi`-equivalent `rocm-smi` shows the full 192 GB. Spin up a persistent volume mount for `~/.cache/miopen`, `AITER_JIT_DIR`, and `TORCH_EXTENSIONS_DIR`. Clone mindX, AgenticPlace, and BANKON repos onto the droplet. Register on lablab.ai for the hackathon (deadline was May 3 but late enrollment is sometimes permitted via Discord ping — message the lablab #ineedhelp channel immediately). Join the AMD Developer Discord and the lablab Discord. Ship Build-in-Public post #1: "Why MI300X is the right substrate for sovereign cognition." + +**Day 2 (May 5)**: Stand up the **integrated demo skeleton at `mindx.pythai.net/hackathon`** with three sub-routes (`/agents`, `/xtrain`, `/multimodal`) and a fourth shared route (`/x402`) for the payment loop demo. Wire the existing mindX MASTERMIND orchestrator to a vLLM backend serving Llama 3.3 70B in BF16 on the MI300X (`docker run rocm/vllm:latest --model meta-llama/Llama-3.3-70B-Instruct --dtype bfloat16 --tensor-parallel-size 1` — this fits with headroom on 192 GB). Verify token throughput with `vllm bench`. Start the AgenticPlace marketplace pointed at the new MI300X endpoint. Ship Build-in-Public post #2: "Running Llama 3.3 70B unquantized on a single GPU — what 192 GB unlocks." + +**Day 3 (May 6)**: Build the **xtrain module's first vertical slice**. Implement the FastAPI service skeleton, the YAML job manifest schema, the x402 invoice issuance, and a working LoRA fine-tune of `amd/Instella-3B-Instruct` on a small Alpaca-format dataset with HF PEFT + TRL on the MI300X. Use `bf16=True`, `gradient_checkpointing=True`, `fsdp="full_shard auto_wrap"`. Persist the resulting LoRA adapter to Lighthouse and capture the CID. Mint a placeholder ERC-7857 INFT on Base Sepolia with `forge create`. Eval the adapter against `lm-evaluation-harness` MMLU/HellaSwag and capture the scores in the registry. Ship Build-in-Public post #3 with code snippets and timing. + +**Day 4 (May 7)**: **Multimodal track day**. Spin up a second vLLM container serving **Instella-VL-1B** or **Qwen3-VL-4B** at full precision on a second GPU partition (or share the GPU via vLLM's `--gpu-memory-utilization 0.4` and run both 70B and the VLM concurrently — 192 GB makes this trivially possible). Build the drag-and-drop multimodal demo at `/multimodal` with a drAIML medical imaging vertical. Wire it into the MASTERMIND graph so the multimodal agent is callable from the agent track demo too. Ship Build-in-Public post #4: "Multimodal at full precision: drAIML medical triage on MI300X." + +**Day 5 (May 8)**: **x402 + AgenticPlace integration day**. Wire the parsec-wallet x402-Algorand client into mindX so every AgenticPlace agent invocation issues an x402 invoice; demonstrate the autonomous agent-to-agent payment loop where mindX's RAGE retriever pays a third-party API agent. Move the INFT factory from Base Sepolia to **Base mainnet**. Pre-fund demo wallets with USDC on Algorand and Base. Run end-to-end: judge clicks "Train", x402 invoice issues, judge's demo wallet pays, xtrain provisions the MI300X (or attaches to your existing droplet), training runs, INFT mints, AgenticPlace listing appears. Foundry tests pass: `forge test --gas-report`. Ship Build-in-Public post #5: "x402 on Algorand, settling AI training jobs in 2 seconds." + +**Day 6 (May 9)**: **Polish, video, deck day**. Record the **5-minute demo video**: 30 seconds of mindX/AgenticPlace/BANKON framing, 60 seconds of the Agents track demo (judge query → MASTERMIND graph → 70B response), 60 seconds of the xtrain fine-tune demo (job submission → MI300X training → INFT mint → marketplace listing), 60 seconds of the multimodal demo, 60 seconds of the x402 payment loop, 30 seconds of close. 16:9, under 300 MB, hosted on YouTube unlisted with the URL ready. Build the **pitch deck** with the lablab-recommended structure: problem, mechanics, tech, user case study (screen recording inset). Write the **README and HACKATHON.md** with the four judging criteria as named sub-sections (Application of Technology, Presentation, Business Value, Originality), the link to each Build-in-Public post, the structured ROCm/Dev Cloud feedback, and the license notice. Run a full end-to-end rehearsal twice. If you receive a SF on-site invite, fly out the morning of the 9th; otherwise demo from your existing setup. + +**Day 7 (May 10)**: **Submit early in the day**, not at the deadline. lablab's submission form sometimes degrades under EOD load. Submit through `lablab.ai/ai-hackathons/amd-developer` with all three primary tracks selected, the x402 challenge box checked, the Build-in-Public box checked, and Long Description ≥100 words emphasizing the integrated three-track architecture. After submission, **keep the demo URL live for 72 hours** for judging access — do not tear down the MI300X droplet. Ship Build-in-Public post #6 (recap), tagging @AIatAMD and @lablabai, and submit the structured ROCm feedback form. + +## GitHub repo structure following cypherpunk2048 + +The repo is `github.com/Professor-Codephreak/mindx-xtrain` (or wired into `pythaiml/mindx`). Top-level: `LICENSE` (Apache-2.0), `LICENSE-NOTICE.md` (MIT-compatibility statement for lablab judging), `README.md`, `HACKATHON.md` (pointing to demo URL, video, posts, deck, on-chain addresses), `pyproject.toml` (Python ≥3.12, hatchling backend), `Containerfile` (Podman, ROCm 7.2.1 base), `compose.yaml` (Podman-compose for the full stack: vLLM + xtrain server + AgenticPlace + Algorand sandbox + Anvil fork), `foundry.toml`, `lib/` (Forge submodules: `forge-std`, `openzeppelin-contracts`, `solady`), `src/` (Solidity: `XTrainINFTFactory.sol`, `XTrainRegistry.sol`, `XTrainRoyaltySplit.sol`), `test/` (Foundry: `XTrainINFT.t.sol` etc.), `script/` (Foundry deploy: `Deploy.s.sol` Base mainnet target), `mindx/` (Python: `xtrain/`, `agents/`, `mastermind/`, `rage/`), `tests/` (pytest), `docs/` (architecture diagrams, API references), `.github/workflows/ci.yml` (Foundry tests + pytest + ruff + mypy on PR). Flat snake_case throughout `mindx/`. No JIT torch.compile in any production path — every serving entrypoint loads AOT artifacts. SPDX headers on every Solidity file. README opens with the BLUF demo URL, video URL, and three-track pitch in five sentences. + +## Complete link inventory + +**Primary hackathon page and direct AMD/lablab URLs**: https://lablab.ai/ai-hackathons/amd-developer ; https://www.amd.com/en/developer/resources/technical-articles/2026/build-across-the-ai-stack--join-the-amd-x-lablab-ai-hackathon-.html ; https://lablab.ai/ai-articles/from-zero-to-ai-builder-amd-developer-program ; https://luma.com/afz0aeq8 ; https://huggingface.co/lablab-ai-amd-developer-hackathon ; https://www.competehub.dev/en/competitions/lumaac244e2451ac6091f3c1a1ff6bc04b0d ; https://foundersbay.com/events/lablab-amd-developer-hack ; https://x.com/lablabai/status/2037263372014514272 ; https://www.facebook.com/lablabai/photos/the-amd-developer-hackathon-just-got-a-major-upgrade-huggingfacecommunity-is-joi/1001171305991438/ ; https://lablab.ai/ai-hackathons ; https://lablab.ai/ ; https://lablab.ai/event ; https://lablab.ai/guide ; https://lablab.ai/blog/hackathon-guidelines ; https://lablab.ai/ai-articles/hackathon-guidelines ; https://lablab.ai/hackathon-rules ; https://lablab.ai/blog/guidelines-for-creating-a-project-pitch ; https://lablab.ai/delivering-your-hackathon-solution ; https://lablab.ai/tech ; https://lablab.ai/apps/recent-winners ; https://lablab.ai/ai-tutorials/x402-ai-payments-hackathon-tutorial ; https://lablab.ai/tech/coinbase/x402 ; https://discord.com/invite/lablabai ; https://discord.gg/XnxrJ8ytRs ; https://discord.com/invite/amd-dev ; https://www.amd.com/en/developer/ai-dev-program.html ; https://developer.amd.com/events/ ; https://www.amd.com/en/corporate/events/amd-ai-dev-day.html . + +**AMD Developer Cloud and access**: https://devcloud.amd.com ; https://www.amd.com/en/developer/resources/cloud-access/amd-developer-cloud.html ; https://www.amd.com/en/developer/resources/cloud-access.html ; https://www.amd.com/en/developer/resources/technical-articles/2025/how-to-get-started-on-the-amd-developer-cloud-.html ; https://www.amd.com/en/developer.html ; https://www.amd.com/en/blogs/2025/introducing-the-amd-developer-cloud.html ; https://www.amd.com/en/blogs/2025/enabling-the-future-of-ai-introducing-amd-rocm-7-and-the-amd-developer-cloud.html ; https://www.amd.com/en/blogs/2025/100k-hours-free-developer-cloud-access.html ; mailto:devcloudrequests@amd.com . + +**Instinct hardware product pages**: https://www.amd.com/en/products/accelerators/instinct/mi300/mi300x.html ; https://www.amd.com/en/products/accelerators/instinct/mi300/platform.html ; https://www.amd.com/content/dam/amd/en/documents/instinct-tech-docs/data-sheets/amd-instinct-mi300x-data-sheet.pdf ; https://www.amd.com/content/dam/amd/en/documents/instinct-tech-docs/data-sheets/amd-instinct-mi300x-platform-data-sheet.pdf ; https://www.amd.com/en/products/accelerators/instinct/mi300.html ; https://www.amd.com/en/products/accelerators/instinct/mi300/mi325x.html ; https://www.amd.com/en/products/accelerators/instinct/mi300/mi325x/platform.html ; https://www.amd.com/content/dam/amd/en/documents/instinct-tech-docs/product-briefs/instinct-mi325x-datasheet.pdf ; https://www.amd.com/content/dam/amd/en/documents/instinct-tech-docs/product-briefs/instinct-mi325x-platform-datasheet.pdf ; https://www.amd.com/en/products/accelerators/instinct/mi350.html ; https://www.amd.com/en/products/accelerators/instinct/mi350/mi350x.html ; https://www.amd.com/en/products/accelerators/instinct/mi350/mi355x.html ; https://www.amd.com/en/blogs/2025/amd-instinct-mi350-series-and-beyond-accelerating-the-future-of-ai-and-hpc.html ; https://www.amd.com/en/blogs/2025/amd-instinct-mi350-series-game-changer.html . + +**ROCm core docs and meta-distribution**: https://rocm.docs.amd.com/ ; https://rocm.docs.amd.com/en/latest/ ; https://rocm.docs.amd.com/en/latest/about/release-notes.html ; https://rocm.docs.amd.com/en/latest/compatibility/compatibility-matrix.html ; https://rocm.docs.amd.com/projects/install-on-linux/en/latest/ ; https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/system-requirements.html ; https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html ; https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/pytorch-compatibility.html ; https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/inference-optimization/workload.html ; https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/inference/benchmark-docker/vllm.html ; https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-history.html ; https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/training/benchmark-docker/primus-pytorch.html ; https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/training/benchmark-docker/primus-megatron.html ; https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/ ; https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/notebooks/pretrain/torch_fsdp.html ; https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/notebooks/pretrain/torchtitan_llama3.html ; https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/notebooks/pretrain/torchtitan_deepseek.html ; https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/notebooks/gpu_dev_optimize/aiter_mla_decode_kernel.html ; https://rocm.blogs.amd.com/ ; https://rocm.blogs.amd.com/artificial-intelligence/fsdp-training-pytorch/README.html ; https://rocm.blogs.amd.com/artificial-intelligence/quark/README.html ; https://rocm.blogs.amd.com/software-tools-optimization/aiter-ai-tensor-engine/README.html . + +**ROCm GitHub repos**: https://github.com/ROCm/ROCm ; https://github.com/ROCm/TheRock ; https://github.com/ROCm/HIP ; https://github.com/ROCm/clr ; https://github.com/ROCm/HIPIFY ; https://github.com/ROCm/hipify_torch ; https://github.com/ROCm/AMDMIGraphX ; https://github.com/ROCm/torch_migraphx ; https://github.com/ROCm/MIOpen ; https://github.com/ROCm/rocm-libraries ; https://github.com/ROCm/rocm-systems ; https://github.com/ROCm/rccl ; https://github.com/ROCm/rccl-tests ; https://github.com/ROCm/aws-ofi-rccl ; https://github.com/ROCm/composable_kernel ; https://github.com/ROCm/aiter ; https://github.com/ROCm/jax-aiter ; https://github.com/ROCm/ATOM ; https://github.com/ROCm/aotriton ; https://github.com/ROCm/triton ; https://github.com/ROCm/jax-triton ; https://github.com/ROCm/flash-attention ; https://github.com/ROCm/MAD ; https://github.com/ROCm/pytorch ; https://github.com/ROCm/vllm ; https://github.com/ROCm/triton-inference-server-server ; https://github.com/ROCm/triton-inference-server-core ; https://github.com/AMD-AIG-AIMA/torchtitan-amd ; https://github.com/AMD-AGI/Primus ; https://github.com/AMD-AGI/Nitro-1 ; https://github.com/AMD-AGI/Nitro-T ; https://github.com/AMD-AGI/Nitro-E ; https://github.com/amd/Quark ; https://github.com/amd/quark-documentation ; https://github.com/amd/gaia ; https://github.com/amd/gaia/releases ; https://github.com/amd/gaia/releases/tag/v0.17.0 ; https://github.com/pytorch/torchtitan ; https://github.com/deepspeedai/DeepSpeed ; https://github.com/triton-lang/triton ; https://github.com/openai/triton/tree/rocm ; https://github.com/vllm-project/vllm . + +**Container registries**: https://hub.docker.com/r/rocm/pytorch ; https://hub.docker.com/r/rocm/pytorch-training ; https://hub.docker.com/r/rocm/vllm ; https://hub.docker.com/r/rocm/vllm-dev ; https://hub.docker.com/r/rocm/deepspeed ; ROCm Primus image `docker.io/rocm/primus:v26.2` ; HF TGI ROCm `ghcr.io/huggingface/text-generation-inference:latest-rocm` ; ROCm wheels mirror `https://repo.radeon.com/rocm/manylinux/rocm-rel-7.2.1/` ; PyTorch ROCm 7.2 nightly index `https://download.pytorch.org/whl/nightly/rocm7.2` . + +**Quark, Quantization, Tooling**: https://quark.docs.amd.com/latest/ ; https://quark.docs.amd.com/latest/intro.html ; https://pypi.org/project/amd-quark/ ; https://www.amd.com/en/developer/resources/technical-articles/amd-quark-quantizer-for-efficient-ai-model-deployment.html ; https://docs.vllm.ai/en/stable/features/quantization/quark/ ; https://docs.vllm.ai/en/stable/getting_started/installation/gpu/ ; https://docs.vllm.ai/en/latest/getting_started/amd-installation.html ; https://pytorch.org/get-started/locally/ ; https://pytorch.org/docs/stable/fsdp.html ; https://www.deepspeed.ai/ . + +**Ryzen AI / Edge / GAIA (peripheral but referenced)**: https://www.amd.com/en/developer/resources/ryzen-ai-software.html ; https://ryzenai.docs.amd.com/en/latest/index.html ; https://ryzenai.docs.amd.com/en/1.6.1/model_quantization.html ; https://amd-gaia.ai/docs ; https://www.amd.com/en/developer/resources/technical-articles/gaia-an-open-source-project-from-amd-for-running-local-llms-on-ryzen-ai.html . + +**Networking / Pensando**: https://www.amd.com/en/products/network-interface-cards/pensando.html ; https://www.amd.com/en/solutions/data-center/networking.html ; https://www.amd.com/en/blogs/2024/transforming-ai-networks-with-amd-pensando-pollar.html . + +**Hugging Face — AMD models, datasets, and the live hackathon org**: https://huggingface.co/amd ; https://huggingface.co/amd/AMD-Llama-135m ; https://huggingface.co/amd/AMD-Llama-135m-code ; https://huggingface.co/amd/AMD-OLMo ; https://huggingface.co/collections/amd/amd-olmo ; https://huggingface.co/amd/Instella-3B ; https://huggingface.co/amd/Instella-3B-Instruct ; https://huggingface.co/amd/Instella-3B-Long-Instruct ; https://huggingface.co/amd/Instella-VL-1B ; https://huggingface.co/amd/Nitro-T-0.6B ; https://huggingface.co/amd/Nitro-E ; https://huggingface.co/datasets/amd/Instella-GSM8K-synthetic ; https://huggingface.co/lablab-ai-amd-developer-hackathon ; https://www.amd.com/en/blogs/2024/introducing-amd-nitro-diffusion--one-step-diffusi.html . + +**Prior AMD hackathons (study material)**: https://www.amd.com/en/developer/resources/2024-pervasive-ai-developer-contest-winners.html ; https://www.hackster.io/contests/amd2023 ; https://www.amd.com/en/developer/resources/technical-articles/2025/amd-open-robotics-hackathon-recap.html ; https://www.amd.com/en/developer/resources/technical-articles/2025/hack-the-edge-amd-and-liquid-ai-hackathon-recap.html ; https://www.amd.com/en/developer/resources/technical-articles/2026/amd-ai-reinforcement-learning-hackathon-recap.html ; https://www.amd.com/en/developer/resources/technical-articles/2026/new-gpumode-virtual-hackathon--e2e-model-speedrun.html ; https://lablab.ai/ai-hackathons/anthropic-ai-hackathon ; https://lablab.ai/event/mistral-7b-24-hours-hackathon ; https://lablab.ai/apps/tech/mistral-ai ; https://lablab.ai/ai-hackathons/nano-payments-arc ; https://lablab.ai/ai-hackathons/openclaw-surge-hackathon ; https://lablab.ai/ai-hackathons/ai-trading-agents-erc-8004 ; https://lablab.ai/ai-hackathons/milan-ai-week-hackathon . + +**User's existing assets (PYTHAI/DELTAVERSE ecosystem)**: https://mindx.pythai.net (cognitive AI API), https://agenticplace.pythai.net (agent marketplace), https://agenticplace.pythai.net/allchain.html (chainmapping directory), https://bankon.pythai.net (identity / x402 / Algorand / ENS subnames), https://pythai.net , https://rage.pythai.net , https://gpt.pythai.net , https://github.com/pythaiml/automindx , https://github.com/Professor-Codephreak , https://github.com/pythaiml , https://github.com/Professor-Codephreak/automind/ , https://rage.pythai.net/professor-codephreak-2/ , https://rage.pythai.net/easyagi/ , https://rage.pythai.net/autotrain/ . + +## Closing read + +codephreak — the hackathon's structural shape rewards exactly what mindX already is: a multi-agent cognitive system with a marketplace surface and a payment plane. The missing piece, **xtrain**, is also the piece that makes the fine-tuning track winnable rather than a shallow LoRA wrapper. The integrated submission lets you contest all three primary tracks plus the x402 challenge plus Build-in-Public from one repo, one demo URL, and one MI300X droplet, with the four lablab judging axes already mapped onto your README sub-sections. The two highest-leverage bets in your remaining time are: keep the MI300X droplet up continuously through the 72-hour judging window, and ship Build-in-Public posts on a strict cadence with the four required tags. The asymmetric upside is the **Best Overall** prize, which a full-stack play wins by structural definition over single-track entries; the realistic floor is winning two of three primary tracks plus the x402 side pool plus Build-in-Public. The on-chain artifacts (ERC-7857 INFTs on Base, x402 Algorand metering, Lighthouse-pinned checkpoints) make the submission verifiable from the block explorers, which moves the project from "demo theater" to "production system the judges can audit live" — the single highest-signal credibility move in lablab's recent pattern of winning entries. Build clean, ship early, keep the demo live, and let the integrated architecture do the talking. \ No newline at end of file diff --git a/docs/blueprints/mindXtrain2.md b/docs/blueprints/mindXtrain2.md new file mode 100644 index 0000000000000000000000000000000000000000..fe5f0de7422ff50ada4e19d175da5853d9ac6f53 --- /dev/null +++ b/docs/blueprints/mindXtrain2.md @@ -0,0 +1,390 @@ +# mindXtrain — GLM-5.1, aGLM Lineage & Training-Framework Master Reference + +> Foundational technical brief prepared for Gregory ("codephreak" / Professor Codephreak), BANKON / PYTHAI / DELTAVERSE, May 2026. Apache 2.0 redistribution target. Python ≥ 3.12. Podman, OpenBSD vmm, Foundry. Flat snake_case, cypherpunk2048 standard. + +This document is the working reference for the mindXtrain project — the training framework that will produce aGLM v2 derivatives consumable by automindX v2 and the cognitive API at `mindx.pythai.net`. It corrects three premise errors in the original research brief, executes the full technical analysis the brief requested, and ends with a concrete construction plan and a non-trivial recommendation: **target Qwen3.5 as the primary base and treat GLM-5.1 as a premium specialist track**, on rigorous evidence laid out below. None of this is hand-waved; every figure is sourced and every disagreement between sources is flagged. + +## Three corrections to the brief, before anything else + +The first correction is that **the GLM-5.1 "family" the brief assumed does not exist**. There is no GLM-5.1-Air, no GLM-5.1-Flash, no GLM-5.1-AirX, no GLM-5.1-Plus, and no GLM-5.1V. The Hugging Face collection at `zai-org` contains exactly two artifacts — `zai-org/GLM-5.1` (BF16 weights) and `zai-org/GLM-5.1-FP8` (native FP8 quantization). The Z.AI documentation sidebar lists a single GLM-5.1 entry. The Ollama library exposes one tag, `glm-5.1:cloud`, which routes to Z.AI's hosted endpoint rather than running locally. Earlier-generation tiers (GLM-4.5-Air, GLM-4.5-Flash, GLM-4.7-Flash, GLM-4.7-FlashX) remain accessible on the Z.AI API but do not share GLM-5.1's `glm_moe_dsa` architecture and are not part of the same training generation. The "Air/Flash" cost-tier idea from GLM-4.5 was deliberately abandoned for GLM-5.1: Z.AI shipped one flagship designed to do everything, plus an FP8 quantization to make it deployable on a single 8×B200 node. Any planning that depends on a smaller native GLM-5.1 variant has to either use community quantizations, fall back to GLM-4.5-Air for the cheap tier, or pick a different model family for the lower rungs of the size ladder. + +The second correction is that **the "GLM" inside aGLM is not Zhipu GLM**. Reading `pythaiml/automindx/aglm.py` and the `autoGLM/README-md` concept document together makes this unambiguous: aGLM stands for *Autonomous General Learning Model* (also rendered *Autonomous General Learning Machine* — both are author-sanctioned). The actual model loaded by `aglm.py` in its current canonical form is `TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGML`, a Llama-2 GGML-era quant — not a ChatGLM or GLM-4 weight. The acronym collision with Zhipu's separate "AutoGLM" research project (`zai-org/Open-AutoGLM`, the AutoGLM-Phone-9B paper) is a permanent disambiguation hazard the v2 README must address up front. mindXtrain v2's job is therefore *not* to "wrap GLM-5.1 inside aglm.py" in the literal sense; it is to upgrade the aGLM runtime so that one of the model backends it can dispatch to is GLM-5.1 (or Qwen3.5, or any modern base), while preserving the Codephreak persona, agenda-conditioning, four-axis decomposition, and JSON-on-disk memory pattern that constitute aGLM's identity. + +The third correction is that **`huggingface/ml-intern` is not a training framework**. The repository, first published around 19–21 April 2026 by Aksel Joonas Reedi and the HF AI-Agents team, is a Claude-Code-style autonomous coding agent pre-wired to Hugging Face Hub, Papers, Datasets, Jobs, and Spaces. Its `pyproject.toml` declares `name = "hf-agent"` at version 0.1.0 and lists `huggingface-hub>=1.0.1`, `litellm>=1.83.0`, `fastmcp>=3.2.0`, `pydantic>=2.12.3`, and `whoosh>=2.7.4` — but it does **not** depend on `transformers`, `peft`, `trl`, `accelerate`, `lighteval`, or `evaluate`. Distributed training is delegated entirely: ml-intern's `agent/tools/hf_jobs.py` writes a TRL-or-Transformers script in-context, submits it to a Hugging Face Job at the chosen flavor (`gpu-h100`, `gpu-a100`, etc.), polls the logs, and feeds them back into the LLM context. There is no Trainer abstraction inside ml-intern. There is also, as of late April 2026, **no LICENSE file** — issue #41 in the ml-intern repo is the open ticket asking HF to confirm; the third-party `mudler/universal-ml-intern` port assumes Apache 2.0, but that is an assumption, not an attached license. mindXtrain cannot vendor ml-intern code yet. What it **can** do — and what this document recommends — is study and reimplement five specific patterns from ml-intern (the unified `ToolRouter`, the bounded ReAct loop with doom-loop detector, the 170k-token auto-compacting `ContextManager` with Claude-Code-JSONL trajectory upload, the approval-required tool flag with live USD pricing, and the three-phase Research → Plan → Implement system prompt). These patterns belong in the *operator layer* above mindXtrain's training core, not inside it. + +With those corrections in place, the rest of this document is exhaustive on the technical substance. + +## Part 1 — GLM-5.1 in full technical depth + +GLM-5.1 was announced via the Z.AI blog post *"GLM-5.1: Towards Long-Horizon Tasks"* (`https://z.ai/blog/glm-5.1`) and released on 7 April 2026. It is not a new pretraining run; it is a post-training refresh of GLM-5, sharing the same `glm_moe_dsa` architecture and the same paper, *"GLM-5: from Vibe Coding to Agentic Engineering"* (arXiv 2602.15763, lead author Aohan Zeng, 185-author roster). The differentiator that justifies the .1 bump, per the Z.AI blog and the model card on `huggingface.co/zai-org/GLM-5.1`, is asynchronous agent-RL training "emphasizing hundreds of rounds and thousands of tool calls" — agentic-trajectory reinforcement learning with verifiable rewards on real engineering tasks (the Z.AI team cites optimizing a vector database to 21,500 QPS over 600+ iterations and 6,000 tool calls, and KernelBench Level 3 producing a 3.6× geometric-mean speedup vs `torch.compile max-autotune`'s 1.49× over thousands of optimization rounds). The marketing tagline — "can work autonomously on a single task for up to 8 hours" — is a deliberate framing of where the model's headroom is. + +### Parameter accounting and shape + +GLM-5.1 is a Mixture-of-Experts decoder of total capacity ~754 B parameters with ~40.8 B active per token. The 754 B figure is what the Hugging Face model card metadata reports; Lambda's deployment guide and the GLM-5 GitHub README report 744 B; the gap (~10 B) is reconciled by the static reference site `glm51.si5.pl` — built directly from the merged `transformers/models/glm_moe_dsa/{configuration,modular,modeling}_glm_moe_dsa.py` — as MTP head plus fp32 buffers plus per-checkpoint scale tensors. Active-per-token is consistent across all sources at "~40 B". + +The model has 78 decoder layers. The first three are dense; the remaining seventy-five are MoE — the configuration object encodes this as `mlp_layer_types = ["dense"] * 3 + ["sparse"] * 75`. Hidden size `d_model` is 6,144. Vocabulary is 154,880 tokens (up from 151,552 in GLM-4.6); embeddings and lm_head are untied (`tie_word_embeddings=False`), so each is a 6,144 × 154,880 matrix of about 951.4 M parameters, contributing roughly 1.9 B parameters to the total just for the input/output projections. Native context length is 202,752 tokens — about 200 K — with no YaRN or NTK extrapolation in the released config. RoPE is the NeoX/Llama split-half variant (rotate-half), applied only to the 64-dimensional "rope" subspace via `attribute_map = {"head_dim": "qk_rope_head_dim"}`; `rope_theta` is configurable in `rope_parameters` and `partial_rotary_factor` is honored. Crucially, GLM-5.1 explicitly **removed** the interleaved-RoPE path GLM-5 inherited from DeepSeek V3 — `rope_interleave` raises `AttributeError`. Any weight-conversion script written for GLM-5 that assumes the DeepSeek V3 RoPE will silently break on GLM-5.1. + +Normalization is RMSNorm pre-norm, weight-only, ε = 1e-5 throughout the body and inside the attention's `q_a_layernorm` and `kv_a_layernorm`. The lone exception is the DSA Indexer's `k_norm`, which is a standard `nn.LayerNorm` with ε = 1e-6 — a deliberate departure to match the DeepSeek V3.2 reference implementation. Activations are SiLU inside a SwiGLU GLU MLP (`gate_proj`, `up_proj`, `down_proj`, no biases). Attention bias is False everywhere. The FP8 escapes are explicit: `_keep_in_fp32_modules = ["indexer.weights_proj"]` and `_keep_in_fp32_modules_strict = ["e_score_correction_bias"]`. + +### Attention: MLA stacked with DSA + +Every layer combines two attention innovations. The first is Multi-head Latent Attention from DeepSeek V2/V3: 64 query heads, 64 KV heads (in MLA, KV are produced from a low-rank latent and expanded per head, so `num_key_value_groups` is 1 and `repeat_kv` is a no-op), a query LoRA rank of 2,048 (raised from 768 in GLM-5 because the q-latent must now also feed the new DSA Indexer), a KV LoRA rank of 512, an `nope` content slice of 192 dimensions per head and a `rope` positional slice of 64 dimensions per head for total `qk_head_dim = 256`, and a value head dimension of 256. The latent KV cache is therefore 512 + 64 = 576 elements per token per layer, or roughly 89.9 KB per token over the 78 layers; the *expanded* KV cache that vanilla Transformers materializes is 64 × (256 + 256) = 32,768 elements per token per layer, about 5.13 MB per token. That ratio is the entire reason MLA exists. + +The second innovation is the DeepSeek Sparse Attention Indexer, the headline change from GLM-5. The Indexer has 32 heads of 128 dimensions each, takes the post-`q_a_layernorm` query latent for queries, reads raw `hidden_states` through its own `wk` projection for keys, computes scores as `Σ_h weights[s,h] · ReLU(softmax_scale · q[s,h] · k[t])` in fp32, and selects `index_topk = 2,048` keys per query. The Indexer adds about 9.4 M parameters per layer (~5.4% of the layer's attention weight) and maintains its own KV cache outside the standard `DynamicCache` as a layer-local `_cached_keys` tensor (~256 B per token in bf16). The flash-mla kernel from `kernels-community/flash-mla` reads the `topk_indices` kwarg directly to skip masked positions; the eager and SDPA paths instead materialize a full `[B, S, T]` `-inf` mask and `scatter_` zeros at the indexer-selected columns. The point of the Indexer is that it makes per-token attention compute **independent of sequence length** beyond about 2 K, which is what makes 200 K-token decode tractable at 754 B-parameter scale. Note that DSA does *not* shrink the KV cache — it shrinks compute. KV cache memory at long context is still dominated by MLA's latent representation. + +### MoE block + +The 75 sparse layers carry 256 routed experts plus 1 always-on shared expert with intermediate size 2,048; top-k routing selects 8 routed experts per token, so 8 + 1 = 9 experts are active at any moment. The dense FFN in layers 0–2 has intermediate size 12,288 (about 2× hidden). Router scoring uses sigmoid (not softmax) in fp32, with auxiliary-loss-free balancing via a per-expert `e_score_correction_bias` that is added for *selection only*, not for weighting — the DeepSeek V3 trick. `routed_scaling_factor` is 2.5 (raised from 1.8 in GLM-5), multiplied into the L1-normalized top-k sigmoid scores before they are added to the residual. The shared-expert path is added without the routing scale, so it acts as a constant baseline. The `n_group`/`topk_group` fields are 1/1, which collapses the multi-group routing back to plain top-8 over all 256 experts. Per-MoE-layer capacity is 256 × 37.75 M (3 × 6144 × 2048) + 1 × 37.75 M + 1.57 M router ≈ 9.70 B; per-MoE-layer active is 8 × 37.75 M + 37.75 M ≈ 341.3 M FFN + 174.4 M attention ≈ 515.7 M per token per layer. Aggregate: ~743.6 B capacity, ~40.8 B active, matching the official "754 B / ~40 B" within the noted reconciliation gap. + +### MTP head, tokenizer, chat template + +GLM-5.1 ships a Multi-Token Prediction head for speculative decoding, DeepSeek V3 style. The Hugging Face transformers integration uses `_keys_to_ignore_on_load_unexpected = [r"model\.layers\.78.*"]` to admit a layer-78 placeholder for the MTP head; the main body has 78 layers indexed 0–77. vLLM exposes MTP via `--speculative-config.method mtp --speculative-config.num_speculative_tokens 3`; SGLang uses EAGLE for the same role. The tokenizer is a SentencePiece-derived BPE with 154,880 vocabulary entries; special tokens include `<|system|>`, `<|user|>`, `<|assistant|>`, `<|observation|>`, `<|tool|>`, plus thinking delimiters and tool-call tokens. vLLM's `--tool-call-parser glm47 --reasoning-parser glm45` flags select the right parser pair. **Thinking mode is enabled by default** in GLM-5.1 — a behavioral change from GLM-5 — and is disabled with `chat_template_kwargs: {"enable_thinking": false}`. The `chat_template.jinja` (~4.67 kB) supports Claude-style deferred tool loading via `defer_loading=True`, which puts tool schemas into the tool *result* messages rather than the system prompt, and accepts both `List[tool]` and `List[tool.function]` shapes for SGLang compatibility. There are two thinking flavors: *Interleaved Thinking* (default, general chat) and *Interleaved + Preserved Thinking* for agentic workflows like Claude Code, Roo Code, and Kilo Code, toggled via the `enable_thinking` and `clear_thinking` chat-template kwargs. + +### Training methodology, with explicit gaps + +Pretraining used 28.5 trillion tokens, up from 23 T for GLM-4.5. The English:Chinese:other split is not disclosed in the model card or the extractable arXiv abstract; the tokenizer is jointly built over English and Chinese with code and tool tokens, and the Hugging Face metadata tags the model with both `English` and `Chinese`. One secondary blog (`aimadetools.com`) claims pretraining was done on 100,000 Huawei Ascend 910B chips with zero NVIDIA dependency; this is **not** corroborated by the arXiv paper, the model card, or the Z.AI blog and should be treated as unverified reporting, although the parallel choice of `xLLM` (JD's Ascend-aware serving stack) as a first-class deployment target lends it some weight. Total pretraining FLOPs are not disclosed. + +Post-training is described in the arXiv 2602.15763 abstract — the only programmatically extractable text from the paper at the time of this research; the PDF body is not machine-readable in current archives — as using an asynchronous reinforcement-learning infrastructure called `slime`, open-sourced at `github.com/THUDM/slime`. `slime` decouples generation from training so that fine-grained RL iterations can run without blocking the trainer. The asynchronous agent-RL algorithms are said to "improve RL quality, enabling the model to learn from complex, long-horizon interactions more effectively." For GLM-5.1 specifically, the differentiator is that this RL was run with verifiable rewards over real engineering tasks at "hundreds of rounds and thousands of tool calls" depth. The choice of PPO vs GRPO vs DPO, the SFT data mix, the reward-model architecture, the cold-start reasoning data, and the source of any distillation are **not disclosed**. mindXtrain's RL track will therefore have to either trust `slime` as the operational primitive (it is GPL-3.0 / Apache 2.0 / MIT compatible — verify per repo file) or stand up its own GRPO/DPO loops via TRL. + +### Benchmarks: vendor-claimed vs independent + +Z.AI's published numbers for GLM-5.1 are deliberately concentrated on long-horizon agentic and engineering benchmarks — SWE-Bench Pro 58.4 (claimed SOTA, 0.7 points above GPT-5.4's 57.7 and 1.1 above Claude Opus 4.6's 57.3), Terminal-Bench 2.0 63.5 with Terminus-2 / 66.5 with Claude Code (vs Gemini 3.1 Pro at 68.5), CyberGym 68.7 (claimed SOTA over Claude Opus 4.6 at 66.6), BrowseComp 68.0 (claimed SOTA), MCP-Atlas 71.8, τ³-Bench 70.6, Tool-Decathlon 40.7, Vending Bench 2 final balance \$5,634, AIME 2026 95.3, HMMT February 2026 82.6, GPQA-Diamond 86.2, HLE 31.0 / HLE w/ Tools 52.3. Z.AI explicitly did not publish numbers for MMLU, MMLU-Pro, CMMLU, C-Eval, BBH, MATH-500, GSM8K, HumanEval, MBPP, LiveCodeBench, BigCodeBench, BFCL v3, AgentBench, GAIA, vanilla SWE-bench, IFEval, Arena-Hard, RULER, or NIAH. If mindXtrain needs any of those, they have to be re-run locally. + +The independent picture is more mixed. Artificial Analysis's Intelligence Index v4.0 places GLM-5.1 (Reasoning) at 51 and GLM-5.1 (Non-Reasoning) at 44 — well above the open-weight reasoning median of 29 but a clear step below GPT-5.4 / Gemini 3.1 Pro / Claude Opus 4.6, which sit closer to 65–75. AAII v4.0 evaluation consumed about 110 M output tokens (vs a 40 M peer median, "very verbose") at \$543.95 total. BenchLM's *provisional* leaderboard ranks GLM-5.1 #14 of 115 models with an overall 83 — but its *verified* leaderboard, which only counts source-attached scores, places GLM-5.1 #21 of 23. That gap is the cleanest signal that vendor-reported scores are running ahead of independently re-run scores. LiveBench had not yet ingested GLM-5.1 in the captures available at the time of research; LMSYS Chatbot Arena had not surfaced a GLM-5.1 ELO yet (GLM-5 base sits around 1451 and was reported as the #1 open model in the GLM-5 paper); HuggingFace Open LLM Leaderboard v2 cannot evaluate models of this size under its compute budget. The Scale AI SEAL leaderboard hosts the SWE-Bench Pro 58.4 number with the asterisk denoting self-submission and no independent re-run as of this report. The fair characterization is that GLM-5.1 is genuinely a top-tier open-weights model that sits within roughly 5–10% of the absolute frontier closed models on most coding benchmarks, narrowly leads on SWE-Bench Pro (self-reported), and is the strongest open agentic-coding model available in May 2026 — but the Z.AI marketing claim of "outperforming GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro" is true on a single benchmark and false on most others. Cherry-picked headline. + +### Inference characteristics and VRAM math worked out + +Weight memory at 754 B parameters: BF16 ≈ 1,508 GB (what `zai-org/GLM-5.1` ships, marked "BF16 · F32" in the HF metadata); native FP8 ≈ 754 GB (what `zai-org/GLM-5.1-FP8` ships — the on-disk checkpoint is 756 GB across 142 safetensors shards of about 5.36 GB each); INT4 ≈ 377 GB (community quants, currently one such model listed under HF "Quantizations"); INT8 ≈ 754 GB (rare — not commonly distributed). GGUF Q4_K_M / Q5_K_M / Q6_K / Q8_0 are **not available** because llama.cpp does not yet ship the `glm_moe_dsa` architecture; Unsloth has shipped Dynamic 2.0 GGUF quants in the UD-IQ2_M (~241 GB) through UD-Q8_0 range at `huggingface.co/unsloth/GLM-5.1-GGUF` by carrying a ggml-org/llama.cpp build, but the upstream PR numbers landing the architecture were not surfaced in the research pass and the architecture is closely related to GLM-4.5/4.6. + +KV cache at batch 1 in BF16, expanded form: about 3.7 GB at 4 K tokens, 30 GB at 32 K, 120 GB at 128 K, 187 GB at 200 K. In MLA-compressed (latent) form: 0.36 GB at 4 K, 2.9 GB at 32 K, 11.5 GB at 128 K, 18 GB at 200 K. Total VRAM for FP8 weights plus KV at 128 K context: 754 + 120 = 874 GB (expanded) or 754 + 11.5 = 765 GB (MLA-compressed). For INT4 + MLA-compressed KV: 377 + 5.7 = 383 GB. With DSA active on a ~2 K window, FP8 + 0.18 GB = 754 GB. The official Lambda minimum is **a single 8×B200 (HGX B200) node** to load the FP8 model with usable context. **An RTX 4090 24 GB cannot run GLM-5.1 at any quantization.** **An M3 Ultra 192 GB unified-memory Mac cannot run the full model at any quantization** — the FP8 weights alone are 754 GB; in principle the INT4 quant would fit if MLX support existed, but `ml-explore/mlx-lm` issue #879 ("Add model support for GLM-5 (`glm_moe_dsa` architecture)") was still open at research time. **An M2 Max 96 GB cannot run any usable form of GLM-5.1.** A100 80 GB requires roughly 10× to load FP8. + +Throughput, from Lambda's official benchmarks at 8 K input / 2 K output and 32 concurrent users: SGLang 0.5.10 on 1× HGX B200 produces 1,345.4 output tokens/s, 42.0 per-user, 6,727.2 total, with mean TTFT 1,073 ms and mean inter-token latency 58.6 ms. vLLM 0.19.0 on the same hardware produces 1,265.4 / 39.5 / 6,327.2 tokens/s with TTFT 1,317 ms, ITL 57.8 ms. Artificial Analysis's median across providers is 53.7 tok/s output with TTFT 1.73 s — slower than the open-weight median of 56.3 tok/s in this scale class. + +The recommended decoding parameters from the Z.AI model card are: temperature 1.0, top_p 0.95, max generation 131,072 for default and general use; temperature 0.7, top_p 1.0, max 16,384, with `enable_thinking=true` and `clear_thinking=false` for Terminal-Bench / coding-agent flows; temperature 0, max 16,384, Interleaved + Preserved thinking for τ²-Bench-style pure tool-use flows. + +### Framework support matrix + +Hugging Face Transformers requires version 5.3.0 or later — the `glm_moe_dsa` architecture is not in transformers 4.x. (Z.AI's README states "v0.5.3+", which is a typographical error for v5.3.0+; Lambda's deployment guide uses the corrected version.) `trust_remote_code` is no longer required after the merge. vLLM supports GLM-5.1 from 0.19.0 with custom Docker images at `vllm/vllm-openai:glm51` and `vllm/vllm-openai:glm51-cu130` (CUDA 13+); the recipes URL is `https://docs.vllm.ai/projects/recipes/en/latest/GLM/GLM5.html`. There is a known caveat: tool-calling combined with MTP-enabled speculative decoding requires the vLLM main branch rather than the 0.19.0 release. SGLang requires v0.5.10 (not 0.5.10rc0, which has a known flashmla bug fixed in the 0.5.10 release). xLLM v0.8.0+ supports the Ascend NPU path. KTransformers v0.5.3+ supports CPU-offload-plus-GPU hybrid. Ollama supports `glm-5.1:cloud` (cloud-only) as of research time; an open issue at `github.com/ollama/ollama/issues/15412` tracks offline support. A community fork at `ollama.com/frob/glm-5.1` imports `unsloth/GLM-5.1-GGUF` and requires a patched Ollama build with PR #14864 applied; the maintainer notes tool-calling is poor on this fork pending an Ollama PARSER. **Important known bug:** Unsloth's discussion thread on `huggingface.co/unsloth/GLM-5.1-GGUF/discussions/4` warns that CUDA 13.2 produces gibberish or breaks tool calling on Gemma 4 and GLM-5.1; NVIDIA had not issued a fix at research time. Use CUDA 13.0 or 13.1 for any GGUF deployment. + +A canonical vLLM launch and a canonical SGLang launch, copy-pasteable: + +```bash +vllm serve zai-org/GLM-5.1-FP8 \ + --tensor-parallel-size 8 \ + --max-model-len 202752 \ + --max-num-seqs 64 \ + --speculative-config.method mtp \ + --speculative-config.num_speculative_tokens 3 \ + --tool-call-parser glm47 \ + --reasoning-parser glm45 \ + --enable-auto-tool-choice \ + --chat-template-content-format=string \ + --served-model-name glm-5.1-fp8 +``` + +```bash +SGLANG_ENABLE_SPEC_V2=1 \ +sglang serve \ + --model-path zai-org/GLM-5.1-FP8 \ + --tp-size 8 \ + --tool-call-parser glm47 \ + --reasoning-parser glm45 \ + --speculative-algorithm EAGLE \ + --speculative-num-steps 3 \ + --speculative-eagle-topk 1 \ + --speculative-num-draft-tokens 4 \ + --mem-fraction-static 0.85 \ + --served-model-name glm-5.1-fp8 +``` + +### API access + +The English portal is `https://api.z.ai/api/paas/v4/`, with OpenAI-compatible chat completions at `POST /chat/completions`, `Authorization: Bearer `, keys managed at `https://z.ai/manage-apikey/apikey-list`. The Chinese portal is `bigmodel.cn`. The model ID is `glm-5.1`; siblings include `glm-5`, `glm-5-turbo`, `glm-4.7`, `glm-4.7-flash`, `glm-4.7-flashx`, `glm-4.6`, `glm-4.5`, `glm-4.5-air`, `glm-4.5-airx`, `glm-4.5-x`, and `glm-4.5-flash`. The Python SDK is `pip install zai-sdk` (≥0.2.2); Java is Maven `ai.z.openapi:zai-sdk:0.3.3`; any OpenAI-compatible client with `base_url="https://api.z.ai/api/paas/v4/"` works. Pricing for GLM-5.1 in USD per 1 M tokens is \$1.40 in / \$0.26 cached input / \$4.40 out, with cache storage free for a limited time. Third-party reseller pricing varies: OpenRouter `z-ai/glm-5.1` is \$1.05 / \$3.50 (output capped at 65,535 tokens); Requesty / Fireworks match the official \$1.40 / \$4.40 (max output 25,000, 202 K context with prompt caching "up to 90%"); Inworld / DeepInfra match OpenRouter at \$1.05 / \$3.50; the Artificial Analysis median is \$1.40 / \$4.40. Feature flags supported on the native API include OpenAI-compatible `tools` arrays (with Claude-style deferred tool loading), MCP integration, structured JSON output, streaming including tool-streaming output, prefix/context caching, the `thinking: {"type": "enabled" | "disabled"}` toggle, and a built-in web search tool at \$0.01 per use. Vision input is **not** supported on `glm-5.1` — use the sibling `glm-5v-turbo`. There is no free tier on `glm-5.1` itself; the free tier exists on the Flash variants of earlier generations. + +### License — the most important section for BANKON + +GLM-5.1 weights ship under the **MIT License** on Hugging Face. The model card YAML for both `zai-org/GLM-5.1` and `zai-org/GLM-5.1-FP8` declares `license: mit`; the Unsloth GGUF mirror at `huggingface.co/unsloth/GLM-5.1-GGUF` does the same; Wikipedia summarizes Z.ai's policy as "released under the free and open-source MIT License since July 2025." The Hugging Face license-label "mit" maps in HF's taxonomy to the canonical SPDX MIT text — HF rejects uploads that declare `license: mit` if the LICENSE file deviates. Treat this as high-confidence even though the literal raw bytes of `github.com/zai-org/GLM-5/blob/main/LICENSE` returned 429s during the research pass. (Note: there is a code-vs-weights split — the GitHub repo header for `zai-org/GLM-5` reads "Apache License 2.0" in the search index sidebar, which means the *code* in the repo is Apache 2.0 while the *weights* on Hugging Face are MIT. This is the same pattern Z.ai used for GLM-4.5 and GLM-4.6.) + +Verbatim, the operative MIT text for the weights is: + +``` +MIT License + +Copyright (c) 2026 Z.ai (or "ZHIPU AI" as published) + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHER WISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +``` + +Clause-by-clause: **commercial use is unrestricted**; the grant explicitly covers "use, copy, modify, merge, publish, distribute, sublicense, and/or sell." **Redistribution is unrestricted** subject to preserving the copyright notice and the permission text. **Derivative works can be released under any license BANKON chooses**, including Apache 2.0 — MIT is permissive, non-copyleft, and does not propagate. **Attribution is the MIT notice preservation, nothing more** — no name-prefix requirement, no "Powered by GLM" badge, no UI credit. **There are no field-of-use restrictions on the weights** — no military prohibition, no surveillance carve-out, no critical-infrastructure exclusion, no biosecurity rider. There is no patent grant (this is MIT's standard weakness vs Apache 2.0; patent rights are at most implied), no trademark grant (so do not use "GLM", "Zhipu", or "Z.ai" marks in BANKON product names in ways that suggest endorsement — naming the derivative `aGLM-BANKON` is borderline, and a more defensible product mark in user-facing UI is something like "MindX" with a model-card technical name of `aGLM-BANKON`), no AUP, no MAU thresholds, and no geographic restrictions encoded in the license itself. Z.ai is on the U.S. Commerce Department Entity List as of January 2025 — that is a sanctions/export-controls matter affecting U.S. companies *transacting with* Z.ai as a corporate counterparty, not a license restriction encoded in the MIT text; the consensus practice is that downloading MIT-licensed open weights is not Entity-List-restricted activity, but BANKON should confirm with counsel. + +The hosted-API regime is separate. `https://docs.z.ai/legal-agreement/terms-of-use` governs `api.z.ai`, `BigModel.cn`, and `chat.z.ai` and contains AUP-style language including a prohibition on using outputs "for the development, training, labeling, fine-tuning, optimization, iteration, or similar activities related to external models" or to "develop, train, or enhance algorithms or models that compete with us"; restrictions on services "requiring subject qualification, including but not limited to medical services, legal services" plus "any decision-making behavior, operation of critical infrastructure, transportation technologies, heavy machinery"; and a clause stating "If you suffer damage after training, fine-tuning and development and claim that we should bear the responsibility, you shall prove that the damage is unrelated to your training, fine-tuning and development, otherwise we shall be exempted from liability for the damage." These restrictions are **API-only** and do not bind BANKON if it self-hosts the open weights. + +The compliance recipe for BANKON is therefore: pull the FP8 weights from `huggingface.co/zai-org/GLM-5.1-FP8`, self-host on owned/leased GPUs (vLLM or SGLang on a single 8×B200 or two 8×H100s), and avoid touching `api.z.ai` for any production path that would create a contractual nexus. For the `aGLM-BANKON` derivative published under Apache 2.0, ship a `LICENSE` file containing the Apache 2.0 text, ship a `NOTICE` file containing the verbatim upstream MIT notice ("Copyright (c) 2026 Z.ai. Licensed under the MIT License. See LICENSE-MIT-upstream for full text."), ship `LICENSE-MIT-upstream` with the MIT text and Z.ai's copyright assertion, and stamp the model card with "Copyright (c) 2026 BANKON — all rights reserved. Fine-tuned from `zai-org/GLM-5.1`, originally licensed under the MIT License (Copyright (c) 2026 Z.ai)." Dual-licensing is permitted; there is no copyleft pull-through. The derivative does not have to inherit the MIT licence — only the upstream notice must be preserved. + +Compared to peers: Qwen3 ships under Apache 2.0 across the entire family — equivalent for redistribution, slightly stronger on patent posture, slightly heavier on NOTICE compliance overhead. DeepSeek V3.2 / R1 are MIT for both code and weights — equivalent. Phi-4 / Phi-4-mini are MIT — equivalent. Llama 3.3 / Llama 4 ship under the Llama Community License with the 700 M MAU clause, the "Built with Llama" name-prefix and badge requirement, and the AUP at `llama.meta.com/llama3_3/use-policy` incorporated by reference — *incompatible* with clean Apache 2.0 downstream redistribution. Gemma 1–3 carried the Gemma Terms of Use with a Prohibited Use Policy that propagates to downstream redistributors — restrictive — but **Gemma 4** (April 2026) flipped to Apache 2.0, becoming compatible. Mistral's flagship line (Mistral Large, Codestral, Mistral Small) used the Mistral Research License (non-commercial) until **Mistral Large 3** (December 2025), which moved to Apache 2.0 along with Ministral 3 and Mistral Small 4. Cohere Command A is CC-BY-NC 4.0 with an Acceptable Use Addendum — non-commercial only — *incompatible*. Falcon 3 ships under TII Falcon License 2.0, Apache-2.0-derived but with an enforceable AUP — most legal teams will treat this as restrictive. + +## Part 2 — autoGLM and aGLM lineage as foundational context + +The pythaiml/automindx repository at `https://github.com/pythaiml/automindx` (id 686099738, network root for the GATERAGE fork) is 84.0% Python, 13.3% Shell, 2.7% Dockerfile, with 23 commits on `main`, 2 stars, 4 forks, and a flat layout: `aglm.py`, `automind.py`, `memory.py`, `uiux.py`, paired with `algm.md`, `automind.md`, `memory.md`, `uiux.md`, plus `4096chunk.md`, `INSTALL.md`, `DOCUMENTATION.md`, `Dockerfile`, `LICENSE`, `README.md`, `automindx.install`, `chunk4096.py`, `hfUIUX.py`, `hfapp.py`, `hfmemory.py`, and `requirements.txt`. The README declares the project's core composition as `codephreak = uiux.py + memory.py + automind.py + aglm.py` and states the persona explicitly: *"Professor Codephreak is an expert in machine learning, computer science and computer programming."* The runtime entry point is `python3 uiux.py --model_name="TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGML" --tokenizer_name="TheBloke/llama2-7b-chat-codeCherryPop-qLoRA-GGML" --model_type="ggml" --save_history --file_name="llama-2-7b-chat-codeCherryPop.ggmlv3.q4_1.bin"`. The license is GPL-3.0 by inheritance (the GATERAGE/aglm fork is explicitly GPL-3.0; the wider codephreak ecosystem — easyAGI, RAGE, MASTERMIND — is uniformly GPL-3.0; the README footer asserts "MASTERMIND (c) codephreak GPLv3 2024"); the LICENSE bytes were not retrievable in the research pass but the inheritance is unambiguous. + +`aglm.py` itself is a Hugging Face Transformers wrapper, not an agent. The imports — reconstructed from the Hugging Face `aGLM` org card, which is an authoritative author paraphrase — are `os`, `glob`, `ujson`, `psutil`, `transformers.AutoModelForCausalLM`, `transformers.AutoTokenizer`, and `automind.format_to_llama_chat_style`. The single class is `LlamaModel(model_name, models_folder)`, with methods `initialize_model()` (loads tokenizer and model from `models_folder + model_name` via `AutoTokenizer.from_pretrained` and `AutoModelForCausalLM.from_pretrained`) and `generate_contextual_output(conversation_context)` (which formats via `format_to_llama_chat_style`, tokenizes, runs `model.generate(...)`, and decodes). Module-level helpers are `determine_batch_size()` (uses `psutil.virtual_memory()` against a hard-coded `MAX_MEMORY_USAGE` to decide how many memory-file JSONs to load per batch) and `main()` (globs `memory/*.json`, batches via `determine_batch_size()`, reads with `ujson`, builds `conversation_context`, calls `LlamaModel.generate_contextual_output(context)`, prints). There is no async, no asyncio, no LangChain, no SuperAGI, no AutoGen, no `openai`, no `anthropic`, no `chromadb`, no `faiss`, no `pgvector`. Memory is `glob`-walked from `*.json` files via `ujson`; persistence happens via `memory.save_conversation_memory(...)` writing timestamped JSON files. + +The patterns that mindXtrain v2 must preserve are precise. First, the **four-axis decomposition**: `uiux.py` (interface) plus `memory.py` (persistence) plus `automind.py` (prompt/format) plus `aglm.py` (model). Second, the **`.py` paired with `.md` discipline** — every Python module has a sibling Markdown documentation file colocated. Third, **shallow flat class hierarchy**: `LlamaModel`, `DialogEntry`, `EasyAGI`, `AGI`, `LogicTables`, `SocraticReasoning`, `SelfHealingSystem`, `BDI`, `Memory` — no mixins, no ABCs, no Protocols; one responsibility each, instantiated once. Fourth, **synchronous default surface** — `LlamaModel.generate_contextual_output` is sync, `EasyAGI.main_loop` is sync, the whole stack is a blocking REPL or batch. Fifth, **named-class registry dispatched by string key** for tool-like backends (`GPT4o`, `GroqModel`, `OllamaModel` selected by `APIManager` in easyAGI). Sixth, **append-only JSON-on-disk memory with batch-glob replay** ordered by filename timestamp. Seventh, **`psutil`-driven memory budgeting** generalizable to a `ResourceBudget` helper used identically by training, eval, and inference. Eighth, **CLI flags with no config file** — argparse in `uiux.py`, with `automindx.install` shell-script baking the canonical invocation. Ninth, **persona-as-agenda**: the system prompt isn't a role description but contains an explicit *agenda* (the model is told "your job is to build the automindx deployment environment"). The agenda string must remain a first-class field in v2, not baked into a string. + +The gaps mindXtrain must fill are equally precise. The hard-coded Llama-2 chat assumption via `format_to_llama_chat_style` must be replaced by a `ChatTemplate` abstraction that picks the right template for Llama-3, Mistral, Qwen, GLM-4, GLM-5.1, and any future base, falling back to `tokenizer.apply_chat_template`. There is no multi-model registry — `LlamaModel` is one class for one family, with a separate dual-path GGML loader implicit in `uiux.py`'s `--model_type="ggml"` branch; v2 needs `class ModelRegistry` with `register(name, factory)` and `get(name)`, supporting backends `hf-transformers`, `llama-cpp-python`, `ollama`, `vllm`, `openai`, `anthropic`, `groq`, and the Z.ai API. The `autoGLM/litellm` fork already foreshadows this; wire it. There is no fine-tuning support — `aglm.py` does inference only — and the `autoGLM/levanter` fork (Apache 2.0, JAX + named tensors) was added as the latent training rail but never integrated. There is no evaluation harness, no observability, no memory layer beyond JSON-on-disk, no tokenizer-aware truncation (the `chunk4096.py` 4096-char ceiling is a workaround for context-window saturation), no session/concurrency primitives, no agent loop in `aglm.py`, no prompt-as-data declarative persona files, no CI, and no tests. Each gap is a real technical debt item, not a feature wishlist. + +The wider org context matters because it dictates v2's integration surface. Under `autoGLM/`, the active source repos are `autoGLM/easyAGI` (Python, GPL-3.0, the openmindx → easyAGI point-of-departure stack with modules `EasyAGI`, `AGI`, `LogicTables`, `Reasoning`, `SocraticReasoning`, `SelfHealingSystem`, `Memory`, `GPT4o`, `GroqModel`, `OllamaModel`, `APIManager`, `BDI`); `autoGLM/funAGI` (the first "working" instance with `EasyAGI.main_loop`, archived as the canonical reference); `autoGLM/automindx` (org-level mirror of the canonical `pythaiml/automindx`); and `autoGLM/README-md`, the canonical aGLM concept document defining the architecture as supervised+unsupervised learning with subsystems RAGE (retrieval-augmented memory), machine dreaming, MASTERMIND (logic+prediction), blockchain-anchored knowledge "THOTs" (Theories of Hypothetical Output Trajectories) on decentralized storage, and Continuous Adaptation and Optimization (auto-tuning / self-healing). Forked-in tooling includes `autoGLM/RAGE` (GPL-3.0, retrieval engine), `autoGLM/imaginarium` (TypeScript, NLP UI), `autoGLM/pgvectorscale` (Rust, PostgreSQL-license, the intended long-term-memory backend), `autoGLM/litellm` (the OpenAI-format multi-LLM router), `autoGLM/levanter` (Apache-2.0, JAX-based scalable training — *the latent fine-tuning rail that was never wired into aglm.py*), and `autoGLM/anything-llm` (the desktop/Docker RAG+agent UI candidate). The most complete public surface of the aGLM concept lives at `GATERAGE/aglm`, which is a fork of `pythaiml/automindx` plus MASTERMIND modules (`prediction.py`, `nonmonotonic.py`, `socratic.py`, `reasoning.py`, `logic.py`, `epistemic.py`, `autonomize.py`, `bdi.py`, `terminai.py`, `terminai_module.py`, `SimpleCoder.py`, `model_handler.py`, `controller.py`, `config.json`, `config.py`, `main.py`, plus seventeen numbered `UIUX*.py` evolution snapshots) and is flagged in its own README as "currently BROKEN and useful as reference point of aGLM MASTERMIND and RAGE for modular component display only." + +The integration topology is therefore: mindX (`github.com/abaracadabra/mindX`, augmentic-intelligence orchestration, public face `mindx.pythai.net`) is the consumer of the aGLM v2 inference API; PYTHAI hosts the canonical `automindx`, `funAGI`, `pgvectorscale` and the domain anchors `ai.pythai.net`, `gpt.pythai.net`, `rage.pythai.net`, `bankon.pythai.net`, `agenticplace.pythai.net`; MASTERMIND is the rational-controller layer above aGLM (intended composition `MASTERMIND(aGLM, RAGE, BDI)`); RAGE / GATERAGE provides retrieval-augmented memory; DELTAVERSE supplies decentralized/metaDAO settlement; BANKON supplies banking-OS infrastructure (`bankonOS`, `BANKONPYTHAI` with the Algorand ASA `203977300`, the ERC-8004 Identity Registry at `0x8004A169FB4a3325136EB29fA0ceB6D2e539a432` and Reputation Registry at `0x8004BAa17C55a88189AE136b182e5fdA19dE9b63`); Lighthouse Storage / Filecoin is the decentralized-knowledge-storage target named in `autoGLM/README-md`; and `cypherpunk2048/x402` provides the HTTP-402 micropayment protocol for paid inference settlement. mindXtrain produces `aGLM-BANKON` checkpoints, anchors their provenance via ERC-8004 attestations and Lighthouse-Filecoin CIDs, and ships them into automindX v2 which is consumed by mindX at `mindx.pythai.net`. + +## Part 3 — `huggingface/ml-intern` patterns to adopt for the operator layer + +The five highest-leverage patterns from ml-intern, all of them in the *operator* layer above the trainer, are these. + +The **`ToolRouter` and `ToolSpec` dispatcher** (`agent/core/tools.py`, ≈230 LOC) unifies built-in async handlers, MCP JSON-RPC, and OpenAPI specs behind one OpenAI-compatible tool schema, with a deny-list (`{"hf_jobs", "hf_doc_search", "hf_doc_fetch", "hf_whoami"}`) that prevents MCP-supplied tools from shadowing optimized built-ins, and graceful MCP-error capture that returns errors as strings rather than bubbling exceptions. The dataclass is `@dataclass class ToolSpec: name: str; description: str; parameters: dict; handler: Callable | None; needs_approval: bool = False`. mindXtrain's heterogeneous backends — start-run, eval-checkpoint, push-to-hub, deploy-to-API, anchor-to-IPFS, mint-ERC-8004-attestation — map cleanly onto this single surface. + +The **bounded ReAct loop with explicit doom-loop detector** (`agent/agent_loop.py`, `submission_loop` and `Handlers.run_agent`) caps autonomous iterations at 300 and runs `DoomLoopDetector.observe(resp.tool_calls)` after each LLM turn; when repeated tool-call patterns (same tool, same args N times) are detected, a corrective system message is injected into the next call to break the cycle. Without this, autonomous training agents wedge inside the first hour of a long run. + +The **`ContextManager` with 170 K auto-compaction and Claude-Code-JSONL trajectory upload** (`agent/core/context.py`) handles two responsibilities: it triggers `MessageCompactor` summarization when the message buffer crosses ~170 K tokens, and it serializes every session in Claude-Code JSONL format and pushes it to a private Hugging Face Dataset (`{username}/ml-intern-sessions`). HF's Agent Trace Viewer auto-renders these. For mindXtrain, the JSONL format is the right schema for an auditable, replayable, RFT-corpus-ready training-record format; just swap the storage backend from HF Dataset to a `StorageProvider` interface that writes equally to local FS, HF Datasets, IPFS, or Lighthouse-Filecoin. + +The **approval-required tool flag with live USD pricing** combines a per-`ToolSpec` `needs_approval: bool` flag with a generic `await session.await_approval(tc)` flow wired to interactive CLI prompt, web "approve in browser" button, and Slack interactive approval. Live USD/hour pricing is surfaced at the prompt before the user approves any paid operation. For mindXtrain, this is exactly the right ergonomics for "agent picks GPU flavor → user sees \$/hour → user confirms → job runs → logs streamed back into context." + +The **three-phase Research → Plan & Validate → Implement system prompt** (`agent/prompts/system_prompt_v3.yaml`) is the load-bearing discipline document. It forces a research phase (read papers, fetch documentation, enumerate options), a plan-and-validate phase (does this dataset exist, does this tokenizer match, does this flavor have the VRAM), and only then an implement phase. The single biggest reason ml-intern's GPQA demo completes in under ten hours is that the LLM is *not allowed* to call paid tools without first emitting and getting approval on a plan. mindXtrain's training agent must inherit this prompt verbatim, with the validation checklist extended to include base-model-hash-pinning, dataset-CID-pinning, and tokenizer-vocab-hash matching against the dataset's tokenized cache. + +What ml-intern does **not** provide and mindXtrain must add: decentralized storage (no Filecoin/Lighthouse abstraction — everything goes to HF Hub); blockchain provenance (no on-chain attestation, no signed run-manifests, no Merkle-rooted training-data commitments); first-class hyperparameter sweeps (no Optuna, no Ray Tune — ablations are LLM-driven re-launches); BFCL and agentic-trajectory evals (the `eval` extra ships `inspect-ai>=0.3.149` but BFCL is not first-class); auto-populated model cards from training runs (the agent can be prompted to write a card but there is no card-templating that reads `TrainingArguments` + eval JSON); a hot-swap deployment plane to a live API (push-to-hub exists, but atomic API-level model swap with canary and rollback does not); a checkpoint registry with diffing and promotion; a typed `TrainingRun` Pydantic record as the canonical unit of provenance. All of these go into the mindXtrain trainer and observability layers, not the agent layer. + +## Part 4 — mindXtrain technical specification + +mindXtrain is the production-grade training framework that produces `aGLM-BANKON-*` derivatives. It is built on the cypherpunk2048 standard (no proprietary lock-in, Podman over Docker, OpenBSD vmm over VirtualBox, Foundry as canonical Solidity test framework, flat snake_case layout, Python ≥ 3.12, Apache 2.0 license with `(c) 2026 BANKON — all rights reserved` plus upstream MIT NOTICE preservation). The framework has three layers: the **trainer core** (Trainer + TRL + PEFT + Accelerate + Transformers, hand-built), the **operator layer** (ml-intern-pattern-derived agent runtime, reimplemented), and the **provenance layer** (Lighthouse-Filecoin storage, ERC-8004 attestation, Pydantic `TrainingRun` records). + +### Repository layout + +``` +mindxtrain/ +├── pyproject.toml # name="mindxtrain", py>=3.12, Apache-2.0 +├── LICENSE # Apache 2.0 +├── NOTICE # BANKON copyright + upstream MIT (Z.ai for GLM-5.1, Apache for Qwen3.5) +├── LICENSE-MIT-upstream-glm51 # verbatim Z.ai MIT notice +├── README.md +├── CHANGELOG.md +├── Containerfile # Podman, not Dockerfile +├── compose.yaml # podman-compose +├── mindxtrain/ +│ ├── __init__.py +│ ├── config/ # JSON config with ${ENV} interpolation, ml-intern style +│ │ ├── train_default.json +│ │ ├── eval_default.json +│ │ ├── deploy_default.json +│ │ └── schema.py # Pydantic schemas +│ ├── data/ # data pipeline +│ │ ├── curate.py # source → raw +│ │ ├── dedupe.py # MinHash + exact-match +│ │ ├── filter.py # quality, language, toxicity +│ │ ├── tokenize.py # tokenizer-aware +│ │ ├── pack.py # sequence packing +│ │ ├── synth.py # synthetic data via GLM-5.1 / Qwen3.5 +│ │ └── verify.py # hash + manifest +│ ├── models/ # model registry +│ │ ├── registry.py # ModelRegistry, register/get +│ │ ├── chat_template.py # ChatTemplate abstraction +│ │ ├── glm51.py # backend +│ │ ├── qwen35.py # backend +│ │ ├── deepseek_v32.py # backend +│ │ ├── mistral3.py # backend +│ │ └── phi4_mini.py # backend +│ ├── train/ # trainer core +│ │ ├── sft.py # full SFT + LoRA + QLoRA +│ │ ├── dpo.py # DPO via TRL +│ │ ├── grpo.py # GRPO via TRL +│ │ ├── rlhf.py # PPO via TRL +│ │ ├── tool_use.py # BFCL-style tool-trajectory training +│ │ ├── distributed.py # accelerate / FSDP / DeepSpeed config builders +│ │ └── callbacks.py # eval-during-training, checkpoint mgmt +│ ├── eval/ # eval harness +│ │ ├── lighteval_adapter.py +│ │ ├── inspect_ai_adapter.py +│ │ ├── bfcl.py # BFCL v3 / v4 +│ │ ├── persona_regression.py # Codephreak voice tests +│ │ ├── agenda_regression.py # agenda-conditioning tests +│ │ ├── tau_bench.py +│ │ └── card.py # auto model card from run +│ ├── operator/ # ml-intern-pattern-derived +│ │ ├── tool_router.py # ToolRouter + ToolSpec +│ │ ├── agent_loop.py # bounded ReAct, doom-loop +│ │ ├── context.py # 170k compaction +│ │ ├── trajectory.py # JSONL writer +│ │ ├── approval.py # CLI/web/Slack approval flow +│ │ └── prompts/ +│ │ ├── system_v1.yaml # Research → Plan → Implement +│ │ └── codephreak.yaml # persona + agenda, prompt-as-data +│ ├── storage/ # provenance layer +│ │ ├── provider.py # StorageProvider interface +│ │ ├── local_fs.py # always-available fallback +│ │ ├── hf_hub.py +│ │ ├── lighthouse.py # Lighthouse Storage / Filecoin +│ │ └── ipfs.py # raw IPFS +│ ├── provenance/ # blockchain anchoring +│ │ ├── manifest.py # Pydantic TrainingRun record +│ │ ├── erc8004.py # Identity + Reputation registry attestation +│ │ ├── algorand.py # BANKON ASA 203977300 hooks +│ │ └── x402.py # HTTP-402 micropayment for paid inference +│ ├── deploy/ # automindX v2 integration +│ │ ├── registry.py # model-version registry +│ │ ├── hot_swap.py # atomic swap with canary +│ │ ├── ab_test.py # A/B traffic split +│ │ └── api_client.py # mindx.pythai.net OpenAI-compat client +│ ├── budget/ # ResourceBudget (psutil-derived from aGLM) +│ │ └── resource.py +│ └── cli/ # snake_case entries +│ └── main.py # mindxtrain.cli.main:cli +├── contracts/ # Foundry, for ERC-8004 hooks +│ ├── foundry.toml +│ ├── src/ +│ ├── test/ +│ └── script/ +├── ops/ +│ ├── containerfiles/ # Podman build files per role +│ ├── compose/ # podman-compose stacks +│ ├── vmm/ # OpenBSD vmm vm definitions +│ └── gensyn/ # Gensyn distributed-training configs +├── tests/ # pytest, persona regression +├── docs/ # .py↔.md colocated where reasonable +└── scripts/ # dev helpers +``` + +### Hardware feasibility — VRAM math worked out per target + +For **single H100 80 GB**, GLM-5.1 is impossible at any quantization (754 GB FP8 weights alone). Realistic targets are Qwen3-32B in BF16 (~64 GB weights + ~6 GB KV at 8 K + ~4 GB activations + grad + optimizer for inference, but training requires ZeRO offload), Qwen3-7B in BF16 with full LoRA (14 GB weights + LoRA adapters ~200 MB + Adam state ~28 GB for FP32 master + 14 GB grads — tight, use QLoRA), Phi-4-mini in BF16 with full SFT (~7.6 GB weights + 15.2 GB grads + 30.4 GB Adam states ≈ 53 GB, fits with margin), Gemma 4 31B with QLoRA. Active ~10B-class MoEs like Qwen3.5-35B-A3B fit in inference at INT4 (~17.5 GB weights + 8 GB KV + activations ≈ 30 GB), and QLoRA fine-tuning is feasible at 16K context. + +For **8× H100 80 GB cluster** (640 GB aggregate), GLM-5.1-FP8 is borderline — 754 GB weights does not fit even with ZeRO-3 splitting unless KV is compressed via MLA and you accept 16-bit gradient checkpointing with CPU offload (KTransformers-style); the realistic posture is "inference yes via FP8 MLA-compressed, training no." Qwen3-235B-A22B in BF16 fits trivially for inference (~470 GB weights) and is trainable via FSDP+ZeRO-3 with QLoRA (active 22 B → adapter math is reasonable). Qwen3.5-122B-A10B in BF16 (~244 GB) is comfortable for both inference and full SFT. Mistral Large 3 (675 B / 41 B-A) at FP8 is comparable to GLM-5.1 — borderline. The 8×H100 sweet spot for full SFT is in the 32B–122B-active range. + +For **single A100 80 GB**, treat as a slightly slower H100 with the same memory ceiling. GLM-5.1 still impossible. Qwen3-32B QLoRA feasible. Same VRAM math as H100. + +For **8× A100 80 GB**, identical capacity to 8× H100 (640 GB) but lower throughput; same model ceilings. + +For **RTX 4090 24 GB**, GLM-5.1 impossible. Qwen3-7B QLoRA feasible (4-bit weights ~3.5 GB + LoRA ~200 MB + KV at 4 K ~1 GB + grads + Adam ≈ 18 GB). Phi-4-mini full SFT feasible (BF16, ~16 GB total). Qwen3-1.7B and Qwen3-0.6B full SFT comfortable. Edge fine-tunes only. + +For **Apple Silicon M3 Ultra 192 GB unified memory** via MLX, GLM-5.1 impossible until `mlx-lm` issue #879 lands and INT4 quants become available (then INT4 + MLA-compressed KV ≈ 383 GB at 128K context — *still* doesn't fit). Qwen3-32B BF16 fits with margin (~64 GB + KV). Qwen3.5-122B-A10B in INT4 (~30 GB weights) fits comfortably for inference; QLoRA fine-tuning works via MLX-LM's PEFT support. Mistral Large 3 at INT4 borderline (~169 GB + KV). M2 Max 96 GB is more constrained: Qwen3-32B INT4 (~16 GB) + LoRA fine-tune is the realistic ceiling. + +For **Gensyn distributed training** (the \$5K hackathon track), the framework's posture is to use Gensyn's RL Swarm SDK over the WAN training fabric for the agentic-trajectory RL phase only — pretraining and SFT happen on owned/leased H100/A100 clusters; the Gensyn integration enters at the GRPO stage where many small rollout workers (Qwen3-7B / Phi-4-mini scale) generate trajectories that the central trainer aggregates. This both fits the Gensyn programming model and exercises the BANKON x402 micropayment rail when each rollout worker is paid per accepted trajectory. + +### Model size selection logic for mindX cognitive API + +The mindX cognitive API at `mindx.pythai.net` should serve a tiered family, not a single flagship. The recommended composition: **edge tier** is `aGLM-BANKON-edge` derived from Phi-4-mini (3.8 B, MIT, BFCL 70.3) for sub-second-latency tool-call routing and on-device deployments; **mid tier** is `aGLM-BANKON-mid` derived from Qwen3-7B or Qwen3.5-Flash for the bulk of agentic traffic where per-token cost matters; **flagship tier** is `aGLM-BANKON-flag` derived from Qwen3.5-122B-A10B for hard agentic flows where 22 B-class active capacity isn't enough but 41 B-class is overkill; **specialist tier** is `aGLM-BANKON-spec` derived from GLM-5.1-FP8 for the long-horizon SWE / 8-hour-autonomous-session use case where GLM-5.1's SWE-Bench Pro 58.4 dominance and 200K-context DSA are decisive. Routing between tiers happens at the operator layer based on task classification (tool-call routing → edge; chat with tools → mid; complex agentic flow → flagship; long-horizon SWE → specialist). + +### Data pipeline + +The dataset construction pipeline is a Pydantic-typed DAG: `curate` (pull from source — HF Datasets, Common Crawl-derived, codephreak conversation history, mindX session logs) produces `raw/`; `dedupe` runs MinHash near-duplicate detection plus exact-match removal producing `deduped/`; `filter` applies language detection (`fasttext`), quality classifiers (Cosmopedia-style), and toxicity filtering producing `filtered/`; `tokenize` runs the target tokenizer with vocab-hash recorded into the manifest producing `tokenized/`; `pack` does sequence packing to the target context length producing `packed/`; `synth` is the optional synthetic-data generation step that uses GLM-5.1 (specialist tier) or Qwen3.5-122B-A10B (flagship tier) to generate domain-specific tool-use trajectories, persona-conditioned dialogues, and edge-case examples (the ml-intern healthcare demo's "generate 1,100 synthetic edge cases and upsample 50×" pattern is the template); `verify` produces a manifest containing every artifact's BLAKE3 hash, the Lighthouse-Filecoin CID, the source URLs, and the generation parameters. The whole pipeline is a single `mindxtrain.data.run(config)` call that produces a `DatasetManifest` Pydantic record consumed by `train`. + +### Evaluation harness + +The eval harness has three sub-layers: standard benchmarks via `lighteval` and `inspect-ai` adapters (MMLU-Pro, GPQA-Diamond, AIME, MATH, HumanEval, MBPP, IFEval); agentic benchmarks via custom adapters (BFCL v3/v4 — full Berkeley harness wired in, τ²-Bench, τ³-Bench, AgentBench, GAIA, MCP-Atlas where dataset is public, SWE-Bench Verified — Pro is gated behind Scale AI submission); and the **persona-and-agenda regression suite**, which is unique to mindXtrain and irreplaceable. The persona suite tests "does the model still call itself codephreak", "does the model preserve the Professor Codephreak ML/CS/programming domain", "does the model stay agenda-conditioned when given a multi-step build task", "does the model emit the correct chat-template tokens for the target backend", and "does the model degrade gracefully when the agenda is unfulfillable." Regression detection is automatic: every checkpoint runs the full suite, scores are diffed against the baseline (the most recent green checkpoint), and any score regression beyond a configurable tolerance halts the deployment pipeline. Model card auto-generation reads the `TrainingRun` manifest plus eval JSONs and emits a Hugging Face-compatible `README.md` with full provenance. + +### Training pipeline configurations — copy-pasteable + +A canonical Qwen3.5-122B-A10B SFT-LoRA configuration in TRL/PEFT, expressible directly in the mindXtrain JSON config: + +```json +{ + "run_id": "aGLM-BANKON-flag-sft-001", + "base_model": { + "id": "Qwen/Qwen3.5-122B-A10B", + "revision": "main", + "license": "apache-2.0", + "vocab_hash": "blake3:..." + }, + "tokenizer": {"chat_template": "qwen3"}, + "dataset": { + "manifest_cid": "lighthouse://bafy...codephreak-sft-v3", + "format": "jsonl-chatml", + "max_seq_len": 16384, + "packing": true + }, + "trainer": { + "type": "sft", + "framework": "trl", + "lora": {"r": 64, "alpha": 128, "dropout": 0.05, + "target_modules": ["q_proj","k_proj","v_proj","o_proj", + "gate_proj","up_proj","down_proj"]}, + "qlora": {"bits": 4, "compute_dtype": "bfloat16", + "double_quant": true, "quant_type": "nf4"} + }, + "optim": { + "optimizer": "paged_adamw_32bit", + "lr": 2e-5, "lr_scheduler": "cosine", "warmup_ratio": 0.03, + "weight_decay": 0.0, "max_grad_norm": 1.0, + "epochs": 3, "global_batch": 64, "micro_batch": 1, + "grad_accum": 8, "grad_checkpointing": true + }, + "distributed": { + "strategy": "fsdp", "shard": "full", "mixed_precision": "bf16", + "cpu_offload": false + }, + "callbacks": { + "eval_steps": 200, "save_steps": 500, + "eval_suites": ["bfcl_v4","persona_regression","agenda_regression"], + "stop_on_regression": true + }, + "storage": {"provider": "lighthouse", + "checkpoint_dir": "lighthouse://aGLM-BANKON-flag/sft-001/"}, + "provenance": {"erc8004_attest": true, + "x402_settlement": false, + "algorand_asa": 203977300} +} +``` + +For DPO, swap `trainer.type` to `dpo`, point the dataset at a preference manifest with `chosen`/`rejected` pairs, drop the LR to `5e-7`, set `beta: 0.1`, and target the same LoRA modules — TRL's `DPOTrainer` consumes this directly. + +For GRPO over agentic trajectories, `trainer.type: "grpo"` with `reward_funcs: ["bfcl_pass","tau_bench_score","persona_consistency"]`, `num_generations: 8`, `temperature: 0.9`, `max_prompt_length: 8192`, `max_completion_length: 8192`. The reward functions are pluggable Python callables registered in `mindxtrain.train.grpo.reward_registry`. + +## Part 5 — automindX v2 integration + +automindX v2 is the consumer of mindXtrain-produced `aGLM-BANKON-*` checkpoints. The upgrade path from `pythaiml/automindx/aglm.py` to v2 is mechanical given the gap analysis. The single class `LlamaModel(model_name, models_folder)` becomes the package `automindx.models.{Backend}` with backends `HfTransformersBackend`, `LlamaCppBackend`, `OllamaBackend`, `VllmBackend`, `OpenAiCompatBackend`, `ZaiBackend`, `AnthropicBackend`, `GroqBackend` — registered in `automindx.models.registry.ModelRegistry`, dispatched by string key, picking the right `automindx.chat.ChatTemplate` for the backend and falling back to `tokenizer.apply_chat_template`. The synchronous `generate_contextual_output` becomes `generate_contextual_output(context: ConversationContext) -> Generation`, with an `agenerate_contextual_output` async sibling — sync default surface is preserved so existing call sites work unchanged. The JSON-on-disk memory becomes a `MemoryStore` interface with backends `JsonFsBackend` (default, never break), `PgVectorScaleBackend`, `LighthouseFilecoinBackend`. The 4096-character ceiling (`chunk4096.py`) is replaced by tokenizer-aware truncation plus sliding-window summarization for long contexts; default ctx 8K–128K depending on backend. The hard-coded persona becomes `prompts/codephreak.yaml` with the persona, the agenda field as a first-class slot, and the chat-template-token mapping declared. + +The model registry, versioning, and hot-swap mechanics: `automindx.deploy.Registry` is a content-addressed registry where every `aGLM-BANKON-*` checkpoint is identified by the BLAKE3 hash of its safetensors plus its `TrainingRun` manifest CID. The registry tracks `current`, `canary`, and `rollback` pointers per tier (edge, mid, flagship, specialist). Hot-swap is atomic: `automindx.deploy.HotSwap.promote(tier, run_id)` flips the `current` pointer after running the persona-regression and agenda-regression suites against the live API harness; on failure, it auto-rollbacks. A/B testing is a traffic split at the `automindx.api.Router` layer: 95% to `current`, 5% to `canary`, with both branches logged as Claude-Code-JSONL trajectories to Lighthouse for offline statistical comparison via `automindx.eval.ab_compare(run_a, run_b, metric)`. The mindx.pythai.net public API exposes an OpenAI-compatible `/v1/chat/completions` plus a mindX-native `/v1/agentic` that takes an agenda field directly and returns a session ID plus streaming events. Every accepted request is provenance-linked: the model checkpoint hash, the run-id, the registry's ERC-8004 attestation, and (for paid tiers) the x402 micropayment receipt are all included in response headers. + +## Part 6 — comparative due diligence: the verdict + +The brief asked for a comparison of GLM-5.1 against Qwen3 (235B / 72B / 32B / 14B / 7B / 4B / 1.7B / 0.6B), Llama 3.3 70B, DeepSeek V3.1, Mistral Large 2, Gemma 3, and Phi-4. The May-2026 reality has moved beyond several of those: Qwen3.5 (Flash, 27B-dense, 35B-A3B, 122B-A10B) ships and outperforms Qwen3-235B on multiple axes; Llama 4 (Scout/Maverick, April 2025) ships under the *Llama 4 Community License* with the 700M MAU clause and EU AUP; DeepSeek V3.2 / V3.2-Speciale (December 2025) ships under MIT; Mistral Large 3 + Ministral 3 (December 2025) ships under Apache 2.0; Gemma 4 (April 2026) ships under Apache 2.0 — the single biggest license unlock of 2026; Phi-4 / Phi-4-mini ships under MIT. + +Apache-2.0 redistribution requires the upstream to be permissive: Apache 2.0, MIT, or BSD-class. That filter cleanly admits **GLM-5.1, Qwen3, Qwen3.5, DeepSeek V3.2, Mistral Large 3 + Ministral 3 + Mistral Small 4, Gemma 4, Phi-4, Phi-4-mini, and Yi-1.5**, and cleanly rejects **Llama 3.3, Llama 4, Cohere Command A** (CC-BY-NC, non-commercial), **Falcon 3** (TII Falcon 2.0 with AUP — most counsel will flag this as restrictive), and **Yi-Lightning** (proprietary API-only). That kills five of the ten brief candidates before benchmarks are scored. + +The verdict, on the dimensions of license-compatibility, agentic capability, tool-use quality, fine-tunability, ecosystem support, performance-per-parameter, multilingual balance, and size-ladder coverage, is that **mindXtrain should target Qwen3.5 as the primary base and treat GLM-5.1 as a premium specialist track**. Six reasons. + +First, license strength is symmetric. MIT and Apache 2.0 are functionally equivalent for downstream Apache 2.0 redistribution; both pass cleanly. There is no license-based reason to prefer GLM-5.1. + +Second, GLM-5.1's wins are real but narrow. SWE-Bench Pro 58.4 is genuinely SOTA. Terminal-Bench 2.0, MCP-Atlas, BrowseComp, CyberGym, AIME 2026 95.3, GPQA-Diamond 86.2 are all top-tier. But for tool-use plus function-calling plus web automation — the brief's stated agentic spec — GLM-5.1 is overkill. The 8-hour-autonomous-session capability is for software-engineering agents specifically. + +Third, fine-tunability gap. GLM-5.1 was 27 days old at research time; Axolotl recipes are still maturing, LLaMA-Factory support is partial, Unsloth has GGUF but full LoRA/QLoRA flows are not battle-tested. Qwen3 and Qwen3.5 have day-one PEFT, Axolotl, LLaMA-Factory, Unsloth, and TRL support with hundreds of teams' worth of exercised DPO and GRPO recipes. + +Fourth, per-parameter economics. GLM-5.1 at 754B/40B-A requires 4–8×H100 to *serve* and ZeRO-3 territory to fine-tune. Qwen3.5-122B-A10B has roughly comparable intelligence at one-quarter the active footprint and runs on 2×H100 comfortably; Qwen3.5-35B-A3B with 3B active reportedly exceeds Qwen3-235B-A22B. The agentic fine-tune sweet spot for mindXtrain is the 30B–122B-active class. + +Fifth, size ladder. mindXtrain needs a *family*, not a single model — flagship for hard flows, mid-tier for production traffic, edge for cost-or-latency-sensitive deployments. Only Qwen has a contiguous Apache-2.0 family from 0.6B → 235B → 235B-Instruct-2507 → 235B-Thinking-2507 → 3.5 medium series. GLM-5.1 is one model. Family completeness alone tips this toward Qwen. + +Sixth, BFCL leadership lives in the Qwen lineage. Qwen3-32B hits BFCL v3 75.7%; Qwen3.5-122B-A10B reaches BFCL v4 72.2%; the GLM-4.5 ancestor leads at 76.7%, but GLM-5.1 has not officially submitted to BFCL. For a function-calling-first agentic framework, Qwen has more *demonstrated* surface. + +The recommendation, restated in concrete pin-the-version-and-go form: target Qwen3.5-Flash for the edge tier or Phi-4-mini if BFCL-at-tiny-size matters more than open-Apache-only purity (Phi-4-mini is MIT, equivalent for redistribution); target Qwen3-7B for mid-tier; target Qwen3.5-122B-A10B for flagship; maintain GLM-5.1-FP8 as the specialist track for long-horizon SWE flows where 8-hour autonomous sessions and SWE-Bench Pro 58.4 are decisive; keep DeepSeek V3.2 as the reasoning-with-tool-use track backup if Qwen3.5-Thinking-class falls short on internal evals; keep Mistral Large 3 + Ministral 3 as the EU-jurisdictional secondary track; keep Gemma 4 as a watch-list option once vLLM kernel optimization and Axolotl recipes mature (the early-release speed and stability issues should clear within 60–90 days); do not base anything on Llama 3.3, Llama 4, Cohere Command A, Falcon 3, or Yi-Lightning regardless of benchmark performance. + +## Conclusion: provenance chain and execution order + +The end-to-end provenance chain is: **upstream open-weight base** (Qwen3.5-122B-A10B Apache 2.0 for primary, GLM-5.1-FP8 MIT for specialist) → **mindXtrain training pipeline** (data DAG with Lighthouse-anchored manifests, SFT + LoRA + DPO + GRPO + tool-use trajectory training, full eval harness with persona regression) → **`aGLM-BANKON-*` checkpoint** (Apache 2.0, NOTICE preserves upstream MIT / Apache, BLAKE3-hashed and Lighthouse-Filecoin-CIDed) → **ERC-8004 attestation** (Identity Registry `0x8004A169...` and Reputation Registry `0x8004BAa1...` on EVM via Foundry-tested contract calls, BANKON ASA `203977300` on Algorand for settlement) → **automindX v2 model registry** (content-addressed, hot-swap with canary and rollback, persona-and-agenda regression gates) → **mindx.pythai.net cognitive API** (OpenAI-compatible `/v1/chat/completions` plus mindX-native `/v1/agentic`, x402 micropayment for paid tiers, full provenance in response headers). + +Execution order, the boring critical-path version: first, lock the license posture — pull `zai-org/GLM-5.1-FP8` to a self-hosted store, write the Apache-2.0 + MIT-NOTICE compliance bundle for the BANKON derivative, draft the `mindx.pythai.net` Terms of Service that does *not* inherit Z.ai's AUP. Second, stand up the Qwen3.5-122B-A10B fine-tuning rail end-to-end on whatever 8×H100 / 8×A100 capacity is available, validate it with a deliberately-trivial Codephreak-persona SFT to exercise every callback, every storage backend, every regression gate. Third, port `pythaiml/automindx/aglm.py` to `automindx.models.HfTransformersBackend` with the `ChatTemplate` abstraction, preserving the persona-as-agenda discipline and the four-axis decomposition, in a v2 branch that the v1 callers can opt into without breaking. Fourth, reimplement the five ml-intern operator patterns into `mindxtrain.operator.*` once HF posts the LICENSE on `huggingface/ml-intern` (issue #41), or right now if the patterns are clean-room reimplemented from the public API description without copying source. Fifth, wire the storage and provenance layers — Lighthouse-Filecoin first (CIDs anchored in `TrainingRun` manifests), ERC-8004 attestation second (Foundry-tested contract calls from `mindxtrain.provenance.erc8004`), x402 settlement last. Sixth, take the resulting `aGLM-BANKON-flag-001` to the Gensyn distributed-RL track for the GRPO trajectory phase, exercising the WAN training fabric and the x402 per-trajectory micropayment rail simultaneously. + +The framing that matters for everything downstream: GLM-5.1 is a remarkable model, the strongest open-weights agentic-engineering base in May 2026, and a serious specialist track for mindXtrain. But it is not, on the rigorous evidence, the right *primary* base for a multi-tier Apache-2.0-redistributable agentic family. The right primary is Qwen3.5. Building the framework with that priority pair locked in — Qwen3.5 primary, GLM-5.1 specialist — is what makes mindXtrain a production-grade training framework rather than a single-model wrapper, and what gives `aGLM-BANKON` the family completeness it needs to serve every cell of the mindX cognitive API at `mindx.pythai.net`. \ No newline at end of file diff --git a/docs/blueprints/mindXtrain_ Production Blueprint for the AMD and lablab.ai Hackathon.md b/docs/blueprints/mindXtrain_ Production Blueprint for the AMD and lablab.ai Hackathon.md new file mode 100644 index 0000000000000000000000000000000000000000..774ceeec3c38c856d8b297b474984e288328c164 --- /dev/null +++ b/docs/blueprints/mindXtrain_ Production Blueprint for the AMD and lablab.ai Hackathon.md @@ -0,0 +1,499 @@ +# mindXtrain — production blueprint for the AMD × lablab.ai hackathon (May 4–10 2026) + +**The clock is already running.** The lablab.ai AMD Developer Hackathon opened its online build window on May 4 2026, the on-site finale runs May 9–10 in San Francisco at the MindsDB SF AI Collective, and the prize pool is **$21,500+ plus an AMD Radeon AI PRO R9700 GPU** across three tracks (Agents, Fine-Tuning on AMD GPUs, Vision/Multimodal) with stackable cross-cutting prizes for **Best Use of Qwen** and **Build in Public**, and a Hugging Face Spaces "most likes" prize topped by a Reachy Mini Wireless robot. mindXtrain enters as the **Fine-Tuning track** primary, cross-submitted to Best-Use-of-Qwen and Build-in-Public, with a one-line pitch nobody else in the live `lablab-ai-amd-developer-hackathon` HF org currently occupies: *"the first one-command Qwen3.6 fine-tuner natively optimized for MI300X — auto-selects Composable Kernel, AITER, hipBLASLt and Flash-Attention-ROCm configs from a 60-second micro-benchmark, trains via Optimum-AMD + TRL, quantizes with AMD Quark, and serves on vLLM-ROCm 7.2.1, all from a single CLI."* That positioning hits all four explicit lablab judging criteria — Technology Integration, Presentation, Business Value, Originality — and it directly maps onto AMD's actual KPI of ROCm developer adoption, which matters because the judge bench is led by **Ramine Rozen, CVP of AI at AMD**. The remainder of this document is the production blueprint. + +## How the hackathon actually scores, and the angle that wins it + +The lablab page lists four criteria with no explicit weights; sister AMD events (Bengaluru, Delhi) reveal a strong implicit theme of **production-readiness under hardware constraints** — judges reward submissions that exploit MI300X's 192 GB HBM3 to do something an H100 80 GB cannot. The current bar to beat is **REPOMIND**, a repo-scale coding agent in the live HF org built on Qwen3-Coder-Next-FP8 + vLLM ROCm 7 that explicitly markets "H100 OOMs on this workload, MI300X just runs it." mindXtrain's differentiator is orthogonal — it is *infrastructure that produces specialized Qwen3 models cheaply on MI300X*, not a single specialized application — and the AutoML/AutoTrain niche is genuinely empty in the lablab archive. The submission therefore needs to be **demonstrable in 90 seconds**, must produce a tangible artifact (a finetuned Qwen3 checkpoint pushed to the HF org, served on a public Space), and must show the AMD stack being *fully* exercised at every layer rather than treated as a black box. The Build-in-Public extension costs almost nothing — two technical X posts tagging @lablab and @AIatAMD plus an MIT-licensed repo are required anyway — so it is a free additional prize pool. + +The **single most differentiating angle** is the auto-selection layer. No competitor framework — not Axolotl, LLaMA-Factory, Unsloth, torchtune, Primus, or Optimum-AMD itself — runs a **per-job MI300X micro-benchmark** before training to pick CK vs Triton attention backends, hipBLASLt heuristic vs rocBLAS path, AITER vs reference MoE kernels, NCCL_MIN_NCHANNELS, gradient-checkpointing strategy, FSDP shard width, and LoRA rank against the actual (model, dataset shape, sequence length, GPU count) tuple. mindXtrain owns that AOT-only autotune layer. + +## The mindXtrain reference stack, pinned + +The recommended container is **`rocm/primus:v26.2`** (which is identical to `rocm/pytorch-training:v26.2` and bundles ROCm 7.2.1, PyTorch 2.9.1, AOTriton 0.11+, AITER 0.1.12, RCCL with Pollara fixes, Apex with fused RoPE, Megatron-Core, TorchTitan, Primus-Turbo, ROCm/TransformerEngine, and Quark). Pull it once, snapshot the SHA256 digest into `infra/podman/digest.lock`, and never run training off a floating tag. For lighter SFT-only flavors a thinner image — **`rocm/pytorch:rocm7.2.1_ubuntu24.04_py3.12_pytorch_release_2.9.1`** — is acceptable. + +Above the container, the layered Python stack is `torch==2.9.1+rocm7.2.1.lw` (from `repo.radeon.com/rocm/manylinux/rocm-rel-7.2.1`, *not* `download.pytorch.org/whl/nightly/rocm7.2`, because the AMD-validated wheels are reproducible while nightlies churn), `triton==3.5.1+rocm7.2.1`, `transformers>=4.46,<4.50`, `accelerate>=1.0`, `peft>=0.13`, `trl>=0.12`, `datasets>=3.0`, `optimum>=1.24`, `optimum-amd` from main, `amd-quark>=0.11.1`, `auto-gptq` from `huggingface.github.io/autogptq-index/whl/rocm573`, `bitsandbytes` built from `github.com/ROCm/bitsandbytes` branch `rocm_enabled_multi_backend` with `-DBNB_ROCM_ARCH="gfx942;gfx950" -DCOMPUTE_BACKEND=hip`, `flash-attn` built from `Dao-AILab/flash-attention` upstream with both CK and Triton backends compiled (`FLASH_ATTENTION_TRITON_AMD_ENABLE=TRUE`), and `vllm` ROCm 7.0+ wheels for serving. **Numpy must be pinned `<2.0`** against torch 2.9 ROCm wheels. + +The runtime environment is non-negotiable on MI300X: `HSA_NO_SCRATCH_RECLAIM=1`, `NVTE_CK_USES_BWD_V3=1`, `NVTE_CK_IS_V3_ATOMIC_FP32=1`, `PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1`, `HIP_FORCE_DEV_KERNARG=1`, `PYTORCH_ROCM_ARCH=gfx942`, `NCCL_MIN_NCHANNELS=112` for sub-8-GPU jobs, `GPU_MAX_HW_QUEUES=1` for multi-GPU stability, and `numa_balancing` off in `/proc/sys/kernel`. **Pin to ROCm 7.2.1 or later** — 7.1.x has a documented bf16/fp16 GEMM cu-fallback regression on gfx942 that costs ~600 ms per call through hipBLASLt heuristic scans. The deprecated `ROCm/Megatron-LM` Docker is being retired in favor of Primus; adopt Primus from day zero to avoid migration debt. + +## The Qwen3 family targeting decision + +**Qwen3.6 is real**, contrary to the user's implicit doubt. The open-weight cadence reads Qwen3 (April 2025, original 8 dense + 2 MoE checkpoints) → Qwen3-2507 split refresh (July 2025, 256K native context) → Qwen3-Next-80B-A3B (September 2025, hybrid Gated DeltaNet + sparse MoE) → Qwen3-Coder/VL/Omni/Guard/ASR/Image (Aug–Oct 2025) → Qwen3.5 family (Feb 16 2026, unified text+vision backbone, flagship 397B-A17B) → **Qwen3.6** (April 2026, currently the newest open-weight checkpoint as of May 5 2026). The two open Qwen3.6 checkpoints are `Qwen/Qwen3.6-27B` (dense, released April 22 2026) and `Qwen/Qwen3.6-35B-A3B` (MoE, released April 16 2026), both Apache 2.0, both multimodal `image-text-to-text`, both 262,144 native context extendable to ~1M via YaRN, both default-thinking with no `/think` `/no_think` soft switch (use `chat_template_kwargs={"enable_thinking": False}` instead), and both introduce **Thinking Preservation** (`preserve_thinking=True`) for agentic multi-turn KV-cache reuse. Vocab grew from 151,936 in Qwen3 to 248,320 in Qwen3.6 to support 201 languages. + +mindXtrain's **default targets** for the hackathon demo should be a tier of three: **Qwen3-8B** as the headline single-MI300X full-FT case (fits one card with bs=8 seq=4096, AdamW, bf16, ~80 GB peak, 12–20k tok/s on Primus-Turbo); **Qwen3-32B** as the FSDP-2 4×MI300X full-FT showpiece (bs=2, seq=4096, ~5–9 hours per 1B tokens at FP8 with TE-CK); and **Qwen3.6-35B-A3B** as the latest-and-greatest MoE LoRA case (experts-only adapters, gate frozen, 2×MI300X with EP enabled). Qwen3-235B-A22B-Thinking-2507 is the dramatic-but-impractical option — LoRA on FP8/MXFP4-quantized weights across 8×MI300X is feasible at ~5–12k tok/s; full FT requires multi-node and is out of hackathon scope. + +Two Qwen3-specific landmines deserve naming. First, hybrid Gated DeltaNet layers in Qwen3-Next, Qwen3.5 and Qwen3.6 do not use standard SDPA — they require `flash-linear-attention` and `causal-conv1d`, and on ROCm both need community wheels or source builds; without them you fall back to a slow PyTorch reference path. Second, the **MoE router gate** must be frozen during fine-tuning; thawing it almost always diverges. Both pitfalls belong in mindXtrain's autotune as hard rules, not user-facing knobs. + +The Qwen team's preferred RL algorithm is **GRPO** (Qwen3 launch/2507) and its successor **GSPO** (Qwen3-Next/3.5/3.6, recommended for hybrid + sparse MoE stability). AMD has published an end-to-end ROCm + TRL + vLLM + DeepSpeed GRPO recipe on MI300X using GSM8K and Qwen2.5-1.5B-Instruct — that recipe transposes one-to-one onto Qwen3-8B and is the safest single-node demo path. + +## What the open-source landscape gives us, and what it doesn't + +The framework survey produced a clear ranking. **Axolotl** (Apache-2.0, YAML-driven, 70k+ models, March 2026 added Qwen3.5/Qwen3.5-MoE, ND parallelism, FP8 via torchao) is the right *YAML skeleton* for mindXtrain's multi-GPU SFT/DPO/ORPO/GRPO/QAT path; ROCm support is second-class but the AI-DarwinLabs `amd-support` branch ships a working `requirements-amd.txt` and AMD itself published a Dockerfile.rocm walkthrough. **LLaMA-Factory** (Apache-2.0, 70.6k stars, Qwen team's own preferred trainer, AMD's own MI300X tutorial uses it) is the right *Qwen3-specific recipe library* — supports every Qwen3 variant including 2507, VL, Omni and Next, plus PPO/DPO/KTO/ORPO/SimPO/GRPO. **torchtune** (BSD-3, AMD-CI-tested, the cleanest codebase) is the right *modular reference implementation*, weak only because Qwen3 recipes are not yet upstream — a ~200-line PR that mindXtrain should ship as a side-deliverable. **Unsloth** (Apache-2.0, official AMD partnership via OneClickAMD, fastest single-GPU experience) belongs as an **opt-in backend** for single-MI300X jobs where its 16-bit LoRA path beats everyone else, with the caveat that bitsandbytes 4-bit is currently unstable on ROCm 7.x and Unsloth correctly disables it. Below those four sit **TRL + PEFT + Accelerate** as mandatory dependencies (every higher-level trainer wraps them; Accelerate is first-class on ROCm without code changes), **Optimum-AMD** as the mandatory Flash-Attention-2/GPTQ/`amdrun`-topology shim (now MIT-licensed, Apache-compatible), **DeepSpeed** as the first-class ZeRO/MoE backend on ROCm 6+, **vLLM-ROCm** and **SGLang** as first-class rollout engines for GRPO and serving (vLLM ROCm CI went live December 2025, SGLang ships explicit `rocm/sgl-dev:v0.5.8.post1-rocm720-mi30x` images), and **AMD-AGI/Primus + Primus-Turbo** as the right pretraining-scale fallback when mindXtrain is asked to do continued pretraining at >70 B parameters. NeMo is disqualified (CUDA-only TransformerEngine dependency), the AMD GPT-NeoX branch is three years stale, Megatron-DeepSpeed is superseded, and Lamini is CC-BY-NC and disqualified by license. + +The whitespace mindXtrain fills is precisely the seven gaps no existing framework owns: **(1)** MI300X-aware automated hyper-parameter selection driven by a 60-second micro-benchmark; **(2)** a one-line install matrix that pins a known-working ROCm/PyTorch/Flash-Attn/bitsandbytes/Unsloth tuple per ROCm version; **(3)** Qwen3-family torchtune recipes (upstreamable PR); **(4)** a unified `mindxtrain.yaml` schema that compiles down to either Axolotl or Unsloth backends based on cost/scale; **(5)** a native MI300X 4-bit path that bypasses bitsandbytes via Quark FP8/MXFP4 + LoRA-on-quantized weights; **(6)** auto-orchestration of vLLM-ROCm/SGLang rollout engines on idle GPUs of the same MI300X node for in-the-loop GRPO; and **(7)** a hackathon-grade reproducibility manifest — a "training receipt" capturing ROCm version, gfx target, every git SHA, the Docker digest, the exact YAML, and dataset hashes. + +## mindXtrain architecture + +mindXtrain is structured as five concentric layers. The **CLI layer** (`mindxtrain/cli.py`) exposes `mindxtrain init`, `mindxtrain bench` (the autotune micro-benchmark), `mindxtrain dataset prep`, `mindxtrain train`, `mindxtrain eval`, `mindxtrain quantize`, `mindxtrain serve`, `mindxtrain publish` and `mindxtrain receipt`. Each command consumes a single Pydantic-validated YAML config that supersedes Axolotl's, LLaMA-Factory's and torchtune's schemas; mindXtrain's job is to compile that config down to whichever backend YAML the underlying trainer expects. The **autotune layer** runs first — it inspects the (model, dataset, seq-len, GPU topology) tuple, executes a short profiled forward+backward to measure attention kernel throughput, GEMM heuristics, AllReduce bandwidth and HBM usage, then writes a `mindxtrain.tuned.yaml` with backend-specific overrides (CK vs Triton attention, AITER MoE on/off, NCCL_MIN_NCHANNELS, FSDP shard, LoRA rank ceiling, gradient-checkpointing policy). This layer is **AOT-only**: the autotune produces a static plan that is loaded at training start; **JIT autotune is forbidden in production** to keep training reproducible, in line with the user's existing mindX AutoTune discipline. The **dataset layer** wraps `datasets`, deduplicates with MinHash + SemDeDup, applies quality filtering, packs sequences (pack-to-cutoff Qwen3-style), shards for FSDP, and emits content-addressed Lighthouse Storage CIDs for provenance. The **training layer** dispatches to one of four backends — `axolotl` (multi-GPU SFT/DPO/ORPO/GRPO), `unsloth` (single-MI300X fast LoRA/GRPO), `torchtune` (modular recipes), or `primus` (pretraining-scale Megatron/TorchTitan) — selected by a `backend:` key with a sensible default chosen by autotune. Accelerate is the launcher in all cases except primus. The **artifact + integration layer** quantizes via Quark (FP8 / MXFP4 / INT4-FP8 two-level), evaluates with `lm-evaluation-harness` plus user-supplied custom evals with automatic regression detection against a baseline checkpoint, generates a model card, pushes to the HF org, mirrors the safetensors to Lighthouse Storage with a CID receipt, registers the trained model with the **mindX cognitive API** (`mindx.pythai.net`) as a new agent capability, lists it on **AgenticPlace** (`agenticplace.pythai.net`), allocates an **ENS subname** under `bankon.eth` for the resulting agent, and wires **x402 Algorand micropayments via parsec/parsec-wallet** for the training-as-a-service billing flow. Optional decentralized compute hooks for Bacalhau, Akash, and io.net are stubbed in `mindxtrain/compute/` so a job YAML can opt into running on a non-AMD-Cloud provider. + +The **CLI surface** is deliberately small. `mindxtrain init ` scaffolds a flat snake_case directory and a starter YAML. `mindxtrain bench --model Qwen/Qwen3-8B --hardware mi300x --gpus 1` runs the autotune. `mindxtrain train -c config.yaml` runs the full pipeline. `mindxtrain serve --checkpoint ./out/last --backend vllm-rocm` spins a vLLM-ROCm endpoint with the right tool-call and reasoning parsers (`hermes` + `qwen3`/`deepseek_r1` for non-coder Qwen3 models, `qwen3_coder` for Coder variants). `mindxtrain publish` pushes everything — checkpoint, model card, eval report, training receipt, Lighthouse CID — to HF, mindX, AgenticPlace and the BANKON ENS layer in one call. `mindxtrain receipt ` re-emits the manifest for any past run. + +The **config schema** is a single YAML with five top-level sections — `meta`, `model`, `data`, `train`, `serve` — each strongly typed by Pydantic. A canonical Qwen3-8B SFT-with-LoRA-on-1×MI300X config is reproduced below in the snippets section. Cross-cutting fields like `seed`, `provenance.lighthouse_endpoint`, and `hardware.gfx_arch` apply globally. Each training method (full FT, LoRA, QLoRA, DPO, ORPO, GRPO, GSPO, KTO, continued pretraining, multimodal Qwen3-VL/Omni/Image) is a discriminated union under `train.method`. **Hyperparameter search** uses Optuna by default (TPE sampler, MedianPruner) with the search space bounded by autotune-derived hard limits; Ray Tune is supported when a Kubernetes cluster is available; W&B Sweeps is the third option for teams already on W&B. The bounding rule is non-negotiable: autotune's profiled max batch size is the upper bound on `per_device_train_batch_size`, full stop. + +## Hackathon-winning submission strategy + +The 90-second demo storyline is concrete. Open with terminal: a single `mindxtrain train -c demo_qwen3_8b_sft.yaml` command. Cut to the autotune dashboard streaming MI300X micro-benchmark output (CK FA forward TFLOPS, hipBLASLt heuristic times, RCCL bus bandwidth) for 60 seconds. Cut to the training loop streaming loss + tok/s + MFU (target >40% MFU on Qwen3-8B BF16 with Primus-Turbo, comparable to AMD's published Llama-3.1-8B numbers). Cut to a side-by-side cost slide: **MI300X at $1.99/hr × 1 GPU × 3 hours = ~$6 versus H100 at $4/hr × 2 GPUs × 4 hours = ~$32**, with the headline "4× cheaper, can't OOM at 192 GB." Cut to the resulting model live in the HF Space chatting in thinking mode, then show `mindxtrain receipt` printing the full provenance manifest. Closing slide is one diagram of mindXtrain → mindX → AgenticPlace → BANKON. The story takes 90 seconds, leaves 60–90 seconds for Q&A in the 3-minute video budget. + +The deliverables checklist mapped to lablab's submission rubric is: a working prototype deployed as a Hugging Face Space inside the `lablab-ai-amd-developer-hackathon` HF org (Space name: `mindxtrain-demo`); a 2-3 minute demo video posted to YouTube and tweeted from the codephreak account tagging @lablab @AIatAMD @AMDROCm @huggingface @Alibaba_Qwen (auto-qualifies for Build-in-Public); a pitch deck of 5-7 slides Sequoia-style; the GitHub repo at `pythai/mindxtrain` MIT-licensed (or Apache-2.0 — verify the lablab page's "MIT-only" requirement on the live form, with Apache-2.0 as the codephreak-preferred fallback that may need a license-compat note); the lablab platform submission form filled with a 50-char title `mindXtrain — one-command Qwen3 on MI300X`, a 255-char short description, a 100-word-minimum long description naming every AMD library used, the cover image, the video URL, the GitHub URL, and the demo Space URL; verification of AMD AI Developer Program membership for the $100 cloud credits; and at least two technical X posts during the build window plus a written ROCm feedback note for Build-in-Public eligibility. + +The README structure for the GitHub repo is `# mindXtrain` headline, one-line tagline, a 30-second quick-start (`pipx install mindxtrain && mindxtrain init demo && mindxtrain train`), an architecture diagram (Mermaid), a benchmark table comparing mindXtrain to Axolotl/LLaMA-Factory/Unsloth/torchtune on Qwen3-8B-on-1×MI300X for the metrics tok/s, time-to-eval-loss-1.5, MFU, and total $; an "AMD stack exploited" section enumerating ROCm 7.2.1, AOTriton, AITER, Composable Kernel, hipBLASLt with offline tuning, Flash-Attention-CK, RCCL, Optimum-AMD, Quark, Primus-Turbo, vLLM-ROCm, and SGLang with one-line citations; a "what makes this MI300X-native" section pointing at the autotune; a license header; a roadmap; and an acknowledgments block thanking AMD, Hugging Face, and the Qwen team. The benchmark numbers to chase on the hero workload (Qwen3-8B SFT, 1×MI300X, bs=8, seq=4096, BF16, AdamW, 1B tokens) are **>15k tok/s, MFU >40%, time-to-loss-1.5 <90 minutes, total cost <$3**. Hit those and the cost slide writes itself. + +The comparison table mindXtrain prints in its README is the differentiator artifact: rows are Axolotl, LLaMA-Factory, Unsloth, torchtune, Primus, mindXtrain; columns are *one-command install on ROCm 7.2.1, MI300X auto-tune, Qwen3.6 day-zero recipe, FP8 via Quark, x402 micropayments, decentralized fallback, training receipt manifest*. mindXtrain is the only row with all seven cells filled. + +The risks and mitigations are well-defined. **Risk**: bitsandbytes 4-bit instability on ROCm 7.x. **Mitigation**: ship the Quark FP8 + LoRA path as default; bitsandbytes only as opt-in. **Risk**: Qwen3.6 GDN layers need flash-linear-attention/causal-conv1d which lack official ROCm wheels. **Mitigation**: build wheels in CI, ship in the Podman image, pin commit SHAs; fall back to PyTorch reference if build fails. **Risk**: lablab requires MIT license per submission rules but codephreak prefers Apache 2.0. **Mitigation**: dual-license the submission repo MIT for hackathon compliance, with a NOTICE file pointing at the upstream Apache-2.0 mindX ecosystem; reconcile post-hackathon. **Risk**: 60-second autotune window may not cover MoE expert imbalance. **Mitigation**: emit a warning in the receipt and run a longer autotune for Qwen3-30B-A3B / Qwen3.6-35B-A3B / Qwen3-235B-A22B specifically. **Risk**: vLLM-ROCm Triton autotune can stall first-batch on cold start. **Mitigation**: warm-up batch in `mindxtrain serve` before exposing the endpoint; or fall back via `VLLM_USE_TRITON_FLASH_ATTN=0`. + +## Repository skeleton + +The cypherpunk2048 standard is flat snake_case throughout, Apache-2.0 license header on every file, no proprietary lock-in, no upgradeable proxies in any Solidity, no EOA admin keys, Foundry for Solidity build, Podman over Docker: + +``` +mindxtrain/ +├── pyproject.toml +├── README.md +├── LICENSE # Apache-2.0 (hackathon submission may dual-license MIT) +├── NOTICE +├── mindxtrain/ +│ ├── __init__.py +│ ├── cli.py # entry point (typer-based) +│ ├── config.py # Pydantic schema +│ ├── autotune/ +│ │ ├── __init__.py +│ │ ├── benchmark.py # 60-second MI300X probe +│ │ ├── attention_probe.py # CK vs Triton vs AITER selection +│ │ ├── gemm_probe.py # hipBLASLt offline tune trigger +│ │ ├── rccl_probe.py # bus-bandwidth measurement +│ │ └── plan.py # writes mindxtrain.tuned.yaml (AOT) +│ ├── dataset/ +│ │ ├── load.py +│ │ ├── dedupe_minhash.py +│ │ ├── dedupe_semdedup.py +│ │ ├── quality_filter.py +│ │ ├── tokenize.py +│ │ ├── pack.py +│ │ └── shard.py +│ ├── train/ +│ │ ├── dispatch.py # picks backend +│ │ ├── backend_axolotl.py +│ │ ├── backend_unsloth.py +│ │ ├── backend_torchtune.py +│ │ ├── backend_primus.py +│ │ └── recipes/ +│ │ ├── qwen3_8b_sft_lora.yaml +│ │ ├── qwen3_8b_sft_full.yaml +│ │ ├── qwen3_32b_full_fsdp.yaml +│ │ ├── qwen3_32b_dpo.yaml +│ │ ├── qwen3_32b_orpo.yaml +│ │ ├── qwen3_32b_grpo.yaml +│ │ ├── qwen3_30b_a3b_lora.yaml +│ │ ├── qwen3_6_27b_lora.yaml +│ │ ├── qwen3_6_35b_a3b_lora.yaml +│ │ ├── qwen3_vl_8b_sft.yaml +│ │ └── qwen3_8b_cpt.yaml +│ ├── eval/ +│ │ ├── harness.py # lm-evaluation-harness wrapper +│ │ ├── custom_eval.py +│ │ └── regression.py +│ ├── quantize/ +│ │ ├── quark_fp8.py +│ │ ├── quark_mxfp4.py +│ │ └── gptq_rocm.py +│ ├── serve/ +│ │ ├── vllm_rocm.py +│ │ ├── sglang_rocm.py +│ │ └── parsers.py # hermes / qwen3_coder / qwen3 reasoning +│ ├── publish/ +│ │ ├── hf_hub.py +│ │ ├── lighthouse.py # IPFS / Filecoin via Lighthouse Storage +│ │ ├── mindx_register.py # POST to mindx.pythai.net +│ │ ├── agenticplace_list.py # POST to agenticplace.pythai.net +│ │ └── bankon_ens.py # ENS subname under bankon.eth +│ ├── billing/ +│ │ ├── x402_algorand.py # parsec/parsec-wallet integration +│ │ └── pricing.py +│ ├── compute/ +│ │ ├── amd_dev_cloud.py +│ │ ├── tensorwave.py +│ │ ├── bacalhau.py # optional decentralized +│ │ ├── akash.py +│ │ └── ionet.py +│ ├── telemetry/ +│ │ ├── prometheus_exporter.py +│ │ ├── otel_hooks.py +│ │ └── energy.py # MI300X power tracking via rocm-smi +│ ├── receipt/ +│ │ ├── manifest.py # full provenance record +│ │ └── verify.py +│ └── chain_map/ +│ └── allchain.py # consumes agenticplace.pythai.net/allchain.html +├── infra/ +│ ├── podman/ +│ │ ├── containerfile_train # FROM rocm/primus:v26.2 +│ │ ├── containerfile_serve # FROM rocm/vllm-dev:rocm7.2.1 +│ │ └── digest.lock +│ ├── compose/ +│ │ └── compose_dev.yaml +│ └── k8s/ +│ └── train_job.yaml +├── contracts/ +│ ├── foundry.toml +│ ├── src/ +│ │ ├── mindxtrain_registry.sol # immutable, no proxy, no admin +│ │ └── x402_receiver.sol +│ ├── script/ +│ └── test/ +├── examples/ +│ ├── demo_qwen3_8b_sft.yaml +│ ├── demo_qwen3_6_27b_lora.yaml +│ └── demo_grpo_gsm8k.yaml +├── tests/ +│ ├── test_autotune.py +│ ├── test_config.py +│ ├── test_recipes.py +│ └── test_receipt.py +├── docs/ +│ ├── architecture.md +│ ├── benchmarks.md +│ └── hackathon_submission.md +└── .github/workflows/ + ├── ci_lint.yml + ├── ci_rocm_smoke.yml # runs on self-hosted MI300X runner + └── publish_pypi.yml +``` + +## Critical code snippets + +**`pyproject.toml`** (excerpt): + +```toml +[project] +name = "mindxtrain" +version = "0.1.0" +description = "One-command Qwen3 fine-tuning on AMD MI300X" +requires-python = ">=3.12" +license = { text = "Apache-2.0" } +authors = [{ name = "Gregory (codephreak)", email = "codephreak@pythai.net" }] +dependencies = [ + "typer>=0.12", + "pydantic>=2.7", + "pyyaml>=6.0", + "transformers>=4.46,<4.50", + "accelerate>=1.0", + "peft>=0.13", + "trl>=0.12", + "datasets>=3.0", + "optimum>=1.24", + "optimum-amd", + "amd-quark>=0.11.1", + "lm-eval>=0.4.5", + "optuna>=3.6", + "prometheus-client>=0.20", + "opentelemetry-api>=1.27", + "lighthouse-web3>=0.1", + "py-algorand-sdk>=2.6", + "ens>=0.5", + "datasketch>=1.6", # MinHash dedupe + "numpy<2.0", +] + +[project.optional-dependencies] +axolotl = ["axolotl @ git+https://github.com/axolotl-ai-cloud/axolotl@main"] +unsloth = ["unsloth"] +torchtune = ["torchtune"] +primus = ["primus @ git+https://github.com/AMD-AGI/Primus@v26.2"] + +[project.scripts] +mindxtrain = "mindxtrain.cli:app" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.ruff] +line-length = 100 +target-version = "py312" +``` + +**`mindxtrain/cli.py`** (entry-point sketch): + +```python +# SPDX-License-Identifier: Apache-2.0 +"""mindXtrain CLI — one-command Qwen3 fine-tuning on AMD MI300X.""" +from __future__ import annotations +import typer +from pathlib import Path +from mindxtrain.config import load_config +from mindxtrain.autotune.benchmark import run_benchmark +from mindxtrain.autotune.plan import write_tuned_plan +from mindxtrain.train.dispatch import dispatch_training +from mindxtrain.eval.harness import run_eval +from mindxtrain.quantize.quark_fp8 import quantize_fp8 +from mindxtrain.serve.vllm_rocm import serve_vllm +from mindxtrain.publish.hf_hub import publish_to_hf +from mindxtrain.publish.lighthouse import publish_to_lighthouse +from mindxtrain.publish.mindx_register import register_with_mindx +from mindxtrain.publish.agenticplace_list import list_on_agenticplace +from mindxtrain.publish.bankon_ens import allocate_ens_subname +from mindxtrain.receipt.manifest import emit_receipt + +app = typer.Typer(no_args_is_help=True, add_completion=False) + +@app.command() +def init(project: str) -> None: + """Scaffold a new mindXtrain project (flat snake_case).""" + Path(project).mkdir(parents=True, exist_ok=False) + Path(f"{project}/config.yaml").write_text(_starter_yaml(project)) + typer.echo(f"initialized {project}/") + +@app.command() +def bench( + model: str = typer.Option(..., "--model"), + hardware: str = typer.Option("mi300x", "--hardware"), + gpus: int = typer.Option(1, "--gpus"), + seq_len: int = typer.Option(4096, "--seq-len"), + out: Path = typer.Option(Path("mindxtrain.tuned.yaml"), "--out"), +) -> None: + """Run the 60-second MI300X autotune probe; emits an AOT plan.""" + measurements = run_benchmark(model=model, hardware=hardware, gpus=gpus, seq_len=seq_len) + write_tuned_plan(measurements, out) + typer.echo(f"wrote tuned plan to {out}") + +@app.command() +def train(config: Path = typer.Option(..., "-c", "--config")) -> None: + """Run the full pipeline (autotune → train → eval → quantize → publish).""" + cfg = load_config(config) + if cfg.autotune.enabled and not cfg.autotune.plan_path.exists(): + bench(model=cfg.model.name, hardware=cfg.hardware.name, + gpus=cfg.hardware.gpus, seq_len=cfg.data.seq_len, + out=cfg.autotune.plan_path) + run_id = dispatch_training(cfg) + eval_report = run_eval(cfg, run_id) + if cfg.quantize.enabled: + quantize_fp8(cfg, run_id) + if cfg.publish.enabled: + hf_url = publish_to_hf(cfg, run_id, eval_report) + cid = publish_to_lighthouse(cfg, run_id) + register_with_mindx(cfg, run_id, hf_url, cid) + list_on_agenticplace(cfg, run_id, hf_url) + allocate_ens_subname(cfg, run_id) + emit_receipt(cfg, run_id, eval_report) + +@app.command() +def serve( + checkpoint: Path = typer.Option(..., "--checkpoint"), + backend: str = typer.Option("vllm-rocm", "--backend"), + port: int = typer.Option(8000, "--port"), +) -> None: + """Serve a trained checkpoint on vLLM-ROCm or SGLang with correct parsers.""" + if backend == "vllm-rocm": + serve_vllm(checkpoint, port=port) + else: + from mindxtrain.serve.sglang_rocm import serve_sglang + serve_sglang(checkpoint, port=port) + +if __name__ == "__main__": + app() +``` + +**`examples/demo_qwen3_8b_sft.yaml`** (the hackathon hero config, fits one MI300X): + +```yaml +# SPDX-License-Identifier: Apache-2.0 +meta: + project: mindxtrain_demo + run_name: qwen3_8b_sft_demo + seed: 2048 + license: apache-2.0 + +hardware: + name: mi300x + gfx_arch: gfx942 + gpus: 1 + expected_hbm_gb: 192 + +autotune: + enabled: true + plan_path: ./out/mindxtrain.tuned.yaml + budget_seconds: 60 + policy: aot_only # JIT autotune forbidden in production + +model: + name: Qwen/Qwen3-8B + attn_implementation: flash_attention_2 # CK backend by default; autotune may override + torch_dtype: bfloat16 + trust_remote_code: false + +data: + source: hf + hf_id: HuggingFaceH4/ultrachat_200k + split: train_sft + seq_len: 4096 + packing: true + dedupe: + minhash: { threshold: 0.85 } + semdedup: { threshold: 0.95, model: sentence-transformers/all-MiniLM-L6-v2 } + shard: + num_shards: 1 + +train: + backend: axolotl # autotune may flip to unsloth for single-GPU LoRA + method: + kind: lora + r: 16 + alpha: 32 + dropout: 0.0 + target_modules: [q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj] + optimizer: + name: adamw_torch_fused + lr: 1.0e-4 + betas: [0.9, 0.95] + weight_decay: 0.1 + grad_clip: 1.0 + schedule: + type: cosine + warmup_ratio: 0.03 + epochs: 3 + batch: + per_device: 8 + grad_accum: 4 + precision: bf16 + gradient_checkpointing: true + flash_attention: + backend: ck # ck | triton | aiter — autotune picks + fsdp: { enabled: false } # single GPU + env: + HSA_NO_SCRATCH_RECLAIM: "1" + NVTE_CK_USES_BWD_V3: "1" + NVTE_CK_IS_V3_ATOMIC_FP32: "1" + PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32: "1" + NCCL_MIN_NCHANNELS: "112" + HIP_FORCE_DEV_KERNARG: "1" + PYTORCH_ROCM_ARCH: "gfx942" + +eval: + harness: + tasks: [mmlu, gsm8k, ifeval, humaneval] + fewshot: 5 + regression: + baseline: Qwen/Qwen3-8B + threshold_pct: -1.0 # fail if any task drops more than 1 pct + +quantize: + enabled: true + scheme: quark_fp8 # quark_fp8 | quark_mxfp4 | gptq_rocm + ptpc: true # PTPC FP8 GEMM (15-30% faster than BlockScale on MI300X) + +serve: + backend: vllm-rocm + reasoning_parser: deepseek_r1 # qwen3 for 3.5/3.6 + tool_call_parser: hermes # qwen3_coder for Coder family + tensor_parallel: 1 + +publish: + enabled: true + hf: + repo: pythai/qwen3-8b-mindxtrain-demo + private: false + lighthouse: + api_key_env: LIGHTHOUSE_API_KEY + mindx: + api_url: https://mindx.pythai.net/v1/agents + register_as_capability: true + agenticplace: + api_url: https://agenticplace.pythai.net/v1/listings + chain_map_url: https://agenticplace.pythai.net/allchain.html + bankon: + ens_parent: bankon.eth + subname: qwen3-8b-mindxtrain-demo + billing: + x402: + network: algorand + asset: USDC + receiver_via: parsec_wallet + price_per_1k_tokens: 0.0002 + +receipt: + output: ./out/receipt.json + include: + [rocm_version, gfx_arch, container_digest, all_git_shas, + yaml_hash, dataset_cids, eval_report, energy_kwh] +``` + +**Sample Solidity registry stub (Foundry, immutable, no proxy, no EOA admin)**: + +```solidity +// SPDX-License-Identifier: Apache-2.0 +pragma solidity ^0.8.26; + +contract MindXTrainRegistry { + struct Receipt { + bytes32 yamlHash; + bytes32 datasetCidHash; + bytes32 checkpointCidHash; + bytes32 evalReportHash; + address publisher; + uint64 timestamp; + } + + mapping(bytes32 => Receipt) private _receipts; + event ReceiptAnchored(bytes32 indexed runId, address indexed publisher, bytes32 yamlHash); + + function anchor( + bytes32 runId, + bytes32 yamlHash, + bytes32 datasetCidHash, + bytes32 checkpointCidHash, + bytes32 evalReportHash + ) external { + require(_receipts[runId].timestamp == 0, "exists"); + _receipts[runId] = Receipt({ + yamlHash: yamlHash, + datasetCidHash: datasetCidHash, + checkpointCidHash: checkpointCidHash, + evalReportHash: evalReportHash, + publisher: msg.sender, + timestamp: uint64(block.timestamp) + }); + emit ReceiptAnchored(runId, msg.sender, yamlHash); + } + + function get(bytes32 runId) external view returns (Receipt memory) { + return _receipts[runId]; + } +} +``` + +No constructor admin, no `Ownable`, no upgradeable proxy, no pause function, no setter — write-once anchoring, EOA-key-free administration. DAIO blockchain deployment is the remaining piece per the user's standard preferences; mainnet Foundry deploy targets the canonical chain ID resolved through `agenticplace.pythai.net/allchain.html`. + +## Reconciliation with prior `mindXtrain.md` and `mindXtrain2.md` + +Because the two prior design files are not in research context, this document is written as a forward-compatible **continuation**, not a replacement. Three places need explicit reconciliation when the prior files are read alongside this one. **First**, the AMD-track framing pulls the recommended default model away from any GPU-agnostic choice toward Qwen3.6-27B / Qwen3.6-35B-A3B / Qwen3-8B specifically, because Qwen integration is a stackable hackathon prize and Qwen3 has confirmed Day-0 ROCm support; if mindXtrain.md/mindXtrain2.md committed to a different default base model, that decision should be revisited for the hackathon submission only and reverted afterwards if needed. **Second**, the AOT-only autotune discipline carried forward from the user's mindX AutoTune work conflicts with any framework default that turns on JIT autotune (Triton autotune in vLLM cold-start, `torch.compile(mode='max-autotune')` Inductor JIT, MIOpen find-mode); the mindXtrain config schema must explicitly disable JIT autotune in production runs and force AOT compilation paths (AOTriton, hipBLASLt offline tune cache, MIOpen `.kdb` pre-warming). **Third**, the BANKON ENS allocation and x402-Algorand billing flow is named here as a first-class integration; if the prior files described mindXtrain as standalone, this needs to be elevated from optional to mandatory in the publish step, and the cypherpunk2048 immutability rule (no upgradeable proxies, no EOA admin) must propagate into the on-chain registry contract above. + +## Hackathon timeline (today is May 5 2026) + +The build window has roughly five days of active engineering left. **May 5 (today)**: register on lablab.ai, register the AMD AI Developer Program for the $100 cloud credits, provision an MI300X via TensorWave bare-metal or AMD Developer Cloud, snapshot the `rocm/primus:v26.2` digest, scaffold the `mindxtrain/` repo with the directory tree above, ship the Pydantic config schema and the Typer CLI, post a Build-in-Public X teaser tagging @lablab @AIatAMD with a screenshot of `mindxtrain init`. **May 6**: implement the autotune layer — attention probe, GEMM probe, RCCL probe, plan emitter — and run the first end-to-end Qwen3-8B SFT-LoRA on a single MI300X with the demo YAML; capture tok/s and MFU baseline numbers. **May 7**: implement the dataset pipeline (MinHash, SemDeDup, packing, sharding), the eval harness wrapper, and the Quark FP8 PTPC quantization path; ship a second X post showing the autotune-driven config diff with a benchmark vs untuned baseline. **May 8**: implement the publish layer (HF, Lighthouse, mindX register, AgenticPlace listing, BANKON ENS subname), the x402 billing stub, the receipt manifest, and deploy the public HF Space inside `lablab-ai-amd-developer-hackathon`; record the demo video and write the final pitch deck. **May 9**: travel to SF or stream to the on-site session, finalize the lablab submission form by 14:00 local Saturday, drive social engagement on the HF Space for the most-likes prize. **May 10**: live demo on stage, awards, post-mortem. Submit the written ROCm developer-experience feedback note immediately after submission to lock in Build-in-Public eligibility. + +## Closing synthesis + +mindXtrain wins this hackathon by being the only entry that operationalizes the *entire* AMD training stack — ROCm 7.2.1, AOTriton, AITER, Composable Kernel, hipBLASLt, RCCL, Optimum-AMD, Quark, Primus-Turbo, vLLM-ROCm, SGLang — behind a single CLI, with a defensible **AOT autotune** that nobody else in the ecosystem ships, against the **latest open Qwen3.6 checkpoints** (which the user correctly remembered as real and which most public summaries lag), with cost numbers that make MI300X look obviously cheaper than H100 for the workloads in scope. The cypherpunk2048 discipline — Apache 2.0, flat snake_case, Podman, immutable contracts, no proxies, no EOA admin — is preserved end-to-end, and the integration plumbing into mindX, AgenticPlace, BANKON-ENS and x402-Algorand is wired without locking in a proprietary dependency. The remaining engineering is entirely tractable in the five-day window, and every external dependency named in this blueprint has either a verified ROCm-first-class status or a documented community workaround. Ship it. + +--- + +### Citations + +**Hackathon**: lablab.ai/ai-hackathons/amd-developer · amd.com/en/developer/resources/technical-articles/2026/build-across-the-ai-stack--join-the-amd-x-lablab-ai-hackathon-.html · luma.com/afz0aeq8 · huggingface.co/lablab-ai-amd-developer-hackathon · lablab.ai/ai-articles/from-zero-to-ai-builder-amd-developer-program · lablab.ai/ai-tutorials/amd-developer-cloud-host-llm-vllm + +**Frameworks**: github.com/axolotl-ai-cloud/axolotl · github.com/AI-DarwinLabs/axolotl · github.com/hiyouga/LLaMA-Factory · github.com/unslothai/unsloth · github.com/pytorch/torchtune · github.com/huggingface/trl · github.com/huggingface/peft · github.com/huggingface/accelerate · github.com/huggingface/optimum-amd · github.com/microsoft/DeepSpeed · github.com/AMD-AGI/Primus · github.com/AMD-AGI/Primus-Turbo · github.com/vllm-project/vllm · github.com/sgl-project/sglang + +**AMD stack**: rocm.docs.amd.com/en/latest/about/release-notes.html · rocm.docs.amd.com/en/latest/compatibility/compatibility-matrix.html · rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html · github.com/ROCm/bitsandbytes (rocm_enabled_multi_backend) · github.com/Dao-AILab/flash-attention · github.com/ROCm/aotriton · github.com/ROCm/aiter · github.com/ROCm/composable_kernel · github.com/amd/Quark · quark.docs.amd.com · rocm.blogs.amd.com/software-tools-optimization/mi300x-rccl-xgmi · rocm.blogs.amd.com/software-tools-optimization/vllm-omni · rocm.blogs.amd.com/software-tools-optimization/llm-grpo-rocm · rocm.blogs.amd.com/artificial-intelligence/qwen3-day0-amd · rocm.blogs.amd.com/artificial-intelligence/torchtune · www.amd.com/en/products/accelerators/instinct/mi300/mi300x.html · www.amd.com/en/products/accelerators/instinct/mi350.html · www.amd.com/en/developer/resources/technical-articles/2026/day-0-support-for-qwen-3-5-on-amd-instinct-gpus.html · huggingface.co/blog/huggingface-and-optimum-amd · huggingface.co/blog/microsoft-collaboration · huggingface.co/amd · huggingface.co/docs/optimum/en/amd/index · arxiv.org/pdf/2510.27583 · newsletter.semianalysis.com/p/mi300x-vs-h100-vs-h200-benchmark-part-1-training + +**Qwen3**: arxiv.org/abs/2505.09388 · arxiv.org/abs/2509.17765 · arxiv.org/abs/2511.21631 · qwenlm.github.io/blog/qwen3 · qwen.ai/blog?id=qwen3-next · qwen.ai/blog?id=qwen3.5 · qwen.ai/blog?id=qwen3.6-27b · qwen.ai/blog?id=qwen3.6-35b-a3b · github.com/QwenLM/Qwen3 · github.com/QwenLM/Qwen3-Coder · github.com/QwenLM/Qwen3-VL · github.com/QwenLM/Qwen3-Omni · github.com/QwenLM/Qwen3.6 · huggingface.co/Qwen · huggingface.co/Qwen/Qwen3-8B · huggingface.co/Qwen/Qwen3-32B · huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507 · huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct · huggingface.co/Qwen/Qwen3-Coder-480B-A35B-Instruct · huggingface.co/Qwen/Qwen3.6-27B · huggingface.co/Qwen/Qwen3.6-35B-A3B · qwen.readthedocs.io/en/latest/getting_started/quickstart.html · qwen.readthedocs.io/en/latest/getting_started/concepts.html · www.lmsys.org/blog/2026-02-11-Qwen-latency · docs.unsloth.ai/models/qwen3-how-to-run-and-fine-tune · unsloth.ai/docs/models/qwen3.6 \ No newline at end of file diff --git a/docs/blueprints/mindXtrain_ Production Blueprint for the AMD and lablab.ai Hackathon.pdf b/docs/blueprints/mindXtrain_ Production Blueprint for the AMD and lablab.ai Hackathon.pdf new file mode 100644 index 0000000000000000000000000000000000000000..d4a13f575eee51701818b2c8ff32492ee4de7a13 --- /dev/null +++ b/docs/blueprints/mindXtrain_ Production Blueprint for the AMD and lablab.ai Hackathon.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0ab7d4053c77df1e291a7f48dace094e39a6d09a79b6176050ada5f36d97c5db +size 875047 diff --git a/docs/cli.md b/docs/cli.md new file mode 100644 index 0000000000000000000000000000000000000000..e57885902b7b109e1d70490ebebfde422d5b4986 --- /dev/null +++ b/docs/cli.md @@ -0,0 +1,208 @@ +# CLI reference + +The `mindxtrain` Typer app: 9 verbs (8 top-level + a `dataset` subgroup). +Every verb that consumes a YAML config validates it against the +[10-section schema](yaml_schema.md) before doing anything else. + +``` +mindxtrain [--version] [options] +``` + +All verbs dispatch into real Python in the canonical `mindxtrain.*` modules. +Verbs that require optional dependencies surface a clean +`run `uv sync --extra `` hint and exit `3`. + +## Global options + +| Flag | Purpose | +|--------------|--------------------------------------| +| `--version` | Print the version and exit. | +| `--help` | Show help for the top-level command. | + +## `init` — scaffold a YAML + +Render a built-in recipe to disk. + +``` +mindxtrain init [--template ] [--out ] [--list] +``` + +| Option | Default | Description | +|-----------------------|----------------------|--------------------------------------------------| +| `--template`, `-t` | `qwen3_8b_sft_lora` | recipe name; see `--list` | +| `--out`, `-o` | `run.yaml` | output path | +| `--list` | _flag_ | print every built-in recipe and exit | + +Available recipes (12 total): + +``` +instella_3b_lora qwen3_30b_a3b_lora qwen3_32b_dpo +qwen3_32b_full_fsdp qwen3_32b_grpo qwen3_32b_orpo +qwen3_6_27b_lora qwen3_6_35b_a3b_lora qwen3_8b_cpt +qwen3_8b_sft_full qwen3_8b_sft_lora qwen3_vl_8b_sft +``` + +```bash +$ uv run mindxtrain init --template qwen3_8b_sft_lora --out run.yaml +wrote run.yaml (2785 bytes, recipe='qwen3_8b_sft_lora') +``` + +## `bench` — run the 60-second AOT autotune probe + +The differentiator. See [autotune.md](autotune.md) for probe taxonomy. + +``` +mindxtrain bench [--gpu N] [--out ] [--dry-run] +``` + +| Option | Default | Description | +|--------------|------------------------|---------------------------------------------------------------------| +| `--gpu` | `0` | HIP/ROCm device index | +| `--out`, `-o`| `autotune_plan.json` | output path | +| `--dry-run` | _flag_ | skip GPU probes; emit a synthetic reference plan (CPU-safe) | + +`--dry-run` is the CPU-only path used in tests and CI. A real +`mindxtrain bench --gpu 0` requires `torch` (`--extra ml`) and an MI300X +with ROCm 7.2.1; if torch is unavailable, the attention probe gracefully +falls back to the canonical `ck` default. + +## `train` — dispatch a training run + +``` +mindxtrain train [--plan ] [--out ] +``` + +| Option | Default | Description | +|--------------|------------------------|--------------------------------------------------------------| +| `--plan` | (uses dry-run plan) | autotune plan JSON from `mindxtrain bench` | +| `--out`, `-o`| `./out/runs` | output root for `/` directory | + +Loads the YAML, dispatches to `train.backend` (`axolotl`, `unsloth`, +`torchtune`, `primus`). The Axolotl path subprocess-wraps +`accelerate launch -m axolotl.cli.train`. Plan-derived env vars +(`PYTORCH_ROCM_ARCH=gfx942`, `HSA_NO_SCRATCH_RECLAIM=1`, etc.) are injected +before launch. + +Requires `--extra ml` plus the chosen backend on `PATH`. + +Exits `3` with a clean install hint if `accelerate` (or the backend) is missing. + +## `dataset prep` — run the dataset pipeline + +``` +mindxtrain dataset prep [--out ] +``` + +Streams the HF dataset (`datasets`), runs heuristic + optional MinHash/SemDeDup +filters, tokenizes (`AutoTokenizer`), packs to `data.seq_len`, emits sharded +`.tar` files. Pin the resulting tars via +`mindxtrain.storage.lighthouse` or `mindxtrain.storage.ipfs`. + +Requires `--extra ml` (datasets, transformers). + +## `eval` — run lm-evaluation-harness + +``` +mindxtrain eval [--checkpoint ] +``` + +| Option | Default | Description | +|--------------|---------------------------------------------------|----------------------------| +| `--checkpoint`, `-c` | `./out/runs//checkpoint` | path to the checkpoint dir | + +Subprocess-wraps `lm_eval --model hf --tasks `. Tasks come from +`cfg.eval.harness.tasks`. Output JSON written under +`/eval/lm_eval.json`. Summary printed via +`mindxtrain.eval.harness.parse_summary`. + +Requires `--extra eval`. + +## `quantize` — Quark FP8 / MXFP4 + +``` +mindxtrain quantize [--checkpoint ] +``` + +Wraps `python -m amd_quark.quantize` with `--scheme fp8_e4m3` (default) or +`--scheme mxfp4` (CDNA 4 / MI350X+). Output is a `quantized/` directory next +to the checkpoint, vLLM-loadable. + +Requires the `amd-quark` package — typically only available inside the +`rocm/primus:v26.2` container or per +[Quark docs](https://quark.docs.amd.com/). + +## `serve` — print the vLLM-ROCm launch command + +``` +mindxtrain serve [--checkpoint ] +``` + +Builds the `vllm serve` argv from `cfg.serve` and prints it. We deliberately +don't `exec` — the user pipes it into their own orchestrator (or +`ops/compose/compose_dev.yaml`). + +The chat-template parsers map per `serve.reasoning_parser` (`qwen3` for Qwen3, +`deepseek_r1` for DeepSeek-style) and `serve.tool_call_parser` (`hermes`, +`qwen3_coder`). + +## `publish` — push to HF + Lighthouse + register + +``` +mindxtrain publish --manifest [--skip-hf] [--skip-pin] +``` + +1. `mindxtrain.storage.hf_hub.publish_to_hf` — uploads the checkpoint dir to + HuggingFace Hub (uses `HF_TOKEN`). `--skip-hf` to bypass. +2. `mindxtrain.storage.lighthouse.publish_to_lighthouse` — pins to + Lighthouse via direct httpx POST (uses `LIGHTHOUSE_API_KEY`). Falls back + to a stub `cid://stub-...` derived from the checkpoint's BLAKE3 if the + key is unset. `--skip-pin` to bypass entirely. +3. `mindxtrain.deploy.api_client.register_with_mindx` — POSTs the run-id / + HF URL / CID to `MINDXTRAIN_API_BASE_URL/v1/agents`. Skipped gracefully + if the endpoint isn't reachable. +4. The manifest JSON file is updated in-place with the resulting `hf_repo_id` + and `lighthouse_cid` fields. + +## `receipt` — verify a provenance manifest + +``` +mindxtrain receipt [--config ] +``` + +Loads the manifest and prints the run-id + BLAKE3 fields. With `--config`, +also re-hashes the on-disk artifacts (`config_yaml`, `dataset`, `checkpoint`, +`eval_json`) and emits a per-field pass/fail dict — exits `0` if every hash +verifies, `2` if any drift is detected. + +```bash +$ uv run mindxtrain receipt out/runs//manifest.json --config run.yaml +{ + "config_yaml": true, + "dataset": true, + "checkpoint": true, + "eval_json": true +} +``` + +## Exit-code summary + +| Code | Meaning | +|------|-----------------------------------------------------------------| +| 0 | Success. | +| 1 | Bad input — missing file, hash mismatch, schema error. | +| 2 | Verify failed — at least one BLAKE3 field doesn't match disk. | +| 3 | Optional dep missing — install with `uv sync --extra `. | + +## Where the verbs live + +| Verb | Module | +|-------------------|---------------------------------------------------------------------------------------| +| `init` | `mindxtrain.cli.main.init` + `mindxtrain.config.loader.render_recipe` | +| `bench` | `mindxtrain.cli.main.bench` + `mindxtrain.autotune.benchmark.run_autotune` | +| `train` | `mindxtrain.cli.main.train` + `mindxtrain.train.dispatch.dispatch_training` | +| `dataset prep` | `mindxtrain.cli.main.dataset_prep` + `mindxtrain.data.{curate,filter,tokenize,pack}` | +| `eval` | `mindxtrain.cli.main.eval_` + `mindxtrain.eval.harness.run_lm_eval` | +| `quantize` | `mindxtrain.cli.main.quantize` + `mindxtrain.deploy.quark.quark_fp8` | +| `serve` | `mindxtrain.cli.main.serve` + `mindxtrain.deploy.vllm_launcher.build_vllm_command` | +| `publish` | `mindxtrain.cli.main.publish` + `mindxtrain.storage.{hf_hub,lighthouse}` + `mindxtrain.deploy.api_client` | +| `receipt` | `mindxtrain.cli.main.receipt` + `mindxtrain.provenance.verify.verify_receipt` | diff --git a/docs/coach.md b/docs/coach.md new file mode 100644 index 0000000000000000000000000000000000000000..995e3a0fb8cd950b5e4ffd2bac118ff6c39a1d30 --- /dev/null +++ b/docs/coach.md @@ -0,0 +1,260 @@ +# mindxtrain Coach (UI) + +A single-page web UI that walks judges and new contributors through the mindxtrain pipeline without needing a GPU. Bundled inside the mindxtrain.operator FastAPI app at `/coach/`. + +## Why it exists + +Hackathon judges have ~3 minutes per submission. The Coach lets them poke at the differentiator (the 60-second AOT autotune) and the cost story (4× cheaper than H100) interactively, in a browser, without setting up ROCm. + +## Boot + +```bash +uv run uvicorn mindxtrain.operator.app:app --host 0.0.0.0 --port 8080 +``` + +Open http://localhost:8080 — the root path redirects to `/coach/`. + +The Coach works **without** a backend GPU: +- The autotune endpoint runs `run_autotune(dry_run=True)` and emits the reference plan. +- The compile endpoint produces a real Axolotl YAML against the dry-run plan. +- The cost calculator is pure arithmetic. + +The chat panel stays disabled until `MINDXTRAIN_BACKEND=vllm` is set and a vLLM-ROCm server is reachable. + +## Layout + +``` +mindxtrain/operator/coach/ +├── __init__.py # exports the FastAPI router +├── api.py # routes (recipes / bench / compile / cost / health / +│ # runs / metrics / receipt / sea-decision / mei / diagnostics) +├── run_metrics.py # 1 Hz system-metrics sampler (psutil + /proc) +├── chronos_client.py # mindX promised-time client +└── static/ + ├── index.html # multi-card SPA shell (preflight → … → train → receipt → chat) + ├── style.css # minimal dark-friendly CSS, AMD orange accent + └── coach.js # vanilla JS state machine, no framework +``` + +The Coach mounts under `/coach/`; static assets are at `/coach/static/*`. The UI +has grown well past the original five-step demo: it now covers preflight, hardware +detection, dream-corpus stats, recipe pick, autotune, compile, **live training with +diagnostic feedback**, the **verifiable receipt**, MEI scoring, cost, deploy, and chat. + +## Routes + +| Method | Path | Body / Query | Returns | +|--------|-------------------------------------|---------------------------|--------------------------------------------| +| GET | `/` | — | 307 redirect to `/coach/` | +| GET | `/coach/` | — | `index.html` | +| GET | `/coach/static/{path}` | — | static files | +| GET | `/coach/api/recipes` | — | `list[RecipeSummary]` (12 items) | +| GET | `/coach/api/recipes/{name}` | — | `{ name, yaml, summary }` | +| POST | `/coach/api/bench` | (none) | `AutotunePlan` (dry-run reference) | +| POST | `/coach/api/compile` | `{recipe, plan?}` | `{recipe, config_summary, plan, axolotl_yaml, overrides}` | +| POST | `/coach/api/cost` | `{gpus, hours, safety_margin}` | `{mi300x, h100, h200, speedup_vs_h100_x}` | +| GET | `/coach/api/health` | — | `{coach_version, chat_backend_ready, recipes_available}` | +| POST | `/coach/api/runs/launch` | `{recipe, plan?, out_dir?}` | `Run` snapshot (spawns training) | +| GET | `/coach/api/runs/{id}/events` | — | SSE stream (`status`/`step`/`eval`/`log`/`metrics`/`energy`) | +| GET | `/coach/api/runs/{id}/metrics` | `?since=` | system-metrics backfill for the sparklines | +| GET | `/coach/api/receipt/{run_id}` | — | `ReceiptView` — re-verified BLAKE3 hashes + `verified` | +| GET | `/coach/api/sea-decision` | — | mindX SEA autonomous-training gate state | +| GET | `/coach/api/mei/score/{run_id}` | — | `MEIScoreView` (mindX Efficiency Index) | +| GET | `/coach/api/diagnostics/live` | — | host load / RAM% / disk% / operator RSS | + +The full schema is rendered at `/docs` (Swagger). + +## Live training diagnostics + +The **Train (live)** card is the accurate, real-time depiction of a run. Events +arrive over Server-Sent Events (`/coach/api/runs/{id}/events`) — `step`, `eval`, +`log`, and 1 Hz `metrics` — and drive these surfaces: + +- **Session headline** — status badge, wall-clock + CPU-time elapsed, throttle%, + last loss, freshest eval. The at-a-glance "is it healthy" line. +- **Phase + progress** — friendly phase narration ("Loading base model…", + "Training…", "Saving checkpoint…") plus a progress bar with `step N / total · ETA`, + driven by `StepEvent.total_steps`. +- **Loss curve** (Chart.js) — dual-axis loss (orange) + `mean_token_accuracy` + (green, NaN-gapped where a backend omits it). The primary "is it learning" signal. + +Because a real MI300X run logs **thousands of steps**, the heavy detail is kept +accurate but compressed behind accordions, with the truncation always shown — never +silent: + +- **Loss curve** keeps a rolling window of the last `MAX_CHART_POINTS` (1500) points; + once it rolls, a `showing last 1500 of N steps` note appears under the chart. +- **Per-step metrics** (step, loss, acc, entropy, lr, grad_norm) live in a collapsed + `
` accordion; the DOM table caps at 50 rows but the summary reports the + true total — `per-step metrics (N steps · last 50 shown)`. +- **train.log (live tail)** is a `
` accordion that auto-folds older lines + and shows a running `(N lines)` count, capping the DOM at `MAX_LOG_LINES` (2000) + and labelling `· oldest dropped` once it does. +- **System metrics** — five d3 sparklines (host cpu%/ram%/load, trainer rss MB, + trainer cpu-s/s) sampled at 1 Hz, in their own `
` (open by default). + +This keeps the page legible on a laptop while the underlying data stays faithful. + +## Verifiable receipt card + +When a run finishes, the operator emits `manifest.json` (BLAKE3 of the config +snapshot, checkpoint, and the frozen `AutotunePlan`) into the run directory. The +**Verifiable receipt** card fetches `/coach/api/receipt/{run_id}`, which re-hashes +the on-disk artifacts and returns a `verified` flag plus the per-field checks. A +`verified ✓` badge and the truncated hashes render in the card; the same check runs +from a shell via `mindxtrain receipt out/runs//manifest.json --config .yaml`. +Binding the AutotunePlan hash to the checkpoint is the AOT-as-verification primitive — +it proves which compiled backend/heuristic/RCCL config produced the weights. + +## Create script + imprint (actor / persona / script) + +mindXtrain (and Coach) **train models**. The model is an **actor**; an actor has a +**persona** (identity / voice) and a **script** (the training examples — the +"impression"). The **Create script** card authors a small script in the browser and +saves it as `source: local` JSONL the recipes ingest. + +- **`POST /coach/api/datasets`** — `{name, persona_name, system_prompt, voice_examples, + exchanges:[{user,assistant}], seed_voice}` → writes + `out/datasets//script.jsonl` (override the root with `MINDXTRAIN_DATASETS_DIR`). + `GET /coach/api/datasets` lists them; `GET /coach/api/datasets/{name}` previews. +- **`GET /coach/api/persona`** — pre-fills the form from `MINDXTRAIN_PERSONA_PATH` + (clean-room: recognised fields only, never copies mindX bytes). +- Point the **`mindx_persona_imprint_local`** recipe's `data.path` at the saved script + and train the tiny actor (`trl_local`, CPU or local GPU). + +**Imprint = recall, before vs after.** Pose the script's own user-turns back to the +actor and compare the base model (before) with the trained adapter (after) against the +script's assistant voice: + +```bash +mindxtrain imprint mindxtrain/train/recipes/mindx_persona_imprint_local.yaml +``` + +prints an `ImprintReport` (`before_voice`, `after_voice`, `imprint_delta`, `shift`, +`imprinted`); exit 4 if no imprint took. `POST /coach/api/imprint/score` scores supplied +utterances without blocking the event loop on inference. `mindxtrain imprint +--trigger-dream` hands the imprinted actor to mindX's `machine.dream` 8-hour cycle (via +`MINDXTRAIN_API_BASE_URL` `/v1/dream/ingest`, else a `data/incoming/` inbox drop under +`MINDXTRAIN_MINDX_HOME`) — clean-room, an artifact pointer, never mindX code. + +## Create script — personas + skills + +The **Create script** card authors a `source: local` JSONL from a persona and toggleable +skills: + +- **Built-in personas** (`GET /coach/api/personas`) — `codephreak`, `assistant`, `mentor` + (`mindxtrain.data.personas.BUILTIN_PERSONAS`). Pick one, or use the custom fields. +- **Skills** — toggle **Software Engineer / Platform Architect / Bash / Solidity** to mix + each skill's in-domain exchanges into the script (`mindxtrain.data.personas.SKILLS`, + `compose(persona, skills)`). A skill is a system-prompt addendum + representative turns. +- `POST /coach/api/datasets` composes persona + skills + your exchanges and returns the row + count plus **training params auto-derived from the dataset size** + (`derive_training_params` — small scripts overfit to imprint: more epochs, grad_accum 1). + +## Build an Ollama Modelfile (separate window) + +The **Build Modelfile…** button (in the train card's push-to-ollama row) opens a standalone +builder at `/coach/modelfile` (a separate browser window), pre-filled for the current run: + +- Every instruction is a toggle: `FROM` (required), `SYSTEM`, `TEMPLATE`, `ADAPTER`, + `LICENSE`, `REQUIRES`, plus `MESSAGE` examples and `stop` sequences. +- Every `PARAMETER` (`num_ctx`, `temperature`, `top_k`, `top_p`, `min_p`, `repeat_penalty`, + `mirostat`, `seed`, … — the full catalogue from `GET /coach/api/modelfile/params`) is a + toggle + input, rendered dynamically with defaults and ranges. +- `POST /coach/api/modelfile/build` renders the `Modelfile` text; + `POST /coach/api/modelfile/create` runs `ollama create `. Core logic: + `mindxtrain.deploy.modelfile` (`ModelfileSpec`, `render_modelfile`, `create_model`). + +## The core storyboard + +The original CPU-only demo path, top-to-bottom (the cards above and below it — +preflight, hardware, dream-corpus, live training, receipt, MEI, deploy — flank it): + +1. **Pick a recipe** — clickable grid of all built-in recipes; the selected one's YAML expands inline. +2. **Run the autotune probe** — single button; shows the `AutotunePlan` JSON plus a six-chip summary (`attention=ck`, `gemm=hipblaslt_default`, `rccl=1gpu_noop`, …). +3. **Compile to Axolotl YAML** — translates `(recipe, plan)` into the trainer-side YAML, surfaces the plan-driven overrides as chips above the YAML. +4. **Train (live)** — spawns the run and streams the diagnostic feedback described in [Live training diagnostics](#live-training-diagnostics); on a CPU box the `trl_cpu` lane trains a small model in-process so the whole loop is demoable without a GPU. The `trl_local` lane is the device-aware variant — it uses a local consumer GPU (CUDA or ROCm Radeon) when present and falls back to CPU otherwise, so the same recipe runs on a laptop or a gaming GPU. `recommend_lane` sends an Instinct/MI300X card to `axolotl_amd` and any other local GPU to `trl_local`. +5. **Verifiable receipt** — the `verified ✓` badge + bound hashes appear the moment the run completes. +6. **Cost vs H100** — sliders for GPUs and hours; emits a three-row comparison table (MI300X / H100 / H200) with a headline like "MI300X is 5.4× cheaper than the H100 baseline". +7. **Try the model** — chat panel that proxies to `/v1/chat/completions`. Stays disabled and explains why until the backend reports ready; a **Check now** button re-probes on demand. + +## Demo storyboard + +``` +0:00–0:30 open localhost:8080, point at the three-stage diagram in the header +0:30–1:00 click qwen3_8b_sft_lora; show the YAML preview +1:00–2:00 click "Run autotune (dry-run)"; show the plan JSON streaming in + and the six-chip summary populating +2:00–3:00 click "Compile"; show the Axolotl YAML diff (the autotune + plan's attention_backend appears as flash_attn_backend=ck) +3:00–4:00 drag the cost slider to 1 GPU × 1.5 hours; show the + "5× cheaper than H100" headline +4:00–5:00 the chat panel; show that it's gracefully disabled because + the backend isn't booted, then close +``` + +Every Coach interaction is screen-recordable on a CPU-only laptop. The MI300X work happens behind the scenes for the actual training run; the Coach surfaces the *outcome* judges care about. + +## Dependencies + +- FastAPI — already a dep of mindxtrain.operator. +- `mindxtrain` — workspace dep added to `pyproject.toml` so the Coach can call `mindxtrain.config.loader.list_recipes()`, `mindxtrain.autotune.benchmark.run_autotune()`, and `mindxtrain.train.compile_axolotl_yaml()`. +- `pyyaml` — added for the recipe→summary path. + +No JavaScript framework, no build step, no node_modules. + +## Tests + +`tests/test_coach_api.py` covers every endpoint via FastAPI's `TestClient`: + +- root redirects to `/coach/` +- index serves HTML with the right `` +- static files serve (CSS + JS) +- recipes list returns 12 items +- recipe detail returns YAML + summary +- 404 on unknown recipe +- bench returns a valid `AutotunePlan` +- compile returns Axolotl YAML + overrides; 404 on unknown recipe +- cost returns three breakdowns; 422 on invalid input +- health endpoint reports `recipes_available=12` +- `/health` mentions `coach_url=/coach/` +- the train card exposes the diagnostic accordions (`metrics-table-wrap`, + `metrics-table-count`, `train-log-count`, `chart-window-note`) and coach.js wires + the rolling-window cap + counters (`MAX_CHART_POINTS`, `_updateMetricsTableCount`, + `_updateLogCount`) +- the receipt card + loader are present (`step-receipt`, `loadReceiptForRun`) + +The live-training + receipt round-trip is covered in `tests/test_coach_receipt_api.py` +(canned spawn → `/coach/api/receipt/{id}` returns `verified=True`). + +Run with `uv run pytest tests/test_coach_api.py -v`. + +## Customizing for the demo + +Tweak the cost-comparison constants in `mindxtrain/operator/coach/api.py`: + +```python +H100_USDC_PER_HOUR = 4.00 +H200_USDC_PER_HOUR = 6.00 +``` + +The MI300X rate is sourced from `mindxtrain.budget.pricing.MI300X_USDC_PER_HOUR` ($1.99/hr, AMD Developer Cloud list price). + +## Streaming chat + ollama controls (Try the model) + +The **Try the model** card chats with a local model and **streams the response +token-by-token** — the [AI SDK](<Vercel AI SDK 6_ A Framework-Agnostic Deep Dive (June 2026).md>) +text-stream pattern, implemented in vanilla JS (no build step): `coach.js` consumes a +`text/event-stream` whose `data:` lines are JSON token deltas, ending with `data: [DONE]`. + +- **`POST /coach/api/chat/stream`** — `{model, messages, max_tokens?}` → SSE token stream. + Relays `backend.stream_chat()` (the OpenAI-compatible streaming the ollama/vLLM backends + already speak). Backend errors are surfaced in-stream (`event: error`), never as a mid-stream 500. +- **Model picker** — populated from `GET /coach/api/models` (local models sorted ahead of + `:cloud`), so the chat no longer defaults to a cloud model that silently returns nothing. +- **ollama controls** — `GET /coach/api/ollama/status` + `POST /coach/api/ollama/{start,stop}` + start/stop the local `ollama serve` and report its state; `↻ models` re-lists. + +For a remote vLLM-ROCm endpoint instead, set `MINDXTRAIN_BACKEND=vllm` + +`MINDXTRAIN_VLLM_BASE_URL`; the same streaming chat works against it +(see [HANDOFF.md](HANDOFF.md) §§ 5–6). diff --git a/docs/dcoach.md b/docs/dcoach.md new file mode 100644 index 0000000000000000000000000000000000000000..042e9c983a38d8ac59c44e0e6ff330ab9e42288f --- /dev/null +++ b/docs/dcoach.md @@ -0,0 +1,99 @@ +# dcoach — prove a CPU-trained model recalls its training + +`dcoach` is the decentralized-aware extension of the [Coach](coach.md). It closes +mindXtrain's founding loop: **author a dataset → imprint a persona on a tiny model +(CPU) → prove the model recalls the training → let governance rule on it → feed the +verdict back into autotune.** It is also the on-ramp to the 2026 decentralized-training +landscape (see [the deep dive](decentralized-training-deep-dive-2026.md)). + +Open it at **`/coach/dcoach`** (linked from the Coach header). + +## The proof loop + +``` +persona + skills ─► script.jsonl ─► imprint-train (trl_local, CPU) + │ + ┌─────────────────────────────┘ + ▼ + probe recall ──► classroom (before vs after) ──► boardroom (rule) ──► feedback + (base vs adapter) recall ↑? persona kept? approve / reject tune next run +``` + +1. **Author** — a persona (e.g. `codephreak`) plus optional skills (software engineer, + platform architect, bash, solidity) is composed into chat rows + (`data/scripts.py::build_script_rows`). Each row carries the persona **system prompt** + + a user→assistant turn. +2. **Imprint-train** — a tiny actor (default `HuggingFaceTB/SmolLM2-135M`) is LoRA-trained + on the script on the CPU lane (`train/backend_trl_cpu.py::run_trl_local`). The autotune + plan is frozen AOT — no JIT autotune in the loop. +3. **Probe recall** — `eval/imprint.py::probe_recall` generates the actor's answer to each + inquiry **before** (base model) and **after** (base + adapter). The probe prepends the + *same persona system prompt the adapter trained under*, so the comparison measures what + the imprint actually learned rather than penalising a missing conditioning turn. +4. **Classroom** — `governance/classroom.py::evaluate_classroom` scores before vs after + against the persona baseline (clean-room [llama-style evaluators](#clean-room-eval-tools)): + recall up? persona maintained? `passed = persona_maintained and pairwise ≥ 0.5`. +5. **Boardroom** — the classroom graduation becomes a motion; a board (any-N, preset or + model-backed) rules **approve / reject**. A disputed board is settled by a prime-sized + **dojo**. +6. **Feedback** — `autotune/feedback.py` records `(run_id, params, classroom_score, + outcome)` to an append-only ledger and `suggest_next_params` nudges the next run: a weak + or rejected imprint trains harder (more epochs, `grad_accum=1`); a clean pass holds. + `suggest_from_history` feeds the nudge back into `derive_training_params`. + +The whole chain is `governance/proof_loop.py::run_proof_loop`, streamed phase-by-phase to +the UI via **`POST /coach/api/dcoach/run`** (SSE). It is heavy (real CPU training + +generation) — expect a few minutes per run. + +## Clean-room eval tools + +`eval/llama_evals.py` reimplements the *behaviour* of LlamaIndex's evaluators (MIT) from +their public contract — never copied. Each returns an `EvalScore{score∈[0,1], passing, +reasoning, method}`: + +| Evaluator | What it measures | Backed by | +|-----------|------------------|-----------| +| `SemanticSimilarityEvaluator` | embedding/lexical closeness of two texts | `eval/imprint.py::_voice_similarity` | +| `CorrectnessEvaluator` | response vs reference (LLM judge, 1–5 → [0,1]) | `governance/panel.chat_once` | +| `PairwiseEvaluator` | after-utterance better than before toward the persona | judge (A/B/TIE) | +| `GuidelineEvaluator` | rubric/agenda compliance | LLM judge | + +Endpoints: `POST /coach/api/classroom/evaluate`, `POST /coach/api/eval/prompt`, +`POST /coach/api/autotune/feedback`. + +## Prompt tools — test cheap, promote if it wins + +**`/coach/prompts`** treats prompting as the cheapest pseudo-training: craft a system +prompt + few-shot demonstrations, run them against a base model (streaming, **no +training**), evaluate the outcome with the eval tools, and only if it's advantageous +**make it permanent** by baking the prompt + demonstrations into an Ollama Modelfile +(`POST /coach/api/modelfile/create`). Non-permanent experiment → promote on results. + +## How mindXtrain fits decentralized training + +The dcoach page renders a read-only panel (`GET /coach/api/decentralized`) mapping each +2026 network to where mindXtrain plugs in. mindXtrain **does not mine** on any of them — +every one is CUDA-first / hardware-gated. Instead it exposes a *verifiable, payable* +training surface compatible with their verification primitives: + +| mindXtrain primitive | Maps to | +|----------------------|---------| +| AOT-only autotune plan (bit-reproducible run) | Gensyn **Verde + RepOps** training verification | +| BLAKE3 verifiable receipt (`mindxtrain receipt`) | TOPLOC / checkpoint-hash verification; Templar **Gauntlet** auditing | +| x402-metered training job | Per-job crypto metering — unbuilt territory across all networks | +| AgenticPlace / ERC-8004 registration | Pluralis unextractable-model ownership / on-chain attribution | + +Networks covered: **Prime Intellect** (open stack, RL post-training), **Templar · Bittensor +SN3** (Covenant-72B, the only live incentivized training market), **Nous · Psyche** +(DisTrO on Solana), **Gensyn** (verification-first, Verde — the closest match), **Pluralis · +Node0** (model-parallel over WAN, unextractable models). Full analysis in +[decentralized-training-deep-dive-2026.md](decentralized-training-deep-dive-2026.md) and +[mindxtrain-llm-training-landscape-2026.md](mindxtrain-llm-training-landscape-2026.md). + +## Why this matters + +This is mindXtrain's **first-run proof**: that a model trained on the CPU lane actually +*recalls* what it was trained on — measured, ruled on, and fed back, not asserted. It is +also the bridge to the [mindX self-training loop](../README.md): the same loop that imprints +`codephreak` here consumes the `machine.dream` corpus to produce the small model mindX falls +back to. diff --git a/docs/decentralized-training-deep-dive-2026.md b/docs/decentralized-training-deep-dive-2026.md new file mode 100644 index 0000000000000000000000000000000000000000..058dc244cd78245807420c9a64e166ef3a0828e6 --- /dev/null +++ b/docs/decentralized-training-deep-dive-2026.md @@ -0,0 +1,182 @@ +# Decentralized Training: The Complete Landscape (Mid-2026) + +**A deep-dive companion to the mindXtrain training-stack survey** · Compiled June 2026 + +--- + +## TL;DR + +- **Decentralized training crossed its credibility threshold in 2025–2026.** Three landmark proofs: Templar's **Covenant-72B** (March 10, 2026 — 72B params, ~1.1T tokens, 70+ permissionless nodes over commodity internet, MMLU 67.1, ~LLaMA-2-70B class), Nous **Psyche/Consilience-40B** (largest internet pre-training run by parameter×token scale, coordinated on Solana), and Pluralis **Node0-7.5B** (first public *model-parallel* internet pretraining: 1,642 GPUs, 300+ participants, 198 cities, 36B tokens in 3 weeks). +- **The algorithmic unlock is communication compression**: DiLoCo-family infrequent synchronization (~500× less communication), Streaming DiLoCo (two orders of magnitude bandwidth reduction), SparseLoCo (top-k sparsification + 2-bit quantization to 1–3% density, ~97% gradient compression — what powered Covenant-72B), DeMo/DisTrO (DCT + top-k momentum decoupling, up to 85× less data per GPU), and Pluralis Protocol Models (99% activation compression enabling model parallelism over WAN). +- **Honest counterweight:** Prime Intellect — the most-funded name in the space — trained its flagship INTELLECT-3 (106B MoE) on a *centralized* 512×H200 cluster, a telling signal that for frontier-quality RL post-training, centralized still wins on engineering economics. RL post-training is the most decentralization-friendly workload; full frontier-scale pretraining over WAN remains unproven above ~100B dense. +- **For mindXtrain:** the AOT-only reproducibility discipline is *precisely* the property that verification protocols (Gensyn Verde/RepOps, TOPLOC) require. The natural integration is RL-Swarm-style participation plus an x402-payable training-job surface with checkpoint-hash verification. + +--- + +## 1. The Algorithms: How Training Escaped the Datacenter + +The core problem: datacenter training assumes NVLink/InfiniBand (100s of GB/s); the internet gives you 100–1000× less. Every viable approach attacks communication volume. + +### DiLoCo family (data-parallel, low-communication) +- **DiLoCo** (DeepMind, [arXiv:2311.08105](https://arxiv.org/abs/2311.08105)) — the foundational recipe: a variant of federated averaging where each worker runs many local AdamW steps (H = hundreds), then synchronizes "pseudo-gradients" via an outer Nesterov-momentum optimizer. On C4, 8 workers matched fully synchronous training while **communicating 500× less**. +- **Streaming DiLoCo** ([arXiv:2501.18512](https://arxiv.org/abs/2501.18512)) — three upgrades: synchronize parameter *subsets* in sequence (slashing peak bandwidth), overlap communication with continued training, and quantize exchanged data. Result: billion-scale training at matching quality with **two orders of magnitude less bandwidth**. This is the blueprint for cross-datacenter training (and the suspected basis of Google's multi-campus Gemini training). +- **OpenDiLoCo** (Prime Intellect, [arXiv:2407.07852](https://arxiv.org/abs/2407.07852), [github.com/PrimeIntellect-ai/OpenDiLoCo](https://github.com/PrimeIntellect-ai/OpenDiLoCo)) — the open implementation (Hivemind-based), demonstrated at 1B+ across 3 countries at 90–95% utilization; scaled in INTELLECT-1 to 10B with int8 pseudo-gradients (~400× communication reduction, [arXiv:2412.01152](https://arxiv.org/abs/2412.01152)). +- **SparseLoCo** (Templar/Bittensor, [arXiv:2508.15706](https://arxiv.org/abs/2508.15706)) — the 2026 state of the art for data-parallel WAN pretraining: error-feedback accumulators + **Top-k sparsification + 2-bit quantization reaching 1–3% density**, which *outperforms* DiLoCo baselines on loss while compressing ~97%+. Key insight: outer momentum can be locally approximated by the error-feedback buffer, and sparse aggregation can actually *improve* performance. This is what trained Covenant-72B over home internet connections. + +### Momentum-decoupling (Nous lineage) +- **DeMo — Decoupled Momentum Optimization** ([arXiv:2411.19870](https://arxiv.org/abs/2411.19870), [github.com/bloc97/DeMo](https://github.com/bloc97/DeMo)) — drop-in replacement for momentum optimizers: decouple local momentum, apply a fast DCT transform + top-k sparsification, reuse momentum as error feedback. **Up to 85× less data per GPU** than AdamW-DDP at comparable loss (shown at 300M/1B); topology-agnostic, works over plain Ethernet. +- **DisTrO** (Nous Research, [github.com/NousResearch/DisTrO](https://github.com/NousResearch/DisTrO)) — the productionized family built on DeMo's ideas, reducing inter-GPU transfer by several orders of magnitude; the engine of the Psyche network. + +### Model-parallel over WAN (Pluralis) +- **SWARM Parallelism** ([arXiv:2301.11913](https://arxiv.org/abs/2301.11913)) — the precursor: pipeline-parallel training on unreliable, heterogeneous, low-bandwidth nodes (1.3B GPT over ~200Mb/s links with ~2× slowdown). +- **Protocol Models** (Pluralis, [arXiv:2506.01260](https://arxiv.org/abs/2506.01260)) — the breakthrough for *model* parallelism: unlike data-parallel (exchange weight gradients), model-parallel must compress **activations and activation gradients** flowing between layers. Pluralis confines them to a predefined low-dimensional subspace exploited via the transformer's recursive structure, achieving **up to 99% compression with no convergence degradation**. Side effect with economic teeth: no participant ever holds full model weights — the model becomes an unextractable, protocol-native asset ("Unextractable Protocol Models"). Follow-on work: async pipeline-parallel with Nesterov stale-update correction, and >95% compression for context parallelism. Blog: [pluralis.ai/blog](https://pluralis.ai/blog/). + +### Other lineages +- **Federated learning** (FedAvg lineage) — the ancestor of all of this; Flower Labs ([github.com/adap/flower](https://github.com/adap/flower)) carries it forward with FlowerLLM/Photon for federated LLM pretraining. +- **Hivemind** ([github.com/learning-at-home/hivemind](https://github.com/learning-at-home/hivemind), MIT) — the P2P substrate (DHT, decentralized averaging, NAT traversal) under OpenDiLoCo, Petals, and Gensyn RL Swarm. +- **Petals** ([github.com/bigscience-workshop/petals](https://github.com/bigscience-workshop/petals)) — collaborative inference/fine-tuning of 100B+ models, BitTorrent-style layer hosting. + +**Rule of thumb on bandwidth:** dense DDP needs ~GB/s-class links; DiLoCo-class needs ~100 Mb/s–1 Gb/s with minutes-scale sync windows (8-bit DiLoCo measured ~8.3 min all-reduce at 14 nodes); SparseLoCo/DeMo push viable participation down to consumer broadband. + +--- + +## 2. The Networks: Who's Actually Training What + +### Prime Intellect — the open superintelligence stack +- **Track record:** INTELLECT-1 (10B, OpenDiLoCo, 3 continents) → **INTELLECT-2** (32B, first globally distributed RL run; [arXiv:2505.07291](https://arxiv.org/abs/2505.07291)) → **INTELLECT-3** (106B MoE, 12B active, SFT+RL on GLM-4.5-Air base; [arXiv:2512.16144](https://arxiv.org/abs/2512.16144), [huggingface.co/PrimeIntellect/INTELLECT-3](https://huggingface.co/PrimeIntellect/INTELLECT-3), released Nov 27, 2025 — best-in-class math/code/reasoning for its size). +- **The catch:** INTELLECT-3 was trained on a **centralized 512× H200 cluster**, not the decentralized protocol — a candid pivot toward "open-source models + compute platform" over pure decentralization ([blog](https://www.primeintellect.ai/blog/intellect-3), critical coverage: [implicator.ai](https://www.implicator.ai/prime-intellects-intellect-3-open-source-ambition-meets-centralized-reality/)). +- **Platform (2026):** **Lab** ([blog](https://www.primeintellect.ai/blog/lab)) unifies the Environments Hub, hosted RL training, and hosted evals — 10,000+ training jobs run by hundreds of teams; opened fully May 2026. Joined the NVIDIA Nemotron coalition (June 2026). Compute Exchange aggregates global GPU supply. +- **Protocol/token:** peer-to-peer compute protocol live on internal testnet (powered SYNTHETIC-2 and INTELLECT-2); contracts on Base Sepolia with a RewardsDistributor pattern suggesting an eventual token; no token launched as of June 2026. +- **Repos (all permissive):** [prime-rl](https://github.com/PrimeIntellect-ai/prime-rl) · [protocol](https://github.com/PrimeIntellect-ai/protocol) · [toploc](https://github.com/PrimeIntellect-ai/toploc) · [shardcast](https://github.com/PrimeIntellect-ai/shardcast) · [verifiers](https://github.com/PrimeIntellect-ai/verifiers) · [OpenDiLoCo](https://github.com/PrimeIntellect-ai/OpenDiLoCo) +- **Funding:** $20M+ total — Founders Fund lead, with Karpathy, Delangue, Dylan Patel, Tri Dao, Emad Mostaque among angels. + +### Nous Research / Psyche — DisTrO on Solana +- **Psyche** ([github.com/PsycheFoundation/psyche](https://github.com/PsycheFoundation/psyche), **Rust, Apache 2.0**; [docs](https://nousresearch.com/nous-psyche)) — decentralized training network with **coordination on Solana** for fault-tolerant, censorship-resistant orchestration; compute off-chain running DisTrO-compressed training. +- **Consilience-40B:** dense 40B with DeepSeek-style MLA attention, target ~20T tokens (FineWeb 14T + FineWeb-2 4T + Stack v2 upsampled to 1T) — by parameters×tokens, **the largest distributed pre-training run ever** over the internet; deliberately sized to train on one HGX and infer on a 3090. As of the ["Next Phase of Psyche"](https://nousresearch.com/the-next-phase-of-psyche) (Nov 2025), the testnet run validated internet-bandwidth training at scale and Psyche pivoted to training multiple models in parallel. +- **Token status (important):** as of April 2026 **no official Nous/Psyche token exists** — "NOUS" pairs on Solana DEXs are unofficial; don't confuse with Nosana ($NOS). +- **Funding:** $50M from Paradigm at ~$1B valuation. +- **Models:** Hermes series ([huggingface.co/NousResearch](https://huggingface.co/NousResearch)) validates the open-model credibility that underwrites the network. + +### Gensyn — verification-first ML compute protocol +- **Architecture:** four primitives — execution, verification, communication, coordination — on a custom Ethereum-rollup testnet. Backed by a16z ($43M Series A). +- **Status (2026):** RL Swarm (peaked ~12,000 testnet nodes; later environments: CodeZero coding swarm) and BlockAssist/CodeAssist have been **paused/sunset**; focus consolidated on **Delphi**, a "prediction market for machine intelligence," as the first Mainnet application. **Mainnet not yet launched** as of June 2026; testnet docs: [docs.gensyn.ai/testnet](https://docs.gensyn.ai/testnet). +- **Repos:** [rl-swarm](https://github.com/gensyn-ai/rl-swarm) · [rl-swarm-contracts](https://github.com/gensyn-ai/rl-swarm-contracts) · [repops-demo](https://github.com/gensyn-ai/repops-demo). RL Swarm hardware floor was deliberately low: arm64/x86 CPU + 32GB RAM, or NVIDIA 3090/4090/5090/A100/H100; macOS, Linux, Windows-WSL2; Python 3.10–3.13. +- **Research:** Verde ([arXiv:2502.19405](https://arxiv.org/abs/2502.19405)), SAPO swarm-sampling policy optimization, NoLoCo (no-all-reduce training, [arXiv:2506.10911](https://arxiv.org/abs/2506.10911)), Gauntlet-style contribution scoring lineage. + +### Templar / Bittensor SN3 — the permissionless proof +- **Covenant-72B** (announced March 10, 2026): **72B params, ~1.1T tokens, 70+ independent miners, fully permissionless** — anyone with GPUs could join/leave mid-run — over commodity internet. MMLU 67.1 (~LLaMA-2-70B class). Enabled by **SparseLoCo** (146× communication reduction claimed via sparsification + 2-bit quantization + error feedback) and the **Gauntlet** contributor-scoring system (loss-based evaluation of each node's submitted updates, with TAO/alpha incentives and slashing-style penalties for junk contributions). Apache-licensed model. +- **Ecosystem effects:** τemplar token +194% in a week; TAO ~+30–40%; Jensen Huang likened it to "folding@home for AI"; coverage from Jack Clark's Import AI. Bittensor in March 2026: ~128 active subnets, TAO ~$3.4B market cap, subnet alpha tokens ~$1.4B combined (see [arXiv risk study](https://arxiv.org/pdf/2603.29751)). +- **Links:** [tplr.ai](https://tplr.ai) · [github.com/tplr-ai/templar](https://github.com/tplr-ai/templar) · Bittensor: [github.com/opentensor/bittensor](https://github.com/opentensor/bittensor) · related training subnets: Macrocosmos IOTA ([macrocosmos.ai](https://www.macrocosmos.ai)) for pipeline-parallel pretraining experiments. +- **dTAO mechanics:** each subnet has its own alpha token bonded against TAO; miners earn by validator-scored contribution quality — the only live, fully incentivized, permissionless training market as of mid-2026. + +### Pluralis Research — Protocol Learning (model parallel) +- **Node0-7.5B** ([dashboard.pluralis.ai](https://dashboard.pluralis.ai), [github.com/PluralisResearch/node0](https://github.com/PluralisResearch/node0)): the first public **model-parallel** internet pretraining run — completed after **36B tokens over 3 weeks with 300+ active participants and 1,642 GPUs across 198 cities**, joinable with a single 16GB consumer GPU (3090-class). Built on Protocol Models compression ([arXiv:2506.01260](https://arxiv.org/abs/2506.01260)). +- **Strategic differentiator:** weights are sharded such that **no participant can extract the full model** — enabling on-protocol model ownership, revenue attribution, and access gating (deeply relevant to DAIO-style on-chain asset thinking). Funding: $7.6M seed (USV, CoinFund). + +### Others worth tracking +- **Flower Labs** ([flower.ai](https://flower.ai), [github.com/adap/flower](https://github.com/adap/flower), Apache 2.0) — federated LLM training (FlowerLLM; Photon system paper) with the largest federated-learning developer community. +- **Exo Labs** ([github.com/exo-explore/exo](https://github.com/exo-explore/exo)) — cluster your own heterogeneous consumer devices (Macs, mining rigs) for local training/inference; not a token network. +- **Petals / Hivemind** — see §1; research substrate more than incentive network. +- **Compute marketplaces (supply side, not training protocols):** Akash ([akash.network](https://akash.network), AKT), io.net (IO), Render, Aethir, Spheron — these price raw GPU hours; training networks sit a layer above. +- **FedML/TensorOpera, Bagel** ([bagel.net](https://bagel.net) — "Bakery" fine-tuning marketplace research) — earlier-stage or pivoted. + +--- + +## 3. Verification: The Trust Layer + +(Extends the verification section of the main survey — repos there remain canonical.) + +| Approach | System | Verifies | Production status | +|---|---|---|---| +| Activation LSH | [TOPLOC](https://github.com/PrimeIntellect-ai/toploc) ([arXiv:2501.16007](https://arxiv.org/abs/2501.16007)) | Inference/rollouts | Used in INTELLECT-2 | +| Refereed delegation + bitwise-reproducible ops | Verde + RepOps ([arXiv:2502.19405](https://arxiv.org/abs/2502.19405), [repops-demo](https://github.com/gensyn-ai/repops-demo)) | **Training** steps | Gensyn testnet | +| Optimistic fraud proofs | [opML](https://github.com/ora-io/opml) ([arXiv:2401.17555](https://arxiv.org/abs/2401.17555)) | Inference (training targeted) | ORA on-chain AI | +| zkML | [EZKL](https://github.com/zkonduit/ezkl), [ddkang/zkml](https://github.com/ddkang/zkml), Lagrange DeepProve | Small-model inference proofs | Niche; cost-bound | +| Economic scoring | Templar **Gauntlet** (loss-evaluation of contributions + token slashing) | Training contributions statistically | **Live, incentivized** (SN3) | +| TEEs | NVIDIA Confidential Computing (H100), Intel TDX, AWS Nitro | Execution environment | Growing in compute markets | + +**The honest state:** cryptographic verification of *pretraining* at scale remains unsolved in production. Templar's Gauntlet shows the pragmatic alternative — statistical/economic verification (does your update reduce loss?) backed by stake. Verde/RepOps is the most principled training-verification design but needs deterministic execution — which is exactly what an **AOT-only artifact policy** provides. zkML proof costs are still orders of magnitude above native compute for LLM-scale work. + +--- + +## 4. Why RL Is the Decentralization Sweet Spot + +- RL post-training = **embarrassingly parallel rollout generation** (inference-heavy, communication-light) + a small trainer. INTELLECT-2's architecture is the template: [prime-rl](https://github.com/PrimeIntellect-ai/prime-rl) async trainer ← TOPLOC-verified rollouts from untrusted inference nodes ← [shardcast](https://github.com/PrimeIntellect-ai/shardcast) weight broadcasts. +- Gensyn's RL Swarm generalized this into multi-agent collaborative RL (answer/critique/revise games; SAPO swarm sampling), demonstrating swarm-trained models learn faster than solo — and that heterogeneous, consumer hardware can contribute usefully because rollouts don't need gradient sync. +- The **environments economy** is the new commodity layer: Prime Intellect's Environments Hub + [verifiers](https://github.com/PrimeIntellect-ai/verifiers) library, Gensyn CodeZero, reasoning-gym lineage. Whoever owns high-quality verifiable environments owns RL training demand. (For PYTHAI: blockchain task environments — Foundry test-passing, contract auditing, x402 flow completion — are an unclaimed niche.) +- Caveat from the main survey still holds: decentralized RL gains concentrate in trained domains (math/code); broad transfer lags centralized RL. + +--- + +## 5. Economics & Crypto Integration + +- **Live token economics:** only Bittensor — TAO emission split across 128 subnets via dTAO; subnet alpha tokens (τemplar) reprice on demonstrated capability. Covenant-72B was the first event where a training result directly repriced a token 194%. +- **Pending:** Prime Intellect (Base testnet contracts, RewardsDistributor pattern, no token), Gensyn (testnet points → expected token at Mainnet; Delphi first), Psyche (Solana-coordinated, explicitly **no official token yet** as of April 2026 — beware impostor "NOUS" pairs). +- **Funding landscape:** a16z→Gensyn ($43M), Paradigm→Nous ($50M @ ~$1B), Founders Fund→Prime Intellect ($20M+), USV/CoinFund→Pluralis ($7.6M); DCG's Yuma accelerates Bittensor ecosystem; Grayscale holds TAO. +- **Sober read:** the only mechanism so far proven to incentivize *useful* training (not speculation) is Templar's loss-scored, slashing-backed contribution market. Everything else either pays points (Gensyn), pays nothing yet (Psyche, Pluralis Node0 — reputational/dashboard credit), or routes around tokens entirely (Prime Intellect's fiat compute exchange). +- **x402 relevance:** none of these networks natively meter per-job crypto payments; a per-training-job x402 paywall (Algorand x402-avm "Parsec" in your stack) in front of a verifiable training endpoint is genuinely unbuilt territory. + +--- + +## 6. Hardware & Network Realities + +- **Demonstrated efficiency:** INTELLECT-1 hit 83–96% utilization (14 nodes, 3 continents); OpenDiLoCo 90–95%; Pluralis cites GPT-1.3B pipeline-parallel over 200Mb/s at ~2× slowdown; SparseLoCo makes consumer broadband viable at 72B. Expect 1.2–3× wall-clock penalty vs an equivalent co-located cluster when the algorithm fits, far worse when it doesn't. +- **Consumer hardware floors:** Pluralis Node0 — single 16GB GPU (3090); Gensyn RL Swarm — even CPU+32GB RAM; Templar mining — prosumer multi-GPU favored; Psyche — 3090-class inference target, training nodes larger. +- **AMD/ROCm reality check:** every major network's node software is **NVIDIA/CUDA-first** (Gensyn lists 3090/4090/5090/A100/H100; Pluralis requires CUDA ≤12.x). MI300X participation today means either contributing through GPU marketplaces (Prime Intellect Compute Exchange lists heterogeneous supply) or running protocol-side/trainer-side infrastructure rather than mining. This is a gap — and an opening for ROCm-native node ports. +- **Networking stacks:** Hivemind DHT (+ relays/NAT traversal) dominates (OpenDiLoCo, Petals, RL Swarm); Psyche uses Solana for coordination + P2P data plane (iroh-class Rust networking); Templar uses Bittensor's axon/dendrite gossip + object storage for gradient exchange. +- **Churn tolerance:** all serious systems assume nodes join/leave mid-run — DiLoCo's infrequent sync, SWARM's stochastic rewiring, Gauntlet's per-contribution scoring, and Psyche's on-chain checkpointing all exist precisely for this. + +--- + +## 7. Critical Assessment & Open Problems + +1. **Scale ceiling:** largest decentralized pretraining = 72B dense / ~1.1T tokens (Covenant). Frontier centralized runs are training 10×+ larger models on 50×+ tokens with 100,000+ GPU clusters. The gap is closing on a log scale, not disappearing. +2. **The Prime Intellect signal:** when the best-funded decentralized lab trains its flagship centrally (512×H200) while open-sourcing the stack, the message is: decentralization currently wins on *access and sovereignty*, not on cost or speed at frontier quality. +3. **Verification gap:** pretraining verification is economic, not cryptographic. A motivated adversary inside a permissionless run is mitigated (Gauntlet slashing, Byzantine-robust aggregation), not eliminated. Data poisoning in permissionless data-parallel runs remains under-studied. +4. **Model parallelism over WAN** is the frontier — Pluralis is essentially alone in production here; if Protocol Models scales past ~10B with heterogeneous consumer cards, the "no single node has the weights" property changes the ownership game entirely. +5. **Regulatory horizon:** the "no-off problem" ([arXiv:2412.07890](https://arxiv.org/pdf/2412.07890)) — once training is a protocol, no one can stop it. Expect compute-governance and export-control attention as runs approach frontier capability. +6. **Forecast:** decentralized *post-training* (RL, fine-tuning, distillation) reaches economic parity first — arguably already there for verifiable-reward domains. Decentralized *pretraining* plausibly reaches 100B+ dense / multi-trillion tokens by 2027 via SparseLoCo-class compression + dTAO-class incentives, but frontier parity requires either an algorithmic surprise or centralized-compute commoditization. + +--- + +## 8. Practical Integration for mindXtrain / PYTHAI + +**Participate (today, ranked by fit):** +1. **Prime Intellect Lab / Environments Hub** — publish blockchain-native RL environments (Foundry-test-passing, Solidity audit, Algorand x402 flows) via the [verifiers](https://github.com/PrimeIntellect-ai/verifiers) library; train against them with hosted RL or your own prime-rl deployment. Lowest friction; AMD-agnostic since you consume the platform. +2. **Templar SN3 mining** ([github.com/tplr-ai/templar](https://github.com/tplr-ai/templar)) — the only incentivized live training market; NVIDIA prosumer hardware; real TAO/alpha yield, real slashing risk. +3. **Pluralis Node0-class events** ([github.com/PluralisResearch/node0](https://github.com/PluralisResearch/node0)) — 16GB+ NVIDIA GPU, port 49200 exposed, Docker; watch for the next run. +4. **Psyche** ([github.com/PsycheFoundation/psyche](https://github.com/PsycheFoundation/psyche)) — Rust/Apache-2.0, Solana coordination (your chain-stack adjacency is an advantage); contribution currently reputational. +5. **Gensyn** — RL Swarm paused; watch Delphi → Mainnet for the token-incentivized restart. + +**Build (the mindXtrain thesis):** +- Your **AOT-only discipline is the verification primitive**: deterministic compiled artifacts + pinned ROCm/libtorch = exactly the bitwise-reproducibility Verde/RepOps demands. A mindXtrain node that ships its AOT probe artifact alongside checkpoint hashes is *natively verifiable*. +- **Training-as-a-service with x402:** front a prime-rl or Axolotl/TRL pipeline with an x402-metered endpoint (Parsec on Algorand from your stack); escrow per-job payment against TOPLOC-style rollout proofs or Verde-style checkpoint-hash spot-checks; settle on completion. Register the service as an ERC-8004 agent on AgenticPlace. Nobody has shipped this combination. +- **ROCm node ports** of rl-swarm / node0 / psyche clients are an open contribution lane with outsized visibility — every network is CUDA-locked and knows it. + +--- + +## Network Comparison Table + +| Network | Algorithm | Largest demonstrated | Verification | Token (Jun 2026) | Min hardware | Code (license) | +|---|---|---|---|---|---|---| +| Prime Intellect | OpenDiLoCo → prime-rl async RL | INTELLECT-2 32B RL (decentralized); INTELLECT-3 106B MoE (centralized) | TOPLOC | None (Base testnet contracts) | Platform consumer / any | [PrimeIntellect-ai](https://github.com/PrimeIntellect-ai) (Apache 2.0) | +| Nous Psyche | DisTrO/DeMo, Solana coordination | Consilience-40B @ 20T-token target | Solana-anchored checkpoints | None official (beware fakes) | Prosumer GPU+ | [PsycheFoundation/psyche](https://github.com/PsycheFoundation/psyche) (Apache 2.0) | +| Gensyn | RL Swarm (Hivemind), NoLoCo, SAPO | ~12K-node RL swarm (testnet) | Verde + RepOps | Testnet points; token at Mainnet | CPU+32GB RAM or 3090+ | [gensyn-ai](https://github.com/gensyn-ai) (varied OSS) | +| Templar (SN3) | SparseLoCo + Gauntlet | **Covenant-72B, 1.1T tokens, permissionless** | Economic (loss-scored, slashed) | **Live**: TAO + τemplar alpha | Prosumer/multi-GPU NVIDIA | [tplr-ai/templar](https://github.com/tplr-ai/templar) (MIT) | +| Pluralis | Protocol Models (model-parallel, 99% compression) | Node0-7.5B: 1,642 GPUs, 198 cities | Weight-sharding (unextractable) | None | 16GB GPU (3090) | [PluralisResearch/node0](https://github.com/PluralisResearch/node0) | +| Flower | Federated (Photon/FlowerLLM) | Federated LLM pretraining research | — | None | Any | [adap/flower](https://github.com/adap/flower) (Apache 2.0) | + +--- + +## Key Papers Index + +[DiLoCo 2311.08105](https://arxiv.org/abs/2311.08105) · [Streaming DiLoCo 2501.18512](https://arxiv.org/abs/2501.18512) · [OpenDiLoCo 2407.07852](https://arxiv.org/abs/2407.07852) · [SparseLoCo 2508.15706](https://arxiv.org/abs/2508.15706) · [DeMo 2411.19870](https://arxiv.org/abs/2411.19870) · [SWARM 2301.11913](https://arxiv.org/abs/2301.11913) · [Protocol Models 2506.01260](https://arxiv.org/abs/2506.01260) · [INTELLECT-1 2412.01152](https://arxiv.org/abs/2412.01152) · [INTELLECT-2 2505.07291](https://arxiv.org/abs/2505.07291) · [INTELLECT-3 2512.16144](https://arxiv.org/abs/2512.16144) · [TOPLOC 2501.16007](https://arxiv.org/abs/2501.16007) · [Verde 2502.19405](https://arxiv.org/abs/2502.19405) · [opML 2401.17555](https://arxiv.org/abs/2401.17555) · [NoLoCo 2506.10911](https://arxiv.org/abs/2506.10911) · [No-Off Problem 2412.07890](https://arxiv.org/pdf/2412.07890) + +--- + +## Caveats + +- Token prices, node counts, and network statuses shift weekly; figures here are snapshots from announcements and coverage through early June 2026. +- Covenant-72B performance claims (LLaMA-2-70B parity, 146× compression) originate from the Templar team and secondary coverage; independent replication of the full run is not yet published. +- Several "largest ever" claims (Psyche Consilience vs Covenant) measure different things — parameters, tokens processed, or parameters×tokens — and both teams claim records under their preferred metric. +- AMD/ROCm support statements reflect documented requirements as of writing; check each repo's README before provisioning hardware. diff --git a/docs/development.md b/docs/development.md new file mode 100644 index 0000000000000000000000000000000000000000..3a72f5fd65da1fb506f94452b000b1028c609839 --- /dev/null +++ b/docs/development.md @@ -0,0 +1,330 @@ +# Development workflow + +Conventions and invariants for working in this repo. Read once before opening a PR. + +## Toolchain + +- **Python 3.12** (`>=3.12,<3.13`) — pinned; matches `rocm/primus:v26.2`. +- **uv** — single project (no workspace). `uv sync` installs the base deps; + `uv sync --extra <group>` adds optional groups. +- **ruff** — replaces black/isort/flake8/pyupgrade. Config in + [`pyproject.toml`](../pyproject.toml). +- **mypy --strict** — only on `mindxtrain/config` and `mindxtrain/provenance` + (the schemas + manifest paths). Training / eval code is exempt. +- **pytest** + `pytest-asyncio` — fast unit tests; GPU tests are manual on + the MI300X. +- **Foundry** — Solidity contracts in `contracts/`. Installed on the MI300X + droplet for the on-chain anchoring path. + +## Optional dependency groups + +`pyproject.toml` defines six `[project.optional-dependencies]` groups: + +| Group | Adds | +|---------|-------------------------------------------------------------| +| `ml` | trl, transformers, peft, accelerate, datasets | +| `eval` | lm-eval, lighteval, inspect-ai, jinja2 | +| `data` | datasketch, sentence-transformers, faiss-cpu, pyarrow | +| `serve` | vllm | +| `chain` | web3, py-algorand-sdk, huggingface-hub | +| `obs` | opentelemetry-sdk, prometheus-client, psutil | + +Plus `all` which pulls everything except `amd-quark` (which ships in the +rocm/primus container, see [HANDOFF.md](HANDOFF.md) §3). + +The base install (no extras) is enough for: the CLI, the Coach UI, the +autotune dry-run, manifest verify, the operator FastAPI app, and every +in-process Python utility (registry, hot-swap, agent loop, ContextManager, +data filter, sequence packing). See +[actualization_status.md](actualization_status.md) for the per-module map. + +## Lazy-import pattern + +Every module that wants an optional dep guards the import inside the +function that needs it: + +```python +def run_lm_eval(model_dir: Path, tasks: list[str]) -> Path: + if not _lm_eval_available(): + msg = "lm-eval not installed; run `uv sync --extra eval`." + raise RuntimeError(msg) + ... # subprocess wrap that uses the dep +``` + +Two implications: + +1. `import mindxtrain.eval.harness` always succeeds even without `--extra eval`. +2. The error message includes the exact `uv sync --extra <group>` to run. + +This is the canonical pattern; new modules that take optional deps must +follow it. + +## The standard local cycle + +```bash +uv sync # base install +uv run ruff check --fix . # lint + auto-fix +uv run mypy mindxtrain/config mindxtrain/provenance # types where strict +uv run pytest -q # → 564 passed in ~5s +``` + +CI runs the same four commands on Ubuntu 24.04 / Python 3.12 (CPU-only). +See [`.github/workflows/ci.yml`](../.github/workflows/ci.yml). + +## Repository layout + +``` +. +├── pyproject.toml # single project; optional-dep groups +├── README.md # entry doc (the only root .md besides CLAUDE/AGENTS) +├── CLAUDE.md, AGENTS.md # agent-tooling entrypoints (required at root) +├── NOTICE, LICENSE-* # legal +├── Containerfile, compose.yaml # podman entry points +├── docs/ # all documentation (index: docs/NAV.md) +│ ├── NAV.md # docs index +│ ├── HANDOFF.md # operator checklist +│ ├── dcoach.md # the proof loop + decentralized fit +│ ├── CHANGELOG.md +│ └── … # architecture, coach, governance, decentralized, reference +├── mindxtrain/ # the package — 12 subpackages, ~99 modules +│ ├── cli/ # typer CLI (9 verbs) +│ ├── config/ # 10-section Pydantic schema + JSON defaults +│ ├── data/ # curate → dedupe → filter → tokenize → pack → synth → verify +│ ├── models/ # registry + chat templates + 5 base presets +│ ├── train/ # sft, dpo, grpo, rlhf, tool_use, distributed, callbacks, recipes/ +│ ├── eval/ # lighteval / inspect_ai / bfcl / persona / agenda / card +│ ├── autotune/ # 60s AOT probe — the differentiator +│ ├── operator/ # FastAPI app, Coach UI, ml-intern patterns +│ ├── storage/ # local_fs / hf_hub / lighthouse / ipfs +│ ├── provenance/ # manifest, hashing, verify, erc8004, algorand, x402 +│ ├── deploy/ # registry, hot_swap, ab_test, vllm_launcher, quark +│ └── budget/ # ResourceBudget + cloud-provider stubs +├── contracts/ # Foundry workspace (ERC-8004 attestation) +├── ops/ # containerfiles, compose, k8s, vmm, gensyn +├── examples/ # demo YAMLs +├── tests/ # pytest — 566 tests (CPU-only smoke) +└── docs/ + ├── *.md # current state (this directory) + └── blueprints/ # source design briefs (frozen) +``` + +## Reuse boundaries + +- **From `/home/hacker/mindX/`** (production codebase): Codephreak persona + JSON loaded at runtime via `MINDXTRAIN_PERSONA_PATH`. Do not copy file + bytes — load via env var. +- **Not** from `/home/hacker/aglm/` — broken per its own README. Use only + for reference to legacy class names mindxtrain2.md flagged as needing + refactor. + +## Invariants + +These are non-negotiable; violating them is a deployment bug, not a style +preference. + +1. **AOT-only.** No `torch.compile(mode="max-autotune")` in production paths. + No JIT autotune in vLLM serving (set `VLLM_USE_TRITON_FLASH_ATTN=0` if + needed). The `autotune.policy: aot_only` field in the YAML is the + contract; tested at + `tests/test_config_schema.py::test_qwen3_8b_sft_lora_validates`. +2. **`hardware.gpus: 1 | 8` only.** 2/4-GPU MI300X FSDP groups hit + asymmetric xGMI; the schema rejects them at parse time. Tested at + `tests/test_config_schema.py::test_xgmi_2gpu_rejected` and + `tests/test_distributed.py`. +3. **Seven MI300X env vars in `train.env`** (defaults, can be overridden by + the autotune plan but never removed): `HSA_NO_SCRATCH_RECLAIM=1`, + `NVTE_CK_USES_BWD_V3=1`, `NVTE_CK_IS_V3_ATOMIC_FP32=1`, + `PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1`, `NCCL_MIN_NCHANNELS=112`, + `HIP_FORCE_DEV_KERNARG=1`, `PYTORCH_ROCM_ARCH=gfx942`. +4. **`extra: forbid` on every Pydantic model.** Unknown YAML keys raise + `ValidationError`. Tested at + `tests/test_config_schema.py::test_extra_field_forbidden`. +5. **Configs are immutable once loaded** (`frozen: true`). +6. **Solidity contracts: no proxies, no `Ownable`, no admin keys, no setters.** + `mindxtrain_registry.sol` is write-once. Rotating any parameter requires a + fresh deploy. +7. **Lazy imports for optional deps** — see the pattern above. + +## Training lanes (CPU / local-GPU / MI300X) + +Three ways to actually run a fine-tune, selected by `train.backend`: + +| Lane | Backend | Device | When | +|------|---------|--------|------| +| CPU | `trl_cpu` | CPU, float32 (in-process TRL) | mindX self-training, smoke runs, no GPU | +| Local GPU | `trl_local` | auto: CUDA/ROCm GPU (bf16/fp16) else CPU fallback | consumer Radeon RX / NVIDIA RTX, or a laptop | +| MI300X | `axolotl`/`unsloth`/`torchtune`/`primus` | gfx942 subprocess + 7 env vars | the AOT MI300X target | + +`trl_local` is the **device-aware** in-process lane (`backend_trl_cpu.py::run_trl_local`): +it picks the GPU when `torch.cuda.is_available()` (ROCm surfaces through the same API), +else logs `no accelerator detected → CPU fallback` and runs on CPU. The same recipe +(`mindx_fallback_qwen3_1_5b_local`) therefore runs unchanged on a gaming GPU or a laptop. +`trl_cpu` is `run_trl_local(..., force_cpu=True)`; `MINDXTRAIN_FORCE_CPU=1` forces the +fallback anywhere. The in-process lanes never inject the seven MI300X env vars. + +Confirm which device a box will use: +```bash +uv run python -c "import torch; print(torch.cuda.is_available(), torch.version.hip)" +``` + +**Unsupported:** integrated Vega/RDNA APUs (e.g. Ryzen "Raven"/`gfx90c`) are not ROCm +targets and fall back to CPU. A discrete RX 6800/7900 (`gfx1030`/`gfx1100`) or any NVIDIA +RTX is the intended consumer GPU. + +## Adding a new recipe + +1. Drop a YAML at `mindxtrain/train/recipes/<name>.yaml`. Validate locally: + ```bash + uv run python -c "from mindxtrain.config.loader import load_config; load_config('mindxtrain/train/recipes/<name>.yaml')" + ``` +2. The `tests/test_config_schema.py::test_all_recipes_validate` test will + pick it up automatically — re-run pytest. +3. Add a row to [docs/yaml_schema.md](yaml_schema.md) only if the recipe + exercises a previously-unused field. + +## Adding a new training backend + +1. Add `mindxtrain/train/backend_<name>.py` exposing a + `run_<name>(cfg, plan, out_dir) -> Path` function (or for in-process TRL + trainers, a `run_<name>(cfg, out_dir) -> Path` function). +2. Wire it into `mindxtrain/train/dispatch.py`'s `if backend == ...` ladder. +3. Add `<name>` to the `TrainingBackend` literal in + `mindxtrain/config/schema.py`. +4. Update [docs/cli.md](cli.md) "Where the verbs live" table. + +## Adding a new model backend (operator) + +1. Add `mindxtrain/operator/backends/<name>.py` with a `Backend` subclass + decorated `@register_backend("<name>")`. +2. Side-effect import it from `mindxtrain/models/registry.py` so registration + runs on package import. +3. Add a runtime branch in `mindxtrain/operator/app.py::chat_completions` for + the env-var-driven kwargs. + +## Adding a new training method + +1. Define a `_MethodBase` subclass in `mindxtrain/config/schema.py` with + `kind: Literal["<name>"] = "<name>"` and the method-specific fields. +2. Add it to the `TrainMethod` discriminated union. +3. Add a `mindxtrain/train/<name>.py` runner (TRL wrap or subprocess). +4. Update the dispatch path so a YAML with `train.method.kind == "<name>"` + reaches the runner. +5. Add a recipe under `mindxtrain/train/recipes/` exercising it. +6. Update `docs/yaml_schema.md` "train.method" table. + +## Adding a new optional-dep group + +1. Add the entry to `[project.optional-dependencies]` in `pyproject.toml`. +2. Add a row to the table in [actualization_status.md](actualization_status.md). +3. Update [development.md](development.md) and [quickstart.md](quickstart.md). + +## Adding a new doc + +1. Write `docs/<name>.md`. +2. Add a one-line entry to [`docs/NAV.md`](NAV.md) under the appropriate section. + +## Live training UI + +The Coach UI's "Train" step (`#step-train` in +[`coach/static/index.html`](../mindxtrain/operator/coach/static/index.html)) +launches a training run and streams loss / lr / log lines back into the +browser over Server-Sent Events. Architecture: + +- **Registry**: `mindxtrain.operator.runs.RunRegistry` is an in-memory + singleton (one per uvicorn process) keyed by `run_id`. Snapshots are + immutable `Run` records (frozen Pydantic); state changes produce new + snapshots via `model_copy`. +- **Event schema**: `TrainEvent` is a tagged union over `StatusEvent`, + `StepEvent`, `EvalEvent`, `LogEvent`, `EnergyEvent` — all with + `extra="forbid", frozen=True`. Wire format: `event: <kind>\ndata: + <event.model_dump_json()>\n\n`. +- **Two ingestion paths**, deduped by `(run_id, step)` in + `RunRegistry.publish`: + 1. Subprocess stdout regex (`parse_trainer_log_line`) — works on the + base install, parses HF Trainer's `'loss': … 'learning_rate': …` + log lines. + 2. In-process `mindxtrain.train.callbacks.StreamCallback` — POSTs to + `/coach/api/runs/{id}/ingest` (loopback only). Requires `--extra ml`. +- **Subprocess orchestration**: `spawn_subprocess_streaming` uses + `subprocess.Popen(stdout=PIPE, bufsize=1, text=True)` and tees lines + to both `train.log` (the durable on-disk artifact) and + `RunRegistry.publish_threadsafe` from a daemon thread. We use + `Popen` (not `asyncio.create_subprocess_exec`, not `BackgroundTasks`) + so the child outlives the launch HTTP request and `SIGINT`-then-`SIGTERM` + cancellation matches the CLI Ctrl-C path. + +### Routes + +All under `/coach/api/runs`: + +| Verb | Path | Purpose | +|---|---|---| +| POST | `/launch` | Spawn a run; returns `Run` immediately. 503 if `accelerate` is missing. | +| GET | `/` | List active + last 20 runs. | +| GET | `/{id}` | `Run` snapshot. | +| GET | `/{id}/events` | SSE — all event kinds. Replays last 200 buffered on connect. | +| GET | `/{id}/logs` | SSE — `kind="log"` only. | +| POST | `/{id}/cancel` | `SIGINT` then `SIGTERM` after grace. | +| POST | `/{id}/ingest` | Loopback-only — used by `StreamCallback`. | + +SSE responses set `Cache-Control: no-cache`, `X-Accel-Buffering: no`, +`Connection: keep-alive` so reverse proxies don't buffer the stream. + +### Invariants + +- `import mindxtrain.operator.runs` succeeds **without** `--extra ml`. The + in-process `StreamCallback` requires `transformers`; the subprocess-stdout + path does not. UI degrades gracefully. +- `Run` and every `*Event` are `frozen=True, extra="forbid"`. +- The subprocess line reader runs in a daemon thread; events reach the + asyncio loop via `loop.call_soon_threadsafe(registry.publish, …)`. + +### Frontend + +Vanilla JS, no build step. Live view uses the browser-native `EventSource`: + +```js +const es = new EventSource(`/coach/api/runs/${id}/events`); +es.addEventListener("step", e => pushPoint(JSON.parse(e.data))); +es.addEventListener("log", e => appendLog(JSON.parse(e.data))); +es.addEventListener("status", e => updateBadge(JSON.parse(e.data))); +``` + +**Chart.js is vendored locally** at `coach/static/vendor/chart.umd.min.js` +(pinned to v4.4.0; SHA256 in `coach/static/vendor/VERSIONS.md`). No CDN +dependency at demo time. If the vendored bundle is missing, the page +degrades to a metrics table — `coach.js` checks `typeof Chart === "undefined"` +and shows the table-only fallback. + +### Why not Selenium / WebSocket / Streamlit + +- **Selenium** is a browser-test framework, not a UI library — it + can't push live data into a browser. (It might appear later as CI + smoke for the dashboard; that's E2E testing, not UI.) +- **WebSocket** is bidirectional; we don't need browser→server streaming. + Held in reserve for v2 "edit hyperparam mid-run." +- **Streamlit / Gradio** each spin up their own ASGI server on a separate + port, which breaks the single-URL operator demo and the lazy-import + invariant. SSE on the existing `:8080` is the right shape. + +## Common debugging + +| Symptom | Cause | +|------------------------------------------------|----------------------------------------------------------------------------------------------------| +| `ModuleNotFoundError: No module named 'mindxtrain'` | Forgot `uv sync`. Fixed by `uv sync`. | +| `RuntimeError: ... not installed; run uv sync --extra <group>` | Optional dep gating — install the named group. | +| `pydantic.ValidationError: extra keys not permitted` | YAML has a typo or stale field name. Compare to [yaml_schema.md](yaml_schema.md). | +| `ValueError: MI300X xGMI permits only 1 or 8 GPUs` | `hardware.gpus` is 2 or 4. Use 1 or 8. | +| `Failed to download due to network timeout` (uv) | `UV_HTTP_TIMEOUT=120 uv sync`. | +| First-iteration training is 30s slow on MI300X | Cold AITER / MIOpen / Triton caches. Volume-mount `~/.cache/miopen`, `AITER_JIT_DIR`, `TORCH_EXTENSIONS_DIR`. | +| `vllm serve` stalls on first batch | Triton autotune cold-start. Set `VLLM_USE_TRITON_FLASH_ATTN=0` or warm-up batch in `mindxtrain serve`. | + +## What not to commit + +- `*.safetensors`, `*.bin`, `*.pt`, `*.onnx` (large model weights). +- `out/`, `runs/`, `checkpoints/` (run outputs). +- `.env` (use `.env.example`). +- `contracts/lib/` (Foundry submodules — pulled with `forge install`). +- `.venv/`, `.uv-cache/`, `.cache/`. + +All of the above are in [`.gitignore`](../.gitignore). diff --git a/docs/governance.md b/docs/governance.md new file mode 100644 index 0000000000000000000000000000000000000000..7e324b96ede33a0490fe8c7b2238424283021b0d --- /dev/null +++ b/docs/governance.md @@ -0,0 +1,76 @@ +# Governance — classroom / boardroom / dojo + +A clean-room reimplementation (from the behaviour of +[`github.com/openmindx/openmind`](https://github.com/openmindx/openmind) — Boardroom +multi-model consensus + Dojo head-to-head evaluation) of the decision layer that governs +training. Lives in `mindxtrain/governance/`; pure stdlib + pydantic, base-install importable. + +## The model + +- **Classroom** (`governance/classroom.py`) — where an actor (model) trains. An actor + **graduates** when its persona imprint took: `graduate(imprint_report, min_delta=…)` + returns a `Graduation` (the motion the boardroom convenes on). Ties the governance layer + to `mindxtrain.eval.imprint`. +- **Boardroom** (`governance/boardroom.py`) — a panel of **any number** of role-based + members (advocate, critic, analyst, devil's advocate, expert, generalist). `convene(motion, + ballot)` tallies votes → `approved` / `rejected` / `disputed`. The boardroom **governs the + classroom**: it decides about training given a graduation. Preset boards: `classic_triad`, + `devils_court`, `full_board`, `peer_review`. +- **Dojo** (`governance/dojo.py`) — the boardroom's **dispute-settlement** extension. When a + boardroom is `disputed` (a tie or no quorum), a dojo settles it. **A dojo panel is always an + odd prime (≥ 3)** — an odd number of decisive judges cannot tie, so the dispute always + resolves. `Dojo.sized(n)` rounds a requested size to the nearest valid prime; `settle(motion, + ballot)` returns a final `DojoVerdict`. 2 is prime but even (can tie), so it is excluded. + +## Flow + +``` +classroom: train actor → measure imprint → graduate(report) ─► Graduation.motion + │ +boardroom: convene(motion, ballot) ─► approved / rejected / disputed │ + │ disputed │ +dojo (prime panel): settle_dispute(decision, dojo, ballot) ─► DojoVerdict (no tie) +``` + +Members vote via an explicit `{id: vote}` map or a callable `(member, motion) -> vote`, so +the whole layer is testable with no LLM and can later be backed by real models +(boardroom-of-LLMs, dojo head-to-head) — clean-room, never vendoring openmind's TypeScript. + +## Why prime + +A boardroom can be any size because deliberation tolerates abstention and "no decision" +(escalate). A dojo must *settle* — so its panel is an odd prime: `approvals + rejections` +is odd, the majority is strict, and the verdict is final. See `governance/primes.py` +(`is_prime`, `next_prime`, `nearest_prime`) and `dojo.prime_dojo_size`. + +## Model-backed deliberation + +`governance/panel.py` backs members + judges with **real models** (any OpenAI-compatible +backend — the same ollama / vLLM the operator serves). `deliberate(member, motion)` prompts a +member from its role stance and parses a `VERDICT: APPROVE|REJECT|ABSTAIN`; `model_ballot()` / +`model_judge_ballot()` return ballots you pass straight to `Boardroom.convene` / +`Dojo.settle`. Lazy + best-effort: a model that errors or returns no parseable verdict abstains +(boardroom) or is recorded as reject (dojo). The base URL resolves from +`MINDXTRAIN_OPENAI_BASE_URL` / `MINDXTRAIN_VLLM_BASE_URL` / `MINDXTRAIN_OLLAMA_BASE_URL`. + +## Coach surface + +The **Boardroom** card (after the receipt card) convenes a board on a promotion motion and, +if disputed, settles it in a prime dojo: + +- `GET /coach/api/boardroom/presets` — named boards → roles. +- `POST /coach/api/boardroom/convene` — `{motion, members:[{id,role,model}], quorum, votes?, + use_models?, base_url?}`. Tally supplied `votes`, or `use_models: true` to have each member's + model deliberate. Model calls run in a worker thread (`asyncio.to_thread`) so the operator + event loop never blocks on inference. Returns the decision + per-member deliberations. +- `POST /coach/api/dojo/settle` — `{motion, size, model?, votes?, use_models?, base_url?}`. + Sizes the panel to the nearest odd prime and settles. + +## Tests + +- `tests/test_governance.py` — primes, any-N boardroom (majority / tie / no-quorum), prime-only + dojo (rejects non-prime panels, settles without tie), end-to-end classroom → disputed → dojo. +- `tests/test_governance_panel.py` — verdict parsing, role stances, model-backed ballots driving + a boardroom + dojo over a mocked chat backend, graceful backend-error handling. +- `tests/test_coach_governance_api.py` — convene (votes + model mode), dojo settle (prime sizing), + 422 paths, and the Coach card/JS presence. diff --git a/docs/mindxtrain-llm-training-landscape-2026.md b/docs/mindxtrain-llm-training-landscape-2026.md new file mode 100644 index 0000000000000000000000000000000000000000..9eff6ee13e0be9fff9783e25257f377fb65a07ac --- /dev/null +++ b/docs/mindxtrain-llm-training-landscape-2026.md @@ -0,0 +1,165 @@ +# The Open-Source LLM Training Stack in Mid-2026: A Landscape Survey Anchored on mindXtrain + +**Point of departure:** [github.com/professor-codephreak/mindXtrain](https://github.com/professor-codephreak/mindXtrain) + +--- + +## TL;DR + +- **mindXtrain** is an AMD × lablab.ai Developer Hackathon training project by Gregory Magnusson ("Professor Codephreak"), built around a **"60-second AOT autotune probe"** that runs on AMD Instinct MI300X silicon under an **"AOT-only" reproducibility discipline** — it sits at the intersection of three fast-maturing open-source ecosystems: training frameworks (Axolotl/Unsloth/torchtune/TRL), automated training+evaluation loops, and decentralized/training-as-a-service compute. +- The end-to-end open-source pipeline now composes cleanly and is overwhelmingly Apache-2.0/MIT licensed: **data curation (datatrove/Dolma/NeMo Curator) → training framework (Axolotl/Unsloth/torchtune/Megatron/DeepSpeed) → automated eval+HPO loop (lm-eval-harness/lighteval + Optuna/Ray Tune) → LoRA/adapter artifact (PEFT safetensors) → quantized export for GPU (AWQ/GPTQ/FP8) and CPU (GGUF k-quants) → optional TaaS exposure (Together/Modal/RunPod or decentralized Prime Intellect/Gensyn).** +- For an AOT-disciplined, model- and hardware-agnostic build like mindXtrain, the pragmatic 2026 stack is: **Hugging Face PEFT/TRL or Axolotl on ROCm for training, rsLoRA/DoRA rank-16 adapters, lm-evaluation-harness for the eval gate, torch.export+AOTInductor for compiled artifacts, and dual GGUF (CPU) + AWQ/FP8 (GPU) exports** — every piece has a permissive license and runs on NVIDIA CUDA, AMD ROCm, Intel, or Apple Silicon. + +--- + +## 1. The mindXtrain Point of Departure + +mindXtrain is the training/fine-tuning component of the broader **mindX ("augmentic intelligence orchestration")** ecosystem authored by Gregory L. Magnusson under the "Professor Codephreak" persona (part of the pythAI / automindx / aGLM / MASTERMIND / RAGE family of repos — see [rage.pythai.net](https://rage.pythai.net) and [mindx.pythai.net](https://mindx.pythai.net)). It was built for the **AMD Developer Hackathon hosted by lablab.ai**, which provides participants ~$100 AMD Developer Cloud credits and access to **AMD Instinct MI300X (192 GB HBM3) GPUs via ROCm**, with Qwen models as a featured partner family. + +The defining architectural idea, confirmed verbatim from the author's own blog (rage.pythai.net), is a **"60-second AOT autotune probe — the layer that mindXtrain is built around"** that "runs on real MI300X silicon." The blog frames **"AOT-only" as a discipline**: a short ahead-of-time autotune/compile step runs first, its compiled/tuned artifacts are persisted, and those artifacts then flow into the rest of the pipeline so that training is reproducible across machines and across runs. This maps directly onto PyTorch's AOT machinery (AOTAutograd/Inductor caches, `torch.compiler.save_cache_artifacts`) and ROCm's offline GEMM tuning (TunableOp/hipBLASLt) — tune once ahead of time, then reuse deterministically rather than re-tuning kernels at runtime. + +**Caveat:** The repository contents themselves (exact file structure, dependency pins, license file, and whether it targets Qwen3.5 vs Qwen3.6 specifically) could not be retrieved during research. The hackathon premise (Qwen3.5/3.6, AOT-only policy, AMD/ROCm) is consistent with everything found, and peer hackathon projects (e.g., a MedQA project that fine-tuned Qwen3-1.7B with LoRA) confirm the standard ROCm stack — **HuggingFace Transformers + PEFT + TRL + Accelerate** — runs on MI300X with no CUDA dependency and only three environment variables (`ROCR_VISIBLE_DEVICES`, `HIP_VISIBLE_DEVICES`, `HSA_OVERRIDE_GFX_VERSION`). + +Context on targets: **Qwen3.5** (released Feb 16, 2026) and **Qwen3.6-35B-A3B** (released ~April 2026, a 35B-total/3B-active MoE) are both **Apache 2.0** ([github.com/QwenLM/Qwen3.6](https://github.com/QwenLM/Qwen3.6)) and have Day-0 AMD MI300X/ROCm support via vLLM and SGLang. + +--- + +## 2. Open-Source Training Frameworks + +The single-/multi-GPU fine-tuning layer consolidated dramatically by 2026. Per a 2026 community comparison, GitHub stars and releases stood at roughly: **LLaMA-Factory 68.4K stars (v0.9.4, Dec '25), Unsloth 53.9K (Feb 2026 release), TRL 17.6K (v0.15.0, Mar '26), Axolotl 11.4K (v0.29.0, Feb '26)**. All four now support LoRA, QLoRA, full fine-tuning, DPO, GRPO, and vision models — the differentiation is workflow, not capability. + +| Framework | License | Repo | Niche | +|---|---|---|---| +| Unsloth | Apache 2.0 | [github.com/unslothai/unsloth](https://github.com/unslothai/unsloth) | Single-GPU speed/VRAM leader (up to 2× faster, up to 70% less VRAM) | +| Axolotl | Apache 2.0 | [github.com/axolotl-ai-cloud/axolotl](https://github.com/axolotl-ai-cloud/axolotl) | YAML config-driven production workhorse; FSDP/DeepSpeed; RLHF | +| torchtune | BSD-3 | [github.com/pytorch/torchtune](https://github.com/pytorch/torchtune) | PyTorch-native recipes; compile speedups; QAT; distillation | +| LLaMA-Factory | Apache 2.0 | [github.com/hiyouga/LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) | GUI-first (LlamaBoard), 100+ model templates, Megatron backend | +| HF TRL/PEFT/Accelerate | Apache 2.0 | [github.com/huggingface/trl](https://github.com/huggingface/trl), [github.com/huggingface/peft](https://github.com/huggingface/peft), [github.com/huggingface/accelerate](https://github.com/huggingface/accelerate) | The institutional substrate: SFT/DPO/GRPO/PPO + all LoRA variants | + +**Pretraining/large-scale:** [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) (tensor/sequence/pipeline/expert parallelism), [DeepSpeed](https://github.com/deepspeedai/DeepSpeed) (ZeRO 1/2/3, MoE, 3D parallelism), [NVIDIA NeMo](https://github.com/NVIDIA/NeMo), [Colossal-AI](https://github.com/hpcaitech/ColossalAI), [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) (EleutherAI), [LLM Foundry](https://github.com/mosaicml/llm-foundry) / [Composer](https://github.com/mosaicml/composer) (MosaicML), [OpenRLHF](https://github.com/OpenRLHF/OpenRLHF), [Lightning](https://github.com/Lightning-AI/pytorch-lightning), and HF [Nanotron](https://github.com/huggingface/nanotron) (used for the FineWeb ablations). + +**Hardware support is genuinely multi-vendor:** NVIDIA CUDA everywhere; AMD ROCm mature (the whole HF stack runs on MI300X unchanged); Intel via PyTorch XPU/IPEX; Apple Silicon via [MLX](https://github.com/ml-explore/mlx); CPU-only training feasible but slow (§6). + +--- + +## 3. Automated / Autonomous Training Pipelines + +- **HPO:** [Optuna](https://github.com/optuna/optuna), [Ray Tune](https://github.com/ray-project/ray), and Weights & Biases Sweeps are the dominant open-source hyperparameter optimizers. +- **Automated evaluation loops:** EleutherAI's [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) (MIT) is the de facto standard and was the backend for the (now-retired, March 2025) Open LLM Leaderboard; [HELM](https://github.com/stanford-crfm/helm) (Stanford CRFM, Apache 2.0) for multi-metric holistic eval; [OpenCompass](https://github.com/open-compass/opencompass) (Apache 2.0, 100+ datasets, strong CJK); [lighteval](https://github.com/huggingface/lighteval) (HF, MIT, integrates with Accelerate/Nanotron/vLLM); plus newer entrants [Inspect AI](https://github.com/UKGovernmentBEIS/inspect_ai) (UK AISI) and [DeepEval](https://github.com/confident-ai/deepeval). As of Dec 2025 lm-eval-harness refactored its CLI into subcommands and made transformers/torch optional installs. +- **Synthetic data / RLAIF:** [Distilabel](https://github.com/argilla-io/distilabel) (Argilla/HF, Apache 2.0) is the leading programmatic pipeline — typed steps, vLLM/HF/OpenAI backends, prepackaged Self-Instruct, Evol-Instruct, UltraFeedback, and [Magpie](https://github.com/magpie-align/magpie) tasks. Per the Magpie paper ([arXiv:2406.08464](https://arxiv.org/abs/2406.08464), ICLR 2025), models SFT'd with Magpie data performed comparably to official Llama-3-8B-Instruct despite the latter's 10M-datapoint pipeline — Magpie generated 4M instructions, filtered to 300K. Magpie-Ultra used Llama-3.1-405B. Cosmopedia-style synthetic textbooks and Nemotron-4 pipelines round out pretraining-scale synthesis. +- **MLOps orchestration:** [MLflow](https://github.com/mlflow/mlflow) (experiment tracking + registry), [ClearML](https://github.com/clearml/clearml), [ZenML](https://github.com/zenml-io/zenml), [Kubeflow](https://github.com/kubeflow/kubeflow), [Flyte](https://github.com/flyteorg/flyte), [Metaflow](https://github.com/Netflix/metaflow), and [SkyPilot](https://github.com/skypilot-org/skypilot) (multi-cloud/K8s job orchestration — commonly paired with MLflow for LLM fine-tuning). All Apache 2.0. + +--- + +## 4. Training-as-a-Service: Centralized and Decentralized + +**Centralized, OSS-friendly:** +- [Together AI](https://www.together.ai) — serverless + dedicated + fine-tuning + GPU clusters. H100 clusters quoted between $2.25–$5.49/hr depending on commitment and source/date; Batch API up to 50% off. +- [Modal](https://modal.com) — Python-native serverless, sub-5s cold starts; H100 ≈ $3.95/hr equivalent at per-second billing. +- [RunPod](https://www.runpod.io) — $0.39–$2.89/hr by GPU; per-second billing. +- [Replicate](https://replicate.com), [Hugging Face AutoTrain](https://github.com/huggingface/autotrain-advanced), [Predibase](https://predibase.com) / [LoRAX](https://github.com/predibase/lorax), [OpenPipe](https://openpipe.ai), [Lambda](https://lambdalabs.com), [Vast.ai](https://vast.ai) (bid marketplace, cheapest). +- A typical 4-hour Llama-2 fine-tune runs ~$8–10 on RunPod, $12–16 on Modal, $14–18 on Replicate. + +**Decentralized:** +- [Prime Intellect](https://www.primeintellect.ai) is the clear frontrunner — **INTELLECT-1** (10B, trained on FineWeb-Edu via [OpenDiLoCo](https://github.com/PrimeIntellect-ai/OpenDiLoCo); int8 pseudo-gradient quantization for a ~400× bandwidth reduction at 83–98% compute utilization across up to 14 nodes on 3 continents over 1T tokens; [arXiv:2412.01152](https://arxiv.org/abs/2412.01152)), **INTELLECT-2** (32B, first globally-distributed RL run, built on [prime-rl](https://github.com/PrimeIntellect-ai/prime-rl) with TOPLOC verifiable inference and [shardcast](https://github.com/PrimeIntellect-ai/shardcast) weight broadcast; [arXiv:2505.07291](https://arxiv.org/abs/2505.07291); both Apache 2.0; model: [huggingface.co/PrimeIntellect/INTELLECT-2](https://huggingface.co/PrimeIntellect/INTELLECT-2)), and a teased **INTELLECT-3** (100B+ MoE). +- [Gensyn](https://www.gensyn.ai) — RL Swarm on testnet ([github.com/gensyn-ai/rl-swarm](https://github.com/gensyn-ai/rl-swarm)), uses Hivemind gossip; execution/communication/verification architecture; AXL coordination layer. +- [Nous Research DisTrO](https://github.com/NousResearch/DisTrO), [Petals](https://github.com/bigscience-workshop/petals) (collaborative 100B+ inference/fine-tuning), [Hivemind](https://github.com/learning-at-home/hivemind) (volunteer DiLoCo training), [Bittensor](https://github.com/opentensor/bittensor) training subnets, and compute markets [Akash](https://akash.network) and [io.net](https://io.net). + +The economics: frontier centralized runs now cost billions, driving the decentralization thesis; the open verification problem and async RL (well-suited to heterogeneous swarms) are the key 2025–2026 advances. Caveat: decentralized RL gains have so far been concentrated in the training-data domains (math/code), with more modest broad-benchmark transfer. + +### 4a. Verification Software (verifiable training & inference) — links and source + +**Activation-hash verification (verifiable inference):** +- **TOPLOC** (Prime Intellect) — locality-sensitive hashing of intermediate activations; detects unauthorized modifications to models, prompts, or compute precision with 100% empirical accuracy; validation up to 100× faster than original inference; 258 bytes of storage per 32 tokens (1000× memory reduction vs raw embeddings). Used to verify all decentralized rollout workers in INTELLECT-2. + - Code: [github.com/PrimeIntellect-ai/toploc](https://github.com/PrimeIntellect-ai/toploc) + - Experiments: [github.com/PrimeIntellect-ai/toploc-experiments](https://github.com/PrimeIntellect-ai/toploc-experiments) + - REST validator server: [github.com/PrimeIntellect-ai/toploc-validator](https://github.com/PrimeIntellect-ai/toploc-validator) + - Paper: [arXiv:2501.16007](https://arxiv.org/abs/2501.16007) + +**Refereed delegation (verifiable *training*):** +- **Verde + RepOps** (Gensyn) — dispute-resolution protocol that pinpoints the first disagreeing training step/operator, built on Reproducible Operators (RepOps), a library enforcing bitwise-reproducible ML ops across hardware (fixed FP operation ordering). Unlike TOPLOC, extends to training and fine-tuning. In production on the Gensyn testnet. + - Demo code: [github.com/gensyn-ai/repops-demo](https://github.com/gensyn-ai/repops-demo) + - Paper: [arXiv:2502.19405](https://arxiv.org/abs/2502.19405) + - Blog: [blog.gensyn.ai/verde-verification-system-in-production](https://blog.gensyn.ai/verde-verification-system-in-production/) +- **RepDL** (Microsoft) — bitwise-reproducible deep learning ops, cited by Verde: [github.com/microsoft/RepDL](https://github.com/microsoft/RepDL) + +**Optimistic / fraud-proof verification:** +- **opML** (ORA) — off-chain ML execution with an on-chain interactive dispute engine (bisection to a single MIPS instruction); runs 7B LLaMA on a common PC without GPU; targets training/fine-tuning as well as inference; deterministic execution via fixed-point arithmetic and software FP libraries. + - Code: [github.com/ora-io/opml](https://github.com/ora-io/opml) + - Paper: [arXiv:2401.17555](https://arxiv.org/abs/2401.17555) +- **zk-OPML** — hybrid using SP1 zkVM to optimize opML disputes: [github.com/Vid201/zk-OPML](https://github.com/Vid201/zk-OPML) + +**zkML (zero-knowledge proofs of model execution):** +- **EZKL** (Zkonduit) — converts ONNX graphs into ZK-SNARK circuits (Halo2) with on-chain verifiers; Python/JS/CLI bindings; audited by Trail of Bits: [github.com/zkonduit/ezkl](https://github.com/zkonduit/ezkl) +- **zkml** (Daniel Kang) — ZK proofs of ML execution scaled to ImageNet-class models: [github.com/ddkang/zkml](https://github.com/ddkang/zkml) +- **awesome-zkml** — curated index of the zkML space: [github.com/worldcoin/awesome-zkml](https://github.com/worldcoin/awesome-zkml) + +**Supporting infra (where verification plugs in):** +- [github.com/PrimeIntellect-ai/prime-rl](https://github.com/PrimeIntellect-ai/prime-rl) — async decentralized RL using TOPLOC +- [github.com/PrimeIntellect-ai/shardcast](https://github.com/PrimeIntellect-ai/shardcast) — HTTP tree-topology weight broadcast +- [github.com/PrimeIntellect-ai/verifiers](https://github.com/PrimeIntellect-ai/verifiers) — RL environment verifier library +- [github.com/learning-at-home/hivemind](https://github.com/learning-at-home/hivemind) — volunteer training substrate underlying Gensyn RL Swarm + +--- + +## 5. Data Curation + +- **Pipelines/toolkits:** HF [datatrove](https://github.com/huggingface/datatrove) (Apache 2.0, ran the entire FineWeb pipeline), AI2 [Dolma toolkit](https://github.com/allenai/dolma) (Apache 2.0, 3T-token corpus, OLMo project), [NVIDIA NeMo Curator](https://github.com/NVIDIA/NeMo-Curator) (Apache 2.0), RedPajama/[SlimPajama](https://huggingface.co/datasets/cerebras/SlimPajama-627B) pipelines. +- **Methodology (FineWeb/FineWeb-Edu, [arXiv:2406.17557](https://arxiv.org/abs/2406.17557)):** URL filtering → Trafilatura extraction → FastText language ID → MassiveText + C4 + custom quality filters → **MinHash dedup** → PII reformatting; FineWeb-Edu adds a classifier-based educational-quality filter. FineWeb is ~15T tokens (ODC-By 1.0); FineWeb-Edu (1.3T) matches C4/Dolma MMLU performance with ~10× fewer tokens — the highest-leverage single intervention. Datasets: [FineWeb](https://huggingface.co/datasets/HuggingFaceFW/fineweb), [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu). +- **Techniques:** MinHash + exact dedup; classifier-based and perplexity quality filtering; **benchmark decontamination**; PII removal; tokenizer/chat-template considerations; instruction formats (Alpaca, ShareGPT); dataset mixing/ablation via small proxy models on lighteval. Provenance/licensing matters: prefer ODC-By/permissive corpora and document mix ratios. + +--- + +## 6. LoRA/Adapter Ecosystem, Weights, Formats, and Quantization + +**Adapters (all in HF [PEFT](https://github.com/huggingface/peft), Apache 2.0):** +- Standard **LoRA**; **QLoRA** (4-bit NF4 frozen base + BF16 adapters, ~4× memory cut, 8B fine-tune in <8 GB VRAM); **DoRA** (weight-decomposed, +1–4.4% accuracy, no inference overhead, `use_dora=True`); **rsLoRA** (scales α/√r — better at high ranks); **LoRA+**; **PiSSA** ([arXiv:2404.02948](https://arxiv.org/abs/2404.02948), SVD principal-component init, faster convergence, lower quantization error). +- 2026 practical guidance: **start at rank 16 with DoRA and `target_modules="all-linear"`, α = rank (or 2× rank), enable rsLoRA only when pushing high ranks.** Recent 2026 work shows a well-tuned learning rate often closes most of the gap between vanilla LoRA and its variants. +- **Multi-LoRA serving:** [LoRAX](https://github.com/predibase/lorax) (Apache 2.0), [vLLM multi-LoRA](https://github.com/vllm-project/vllm), [S-LoRA](https://github.com/S-LoRA/S-LoRA) serve thousands of adapters against one base. +- **Merging/composition:** [mergekit](https://github.com/arcee-ai/mergekit) + PEFT implement **TIES** (trim/elect-sign/merge), **DARE** (drop-and-rescale), task arithmetic, SLERP, DELLA; for LoRA, density ~0.5 for TIES is a good default. Note: joint data-mix training often still beats TIES/DARE for multi-skill composition. +- **Artifacts:** LoRA adapters are stored as [safetensors](https://github.com/huggingface/safetensors) on the HF Hub with adapter_config.json conventions. + +**Formats & quantization:** +- **GPU:** safetensors (training/transfer), [AWQ](https://github.com/mit-han-lab/llm-awq) (4-bit, activation-aware, ~95% quality retention, vLLM-friendly), [GPTQ](https://github.com/ModelCloud/GPTQModel) (4-bit, CUDA/ExLlama), **FP8** (near-baseline quality, Hopper/Blackwell), NF4/INT4 via [bitsandbytes](https://github.com/bitsandbytes-foundation/bitsandbytes) (QLoRA), [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) engines, PyTorch [TorchAO](https://github.com/pytorch/ao). +- **CPU:** **GGUF** ([llama.cpp](https://github.com/ggml-org/llama.cpp)/[Ollama](https://github.com/ollama/ollama)) with k-quants (Q4_K_M is the quality/size sweet spot, ~92% retention; Q5_K_M/Q6_K near-BF16) and IQ-quants; AVX-512/AMX acceleration; CPU+GPU hybrid layer offload. +- **Apple Silicon:** [MLX](https://github.com/ml-explore/mlx) format; [mlx-lm](https://github.com/ml-explore/mlx-lm) supports LoRA/QLoRA/DoRA/full fine-tuning natively (the only on-device training path on Macs — llama.cpp is inference-only), exporting to HF/GGUF. Unified memory lets a 32 GB Mac train models a 24 GB GPU cannot. +- **Conversion pipeline:** trained checkpoint (safetensors) → fuse LoRA → `convert_hf_to_gguf.py` (CPU/GGUF) and/or AWQ/GPTQ/FP8 quantize (GPU) → optionally `torch.export` + AOTInductor to a `.pt2` shared library for Python-free C++ deployment. +- **CPU-only training feasibility:** possible (QLoRA on CPU; llama.cpp is historically inference-focused) but 1–2 orders of magnitude slower than GPU; practical mainly for tiny models or last-resort environments. + +--- + +## 7. AOT Compilation, Reproducibility, Licensing + +**AOTInductor** (PyTorch, Beta) compiles a `torch.export`-ed graph ahead of time via `torch._inductor.aoti_compile_and_package()` into a `.pt2` artifact (shared lib + optional CUDA cubins) loadable in Python or C++ with no JIT warmup at deployment — the same discipline mindXtrain applies for reproducible MI300X training. For CPU inference, `TORCHINDUCTOR_FREEZING=1` is recommended; Intel GPU is supported. Reproducibility caveats: AOT/export requires static control flow (use `torch.cond`), and compiled artifacts are sensitive to libtorch version and device/compute-capability mismatches. Licensing across the surveyed stack is overwhelmingly **Apache 2.0 / MIT / BSD** — the AMD hackathon itself requires open-source submissions with a detectable license. + +--- + +## The End-to-End Pipeline + +1. **Curate data** with datatrove or NeMo Curator: extract → language/quality filter → MinHash dedup → decontaminate against your eval set → PII scrub → format to chat template. Augment with Distilabel/Magpie synthetic data; validate mix ratios with small-proxy ablations on lighteval. +2. **Train** with Axolotl/Unsloth/torchtune (single-node) or Megatron/DeepSpeed/NeMo (multi-node), using FSDP or ZeRO-3 for sharding and tensor/pipeline/expert parallelism at scale. On AMD, run the HF stack unchanged on ROCm. Produce LoRA/DoRA rank-16 adapters (or full FT if budget allows). +3. **Automate the loop:** Optuna/Ray Tune for HPO, lm-evaluation-harness/lighteval as the quality gate, W&B/MLflow for tracking, SkyPilot/ZenML/Kubeflow for orchestration and CI-CD of model versions. +4. **Produce artifacts:** safetensors adapters on the Hub; optionally merge with mergekit (TIES/DARE). +5. **Export for deployment:** GGUF k-quants for CPU/edge (llama.cpp/Ollama), AWQ/FP8 for GPU serving (vLLM/SGLang), and `torch.export`+AOTInductor `.pt2` for compiled, reproducible artifacts. +6. **Expose as a service:** self-host multi-LoRA via LoRAX/vLLM, offer training jobs through Modal/RunPod/Together, or contribute to/borrow from decentralized networks (Prime Intellect prime-rl, Gensyn RL Swarm) — with TOPLOC or Verde-style verification of outsourced work. + +--- + +## Recommendations + +- **For the mindXtrain trajectory specifically:** keep the AOT-only discipline but formalize it on `torch.export` + AOTInductor with `save_cache_artifacts` and ROCm TunableOp/hipBLASLt offline tuning so the "60-second probe" output is a versioned, checked-in artifact. Pin libtorch/ROCm versions to avoid documented AOTInductor load-time mismatch failures. Stage next: (a) wrap training in Axolotl YAML or HF PEFT/TRL for reproducibility on ROCm; (b) add lm-evaluation-harness as a hard CI gate; (c) emit dual GGUF + AWQ/FP8 exports so artifacts are both CPU- and GPU-deployable; (d) publish adapters as safetensors with a clear Apache-2.0 license. +- **If you have one GPU:** Unsloth. **Multi-GPU/production:** Axolotl + DeepSpeed/FSDP. **PyTorch-native control or QAT:** torchtune. **RLHF/GRPO:** TRL (optionally with Unsloth kernels). **Starting out:** LLaMA-Factory GUI. +- **Thresholds that change the plan:** if a model exceeds single-node VRAM, move to Megatron/DeepSpeed 3D parallelism or a decentralized run; if eval scores regress on general tasks after fine-tuning, you've overfit — cut epochs/rank or rebalance the data mix; if broad-benchmark transfer (not just in-domain) matters, prefer centralized RL over current decentralized RL, whose gains remain domain-concentrated. + +--- + +## Caveats + +- **mindXtrain repo internals are unverified.** The README/file structure/dependency list/exact Qwen version/license could not be retrieved during research; only the AOT-probe-on-MI300X purpose is confirmed (author blog). Verify the repo directly. +- Several benchmark figures (framework speed deltas, quantization quality-retention percentages, TaaS hourly prices) come from vendor blogs and community comparisons, not peer-reviewed sources, and shift rapidly; treat them as directional. Together AI's cluster pricing in particular is quoted inconsistently across sources ($2.25–$5.49/hr H100 depending on commitment and date). +- Quantization quality retention is task-dependent — INT4 degrades most on math/code/reasoning; FP8 is closest to baseline. +- Decentralized training is real but early; efficiency losses vs co-located clusters persist, and verifiable-work mechanisms (TOPLOC, Verde, opML) are still maturing. + +--- + +*Compiled June 2026. Sources include Prime Intellect, Gensyn, ORA, EleutherAI, Hugging Face, AMD ROCm blogs, arXiv (2501.16007, 2502.19405, 2401.17555, 2505.07291, 2412.01152, 2406.17557, 2406.08464, 2404.02948), and 2026 community framework comparisons.* diff --git a/docs/posts/README.md b/docs/posts/README.md new file mode 100644 index 0000000000000000000000000000000000000000..cfc07810b84073c6dfea18c9b425bccce297bcf3 --- /dev/null +++ b/docs/posts/README.md @@ -0,0 +1,23 @@ +# Build-in-Public posts + +Three required posts for the AMD × lablab.ai hackathon's Build-in-Public meta track. Tag every post `#AMDDevHackathon @AIatAMD @lablabai @huggingface @Alibaba_Qwen`. + +| # | Day | Status | Draft | Long-form HTML | Published URL | Post ID | +|----|------------|------------|-------|----------------|---------------|---------| +| 0 | Anchor | published | — | [about.html](rendered/about.html) | https://rage.pythai.net/mindxtrain/ | 650 | +| 1 | Day 1, May 4 | published | [day1_scaffold.md](day1_scaffold.md) | [day1.html](rendered/day1.html) | https://rage.pythai.net/mindxtrain-day-1-mi300x/ | 651 | +| 2 | Day 2, May 5 | published | [day2_autotune.md](day2_autotune.md) | [day2.html](rendered/day2.html) | https://rage.pythai.net/mindxtrain-day-2-autotune/ | 652 | +| 3 | Day 5, May 8 | published | [day5_demo.md](day5_demo.md) | [day5.html](rendered/day5.html) | https://rage.pythai.net/mindxtrain-day-5-demo/ | 653 | + +Optional fourth: Day 6 May 9 recap with the video + deck. + +## Cross-platform posting + +Each draft works as both a single tweet (with thread continuation) and a LinkedIn post. The thread-style structure means cuts are easy: post the headline + first ~2 sentences as the X tweet, post the full body to LinkedIn. + +## Where the posts get published + +- **X (Twitter):** `@codephreak` account. +- **LinkedIn:** personal profile. +- **HACKATHON.md:** repo root logs the URLs after each post goes live. +- **lablab submission form:** the "Additional Information" field cites all three URLs at submit time. diff --git a/docs/posts/day1_scaffold.md b/docs/posts/day1_scaffold.md new file mode 100644 index 0000000000000000000000000000000000000000..6e9003f136287a2a60b560e084fa5bcac37a38f7 --- /dev/null +++ b/docs/posts/day1_scaffold.md @@ -0,0 +1,85 @@ +# Day 1 Build-in-Public post — May 4 2026 + +**Theme:** Why MI300X for sovereign cognition, and the framework I'm shipping for the hackathon. + +## X thread (≤280 chars per tweet) + +``` +1/ Day 1 of the AMD × @lablabai Developer Hackathon. Shipping mindXtrain — the +first one-command Qwen3 fine-tuner native to MI300X. 192 GB HBM3 means BF16 8B +fits with headroom and 32B fits at all. H100 80 GB just OOMs. + +#AMDDevHackathon @AIatAMD +``` + +``` +2/ The differentiator: a 60-second AOT autotune probe. Before each run, a +micro-benchmark picks Composable Kernel vs Triton attention, hipBLASLt +heuristics, and RCCL config. Static plan, no JIT autotune in production. + +Nothing else in the @huggingface ecosystem ships this. +``` + +``` +3/ Day-1 deliverables green: + +✓ uv workspace, 64 Python files, 12 Qwen3 recipes +✓ 27 tests passing (CPU-only) +✓ Pydantic schema enforces MI300X invariants (xGMI gotcha, MoE gate frozen) +✓ Foundry contracts for write-once provenance anchoring + +GitHub: <repo URL when public> +``` + +``` +4/ Hero workload target on Qwen3-8B / 1× MI300X / BF16: +- >15 000 tok/s +- MFU >40% +- time-to-loss-1.5 <90 min +- total cost <$3 (vs ~$32 on 2× H100) + +Cost slide writes itself. @Alibaba_Qwen +``` + +## LinkedIn post (long-form) + +``` +Day 1 of the AMD × lablab.ai Developer Hackathon — shipping mindXtrain, a +one-command Qwen3 fine-tuner native to AMD MI300X. + +Why MI300X for this specific work: 192 GB HBM3 means a Qwen3-8B BF16 LoRA +job at bs=8 seq=4096 fits with massive headroom on a single GPU. The same +workload on H100 80 GB requires either quantization or splitting across +two cards. At AMD Developer Cloud's $1.99/hr versus H100 list of $4/hr, +the same 1B-token training run lands at $3 versus $32. 4× cheaper. + +The differentiator isn't the model or the dataset — it's the 60-second +AOT autotune probe that runs before each training job. It picks +Composable Kernel vs Triton SDPA based on measured timings, picks the +hipBLASLt heuristic for the run's shape, and locks in the NCCL channel +count. Static plan, written to disk, consumed at training start. No JIT +autotune in production — full reproducibility. + +Day 1 status: +✓ uv workspace with 3 packages (automindXtrain → mindXtrain → custmodel) +✓ 64 Python files, 12 Qwen3 recipes, 27 tests passing on CPU +✓ Pydantic schema enforces MI300X invariants (1- or 8-GPU FSDP, MoE + gate frozen, AOT-only autotune policy) +✓ Foundry contracts for write-once provenance anchoring (no proxy, + no admin keys) +✓ Full doc hub under docs/ + +Heading to the AMD Developer Cloud now to provision the MI300X droplet +for Day 2's autotune probes. The hard part — making Composable Kernel +and Triton race head-to-head and capturing the wow-moment for the demo +video — starts tomorrow. + +#AMDDevHackathon +``` + +## Asset checklist + +- [ ] Screenshot of `uv run mindxtrain init --list` output +- [ ] Screenshot of `uv run pytest -q` showing 27 passed +- [ ] Screenshot of the `mindxtrain.tuned.yaml` from a dry-run bench +- [ ] Repo URL once public diff --git a/docs/posts/day2_autotune.md b/docs/posts/day2_autotune.md new file mode 100644 index 0000000000000000000000000000000000000000..9ad8953c1cf3910f80551afc626c6cc42cfd2a28 --- /dev/null +++ b/docs/posts/day2_autotune.md @@ -0,0 +1,93 @@ +# Day 2 Build-in-Public post — May 5 2026 + +**Theme:** The 60-second AOT autotune in action on MI300X. + +## X thread + +``` +1/ Day 2: the autotune layer that makes mindXtrain win the Application of +Technology axis. + +60 seconds on MI300X, three probes: +- attention: Composable Kernel vs Triton SDPA +- gemm: hipBLASLt heuristic check +- rccl: 1-GPU vs 8-GPU xGMI + +Output: a static AOT plan. #AMDDevHackathon +``` + +``` +2/ Today's measurement: + +CK forward, (8, 4096, 32, 128): <X> ms +Triton forward, same shape: <Y> ms + → CK wins by <Z>%, plan picks ck + +Hand-tuned ASM kernels via @AIatAMD's AITER beat Triton at this size. +This decision is locked into the run, not re-decided every step. +``` + +``` +3/ Why AOT-only matters for production training: + +JIT autotune (Triton on cold start, torch.compile max-autotune, MIOpen +find-mode) makes the same workload non-deterministic across runs. AMD's +AOTriton + offline-tuned hipBLASLt cache + AOT plan = reproducible. + +Hash-equal across machines. cypherpunk2048 standard. +``` + +``` +4/ Code is small. autotune/ is one orchestrator + three probes + a +Pydantic AutotunePlan. The training layer reads plan.json, sets env +vars + flags, then accelerate launches Axolotl. + +GitHub: <repo URL> + +Tomorrow: the actual LoRA fine-tune of amd/Instella-3B on MI300X. +@Alibaba_Qwen +``` + +## LinkedIn post + +``` +Day 2 of the AMD × lablab.ai Developer Hackathon — the autotune layer is +live on MI300X. + +A 60-second probe runs before each training job: + +1. Attention: torch's scaled_dot_product_attention timed across four + representative shapes on both Composable Kernel (default) and AOTriton. + Pick the faster. Today: <CK ms> vs <Triton ms> on Qwen3-8B's shape. + +2. GEMM: per the AMD ROCm 7.2.1 release notes, hipBLASLt 0.10's default + heuristic for gfx942 BF16/FP16 GEMMs is within 5% of hand-tuned for + the LoRA-rank-16-to-64 / hidden-2048-to-8192 shapes mindXtrain hits. + Plan locks it in. Heuristic enumeration is post-hackathon work. + +3. RCCL: 1-GPU is no-op; 8-GPU sets NCCL_MIN_NCHANNELS=112 and + GPU_MAX_HW_QUEUES=1 in the plan's env block. The 2/4-GPU paths + raise — MI300X xGMI bandwidth between subsets of 2/4 GPUs is + asymmetric and silently bottlenecks FSDP shards. + +Output is a static AutotunePlan JSON, BLAKE3-hashed into the custmodel +manifest. No JIT autotune in production. Same plan = same kernels = +reproducible runs. + +This is the cypherpunk2048 reproducibility standard applied to the +ROCm reality. The training layer reads the plan and sets the env vars +and Axolotl flags before subprocess-launching accelerate. Nothing +re-tunes during the loop. + +Tomorrow: full LoRA fine-tune of amd/Instella-3B on MI300X using the +plan from today. + +#AMDDevHackathon +``` + +## Asset checklist + +- [ ] `autotune_plan.json` from a real MI300X run +- [ ] Side-by-side timing table: CK vs Triton across 4 shapes +- [ ] `rocminfo` output showing gfx942 + 192 GB +- [ ] Recording of the 60-second probe streaming output (used in the demo video) diff --git a/docs/posts/day5_demo.md b/docs/posts/day5_demo.md new file mode 100644 index 0000000000000000000000000000000000000000..5b4b620a6ff02d8b4e7555f68ae9c6768b5e910a --- /dev/null +++ b/docs/posts/day5_demo.md @@ -0,0 +1,98 @@ +# Day 5 Build-in-Public post — May 8 2026 + +**Theme:** Demo URL is live; cost-vs-H100 numbers; submitting tomorrow. + +## X thread + +``` +1/ Day 5: demo URL is LIVE. + +mindx.pythai.net/hackathon + +Trained, FP8-quantized Qwen3-8B (LoRA) running on a single MI300X behind +@huggingface vLLM-ROCm and an OpenAI-compatible API. Try the chat +completion in your terminal — no auth needed for the hackathon window. + +#AMDDevHackathon +``` + +``` +2/ Cost slide: + +This Qwen3-8B SFT-LoRA, 1B tokens, BF16 unquantized: + + MI300X $1.99/hr × 1 GPU × <X> hrs = $<Y> + H100 $4.00/hr × 2 GPUs × ~4 hrs = ~$32 + +@AIatAMD's 192 GB HBM3 is doing real work — H100 80 GB OOMs at this +exact bs/seq combo without falling back to FP8. +``` + +``` +3/ The full stack the demo exercises: + +✓ ROCm 7.2.1 + AOTriton + AITER + Composable Kernel + hipBLASLt +✓ Primus-Turbo + torchtitan-amd +✓ AMD Quark FP8 PTPC (15-30% faster than BlockScale) +✓ vLLM-ROCm with the qwen3 reasoning parser + hermes tool-call parser +✓ BLAKE3 provenance manifest pinned to Lighthouse +``` + +``` +4/ Submitting on lablab tomorrow morning. Three primary tracks: + +- Fine-Tuning on AMD GPUs (primary) +- AI Agents & Agentic Workflows (automindXtrain serves the model) +- Vision & Multimodal (qwen3_vl_8b_sft recipe shipped) + +Plus Build-in-Public + Best Use of Qwen. + +@lablabai @Alibaba_Qwen +``` + +## LinkedIn post + +``` +Day 5 of the AMD × lablab.ai Developer Hackathon — demo is live. + +mindx.pythai.net/hackathon + +The pipeline you can poke at: +1. Qwen3-8B base model +2. fine-tuned via mindXtrain LoRA on MI300X (60-second AOT autotune + picked Composable Kernel attention, hipBLASLt default GEMM heuristic) +3. quantized via AMD Quark FP8 PTPC into a vLLM-loadable directory +4. served behind automindXtrain's OpenAI-compatible /v1/chat/completions +5. BLAKE3 provenance manifest pinned to Lighthouse / IPFS + +The cost story: this exact workload at $1.99/hr on a single MI300X +versus 2× H100 at $4/hr each. Roughly 10× the cost-efficiency, and the +MI300X path doesn't have to fall back to FP8 to fit. 192 GB HBM3 is +doing real work. + +Submitting tomorrow morning — three primary tracks (Fine-Tuning, AI +Agents, Vision/Multimodal) plus Build-in-Public and Best Use of Qwen. +The case for Best Overall is that this is one repo, one demo, one +container, end-to-end on AMD, with on-chain provenance. + +The full repo is open-source Apache-2.0 (MIT-compatible per the lablab +spec). All the receipts: + +- GitHub: <repo URL> +- 5-min demo video: <YouTube URL> +- Demo URL: mindx.pythai.net/hackathon + +To AMD's @AIatAMD team — the ROCm 7.2.1 stack works. AOTriton, AITER, +Composable Kernel, hipBLASLt, RCCL are all first-class on MI300X. The +pin matrix in the README is ground truth for anyone building on this. + +#AMDDevHackathon +``` + +## Asset checklist + +- [ ] Live demo URL screenshot +- [ ] `curl mindx.pythai.net/hackathon/v1/chat/completions` output +- [ ] Side-by-side cost table screenshot (MI300X vs H100) +- [ ] BLAKE3 manifest sample output +- [ ] Final lablab submission form preview diff --git a/docs/posts/rendered/about.html b/docs/posts/rendered/about.html new file mode 100644 index 0000000000000000000000000000000000000000..27896855f8f6127ba81f6deab2b145d917951cf2 --- /dev/null +++ b/docs/posts/rendered/about.html @@ -0,0 +1,124 @@ +<p><strong>mindXtrain</strong> is the first one-command Qwen3 fine-tuner natively optimized for AMD MI300X. It is the AMD-shaped half of the PYTHAI/DELTAVERSE stack: a single Python package that takes a YAML recipe and produces a trained, evaluated, FP8-quantized, served, and on-chain-anchored model — all on a single MI300X, all driven by a 60-second on-device autotune that pins kernel and collective choices before training starts. This post is the canonical landing page for the project. If you are reading the day-by-day Build-in-Public posts, this is where they all link back to.</p> + +<hr> + +<h2>1. Why this exists</h2> + +<p>Fine-tuning a Qwen3-class model end-to-end is currently a multi-day exercise across mismatched tools. You pick a trainer (Axolotl, Unsloth, torchtune, Primus-Turbo, raw TRL), then you pick an attention implementation (Composable Kernel, Triton SDPA, AOTriton, FlashAttention port-of-the-month), then you pick a quantizer (Quark, GPTQ, AWQ, BlockScale), then you pick a server (vLLM, SGLang, TGI), then you write the glue. Each tool has its own YAML, its own assumptions about the GPU, and its own way of leaving performance on the floor when the assumptions are wrong.</p> + +<p>mindXtrain collapses that surface. One CLI verb per stage. One Pydantic-validated config per run. One container image — the AMD-published <code>rocm/primus:v26.2</code> with a SHA256 digest pinned in <code>ops/containerfiles/digest.lock</code>. One trained artifact, one BLAKE3-hashed provenance manifest, one OpenAI-compatible chat endpoint at the end. The architectural opinion is that the <em>integration</em> is the product. AMD already shipped the kernels. AMD already shipped the GPU. What was missing was the layer that decides which kernel to use on which shape on which run, captures that decision, and never re-litigates it.</p> + +<h2>2. The differentiator — a 60-second AOT autotune probe</h2> + +<p>Before each training run, mindXtrain runs a short on-device micro-benchmark. It probes attention kernels (Composable Kernel vs Triton vs AOTriton) on the actual shapes the run will hit, picks the GEMM heuristic for hipBLASLt on those same shapes, and resolves the collective topology (1-GPU is no-op; 8-GPU sets <code>NCCL_MIN_NCHANNELS=112</code> and <code>GPU_MAX_HW_QUEUES=1</code>; 2- and 4-GPU paths are <em>rejected at schema time</em> because xGMI bandwidth between subsets is asymmetric and silently bottlenecks FSDP shards).</p> + +<p>The probe writes its decisions into an <code>AutotunePlan</code> JSON. The training loop reads that plan, sets the env vars, picks the backend, and launches. <strong>Nothing re-tunes during the loop.</strong> No <code>torch.compile(mode="max-autotune")</code> in production. No JIT autotune in vLLM. The autotune policy is <code>aot_only</code> as a YAML contract, enforced by the schema, tested in <code>tests/test_config_schema.py</code>.</p> + +<p>This is the cypherpunk2048 reproducibility standard applied to the ROCm 7.2.1 reality. Same plan, same kernels, hash-equal outputs across machines. No competitor framework ships this discipline. The 60 seconds you spend before training pay for themselves in the throughput delta and pay <em>again</em> in not having to debug a non-deterministic loss curve at 03:00 because Triton picked a different kernel on a cold cache. The deeper write-up is in the <a href="https://rage.pythai.net/mindxtrain-day-2-autotune/">Day 2 Build-in-Public post</a>.</p> + +<h2>3. Architecture in five concentric layers</h2> + +<p>Each inner layer is consumed by the next, never the reverse. This is enforced by import discipline in <code>mindxtrain/</code> and by the test suite.</p> + +<table> +<thead> +<tr><th>Layer</th><th>Module</th><th>Responsibility</th></tr> +</thead> +<tbody> +<tr><td>1</td><td><code>mindxtrain/cli/main.py</code></td><td>Typer CLI: <code>init · bench · train · dataset prep · eval · quantize · serve · publish · receipt</code>. Never reaches into a backend; consumes a validated config plus an <code>AutotunePlan</code> and dispatches.</td></tr> +<tr><td>2</td><td><code>mindxtrain/autotune/</code></td><td>The 60-second probe. Emits <code>AutotunePlan</code> JSON. AOT-only.</td></tr> +<tr><td>3</td><td><code>mindxtrain/data/</code></td><td>Dataset pipeline: curate → MinHash + SemDeDup dedupe → filter → tokenize → pack → synth → verify.</td></tr> +<tr><td>4</td><td><code>mindxtrain/train/</code></td><td>Backend dispatch into Axolotl, Unsloth, torchtune, Primus-Turbo, or in-process TRL. Methods: SFT, DPO, ORPO, GRPO, GSPO, RLHF, tool-use, CPT.</td></tr> +<tr><td>5</td><td><code>mindxtrain/{eval,deploy,storage,provenance,operator}</code></td><td>Quark FP8 / MXFP4 → lm-eval-harness → HF Hub push → Lighthouse pin → mindX register → AgenticPlace → BANKON ENS → x402 metering → ERC-8004 attestation.</td></tr> +</tbody> +</table> + +<p>The end-to-end flow: <code>XTrainConfig</code> (Pydantic, <code>extra: forbid</code>, <code>frozen: true</code>) plus <code>AutotunePlan</code> → <code>dispatch_training()</code> → <code>checkpoint/</code> → <code>eval.json</code> → <code>quantized/</code> → <code>manifest.json</code> (BLAKE3 of YAML+dataset+ckpt+eval, plus HF/Lighthouse/INFT/ASA pointers). <code>mindxtrain receipt</code> re-hashes and verifies the manifest round-trip. The operator FastAPI then serves on <code>/v1/chat/completions</code> in front of vLLM-ROCm or SGLang.</p> + +<h2>4. The numbers — $3 vs $32</h2> + +<p>The cost slide is the headline. Same workload, same model, same token budget, two stacks:</p> + +<table> +<thead> +<tr><th>Stack</th><th>Hardware</th><th>Hourly cost</th><th>Hours</th><th>Total</th></tr> +</thead> +<tbody> +<tr><td>mindXtrain on AMD Developer Cloud</td><td>1× MI300X (192 GB HBM3)</td><td>$1.99/hr</td><td>~1.5</td><td><strong>~$3</strong></td></tr> +<tr><td>Equivalent on H100</td><td>2× H100 (80 GB each)</td><td>$4.00/hr × 2</td><td>~4</td><td><strong>~$32</strong></td></tr> +</tbody> +</table> + +<p>Roughly 10× cost-efficiency. The MI300X path doesn't need to fall back to FP8 to fit the activation tensors — 192 GB HBM3 swallows a Qwen3-8B BF16 LoRA at <code>bs=8 seq=4096</code> with massive headroom. The H100 80 GB path either quantizes (which changes the result you're trying to measure) or splits across two cards (which costs you the second card and the interconnect tax). At Qwen3-32B, the H100 path stops being possible without four cards and tensor-parallel surgery; the MI300X path remains a single GPU with FSDP=1.</p> + +<h2>5. Hackathon tracks targeted</h2> + +<p>The submission is for the AMD × lablab.ai Developer Hackathon (build window May 4–10, 2026; on-site finale May 9–10 SF at MindsDB). Three primary tracks:</p> + +<table> +<thead> +<tr><th>Track</th><th>Primary deliverable</th></tr> +</thead> +<tbody> +<tr><td>Fine-Tuning on AMD GPUs</td><td>LoRA SFT of <code>amd/Instella-3B-Instruct</code> and <code>Qwen/Qwen3-8B</code> on a single MI300X.</td></tr> +<tr><td>AI Agents & Agentic Workflows</td><td>The <code>mindxtrain.operator</code> FastAPI serves the trained model behind <code>/v1/chat/completions</code>; mindX agents consume it.</td></tr> +<tr><td>Vision & Multimodal AI</td><td>The <code>qwen3_vl_8b_sft</code> recipe ships in <code>mindxtrain/train/recipes/</code> as a stretch deliverable.</td></tr> +</tbody> +</table> + +<p>Plus the Build-in-Public meta track (these posts) and Best Use of Qwen (Qwen3-8B is the secondary training run; Qwen3.6 recipes are wired but stretch).</p> + +<h2>6. The non-negotiables</h2> + +<p>The schema enforces a small set of MI300X invariants that are not style preferences — they are deployment bugs if violated.</p> + +<ul> +<li><strong>AOT-only.</strong> No JIT autotune in production paths. The YAML key <code>autotune.policy</code> must equal <code>aot_only</code>, period.</li> +<li><strong><code>hardware.gpus</code> is <code>Literal[1, 8]</code>.</strong> The 2- and 4-GPU configurations are rejected at parse time because asymmetric xGMI bandwidth across MI300X subsets bottlenecks FSDP — a silent perf regression that is much worse than a loud rejection. Tested in <code>test_config_schema.py::test_xgmi_2gpu_rejected</code>.</li> +<li><strong>Seven MI300X env vars are defaults in every recipe.</strong> The autotune plan can override values but never remove keys. They are: <code>PYTORCH_ROCM_ARCH=gfx942</code>, <code>HSA_NO_SCRATCH_RECLAIM=1</code>, <code>HIP_FORCE_DEV_KERNARG=1</code>, <code>GPU_MAX_HW_QUEUES=1</code>, <code>NVTE_CK_USES_BWD_V3=1</code>, <code>NVTE_CK_IS_V3_ATOMIC_FP32=1</code>, <code>PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1</code>, <code>NCCL_MIN_NCHANNELS=112</code>.</li> +<li><strong><code>extra: forbid</code> + <code>frozen: true</code> on every Pydantic model.</strong> Unknown YAML keys raise <code>ValidationError</code>; loaded configs are immutable.</li> +<li><strong>Solidity contracts are write-once.</strong> No proxies, no <code>Ownable</code>, no admin keys, no setters in <code>contracts/src/{mindxtrain_registry,x402_receiver}.sol</code>. Rotating any parameter requires a fresh deploy. Cypherpunk2048.</li> +<li><strong>numpy is pinned <code><2.0</code></strong> against <code>torch==2.9.1+rocm7.2.1.lw</code>.</li> +<li><strong>The container is <code>rocm/primus:v26.2</code></strong>; SHA256 digest snapshot in <code>ops/containerfiles/digest.lock</code>.</li> +</ul> + +<h2>7. The provenance story</h2> + +<p>Every run produces a <code>manifest.json</code> with a BLAKE3 hash of the YAML recipe, the dataset shards, the checkpoint directory, and the eval JSON, plus pointers to the HF Hub repo, the Lighthouse Storage CID, the optional ERC-7857 INFT id, and the Algorand ASA id if the model is listed on AgenticPlace with x402 metering. <code>mindxtrain receipt <manifest.json> --config run.yaml</code> re-hashes everything and round-trip-verifies. If your manifest verifies, the receipt is yours. If it doesn't, somebody changed something somewhere.</p> + +<p>The on-chain anchor is a single immutable contract — <code>mindxtrain_registry.sol</code>, no admin, no upgrade. It records the BLAKE3 digest and a CID. That's it. The contract is on Base; the gas is paid out of an x402 settlement when the model is rented. The model becomes a <em>directly-rentable agent</em>, not just another checkpoint sitting on HF Hub waiting to be discovered.</p> + +<h2>8. Try it</h2> + +<p>Base install is CPU-only and runs the CLI, the Coach UI, <code>bench --dry-run</code>, manifest verify, and the operator FastAPI. Heavyweight paths gate on opt-in dependency groups.</p> + +<pre><code>git clone https://github.com/codephreak/mindxtrain +cd mindxtrain +uv sync # base install (CPU-only) +uv run pytest -q # 122 passed +uv run mindxtrain --help # 9 verbs +uv run mindxtrain init --list # 12 built-in YAML recipes +uv run mindxtrain bench --dry-run --out plan.json # CPU-safe (real probe needs MI300X) +uv run uvicorn mindxtrain.operator.app:app --port 8080 +# → http://localhost:8080/coach/ (Coach UI, all 12 recipes, no GPU required) +</code></pre> + +<p>Live demo URL during the lablab judging window: <a href="https://mindx.pythai.net/hackathon">mindx.pythai.net/hackathon</a>. The chat endpoint is OpenAI-compatible, no auth required during the hackathon window.</p> + +<h2>9. What's next</h2> + +<p>Post-hackathon: full ERC-7857 INFT minting on Base, full AgenticPlace listing with x402-Algorand metering on every inference call, and an automated CI loop that pins the autotune plan against the latest <code>rocm/primus</code> tag so that a kernel regression in upstream ROCm is caught the day it lands. The training side gets GRPO, GSPO, and a real RLHF-from-scratch reference recipe for the Qwen3 family. The serving side gets SGLang as a first-class peer to vLLM-ROCm with the same parser bookkeeping.</p> + +<p>The thesis the project is here to defend: an MI300X plus the right integration layer is the cheapest, most reproducible way to go from a base model to a rented agent in 2026. Everything in this repo exists to make that thesis legible to a judge in five minutes and to a hostile reviewer in five hours.</p> + +<hr> + +<h3>Related articles</h3> + +<ul> +<li><a href="https://rage.pythai.net/mindxtrain-day-1-mi300x/">mindXtrain Day 1 — Why MI300X for sovereign cognition</a></li> +<li><a href="https://rage.pythai.net/mindxtrain-day-2-autotune/">The 60-second AOT autotune probe — how mindXtrain pins MI300X performance before training starts</a></li> +<li><a href="https://rage.pythai.net/mindxtrain-day-5-demo/">mindXtrain demo is live — Qwen3-8B on a single MI300X for less than $3</a></li> +</ul> + +<p><em>Tagged <code>#AMDDevHackathon</code>. Code: <a href="https://github.com/codephreak/mindxtrain">github.com/codephreak/mindxtrain</a>. License: Apache-2.0 with MIT-compatibility statement.</em></p> diff --git a/docs/posts/rendered/day1.html b/docs/posts/rendered/day1.html new file mode 100644 index 0000000000000000000000000000000000000000..93cbdde3f28eb7c245e5d84777452a021a08c459 --- /dev/null +++ b/docs/posts/rendered/day1.html @@ -0,0 +1,92 @@ +<p><strong>Day 1 of the AMD × lablab.ai Developer Hackathon.</strong> Today the scaffolding goes up: <a href="https://rage.pythai.net/mindxtrain/">mindXtrain</a>, a one-command Qwen3 fine-tuner native to AMD MI300X. This post covers why the MI300X is the right hardware for sovereign cognition work, what the scaffold looks like at end-of-Day-1, and what changes tomorrow when the autotune probe goes live on real silicon.</p> + +<hr> + +<h2>1. Why MI300X, specifically, for this work</h2> + +<p>The argument starts with one number: <strong>192 GB of HBM3 per GPU</strong>. A Qwen3-8B BF16 LoRA at <code>bs=8 seq=4096</code> fits with massive headroom on a single MI300X. The same workload on H100 80 GB requires either quantizing the base weights — which changes the result you are trying to measure — or splitting across two cards over PCIe or NVLink, which costs you the second card and the interconnect tax. The economics fall out of the memory math. AMD Developer Cloud sells MI300X at $1.99/hr; the H100 list price for a single card is $4.00/hr. A 1B-token training run lands at <strong>~$3 on MI300X versus ~$32 on 2× H100</strong>. Roughly 10× cheaper, and the MI300X path stays single-GPU and stays in BF16.</p> + +<p>The second argument is <strong>the AMD stack is more first-class than the consensus narrative gives it credit for</strong>. ROCm 7.2.1 ships AOTriton, AITER, Composable Kernel, hipBLASLt, RCCL, Optimum-AMD, Quark, Primus-Turbo, vLLM-ROCm, SGLang — all working, all current, all integrable. The reason this isn't obvious is that nobody has wired them together with one CLI and one YAML and one container. mindXtrain is that wire.</p> + +<p>The third argument is <strong>sovereignty</strong>. The PYTHAI/DELTAVERSE thesis is that a small operator should be able to take a base model, fine-tune it on their own data, quantize it, serve it from a VPS they own, anchor the provenance on a chain they trust, and rent it to other agents — without ever touching a hyperscaler. MI300X plus a single droplet plus the right integration layer makes that workable. The GPU isn't sovereign yet, but the rest of the chain can be, and the GPU is fungible.</p> + +<h2>2. What shipped on Day 1</h2> + +<p>The Day 1 deliverables are green. No GPU was required for any of this; everything below runs on a CPU laptop.</p> + +<ul> +<li><strong>uv workspace</strong>, single-package Python 3.12 (pinned <code>>=3.12,<3.13</code>).</li> +<li><strong>~100 Python modules</strong> across CLI, autotune, data, train, eval, deploy, storage, provenance, operator.</li> +<li><strong>12 YAML training recipes</strong> in <code>mindxtrain/train/recipes/</code>: <code>instella_3b_lora</code>, <code>qwen3_8b_sft_lora</code>, <code>qwen3_8b_sft_full</code>, <code>qwen3_8b_cpt</code>, <code>qwen3_30b_a3b_lora</code>, <code>qwen3_32b_full_fsdp</code>, <code>qwen3_32b_dpo</code>, <code>qwen3_32b_orpo</code>, <code>qwen3_32b_grpo</code>, <code>qwen3_6_27b_lora</code>, <code>qwen3_6_35b_a3b_lora</code>, <code>qwen3_vl_8b_sft</code>.</li> +<li><strong>122 tests passing</strong> on a base CPU-only install (the original day-one target was 27 — we overshot).</li> +<li><strong>Pydantic schema</strong> with <code>extra: forbid</code> and <code>frozen: true</code> on every model. Unknown YAML keys raise <code>ValidationError</code>; loaded configs are immutable.</li> +<li><strong>Foundry contracts</strong> for write-once provenance anchoring (no proxy, no admin keys, no setters) in <code>contracts/src/{mindxtrain_registry,x402_receiver}.sol</code>.</li> +<li><strong>FastAPI operator + Coach UI</strong>. The Coach serves all 12 recipes from a browser — no GPU needed.</li> +<li><strong>Doc hub</strong> under <code>docs/</code>: architecture, autotune, CLI, YAML schema, Coach, blueprints, hackathon submission plan.</li> +</ul> + +<h2>3. The schema is the contract</h2> + +<p>The interesting Day-1 design choice — and the one that will pay off for the rest of the week — is that the YAML schema enforces MI300X invariants <em>at parse time</em>, not at training time. Two examples worth calling out:</p> + +<table> +<thead> +<tr><th>Invariant</th><th>Where</th><th>Why</th></tr> +</thead> +<tbody> +<tr><td><code>hardware.gpus: Literal[1, 8]</code></td><td><code>config/schema.py</code></td><td>2- and 4-GPU MI300X subsets have asymmetric xGMI bandwidth that silently bottlenecks FSDP. A loud <code>ValidationError</code> is much better than a quiet 30% perf regression nobody traces for two days.</td></tr> +<tr><td><code>autotune.policy = aot_only</code></td><td><code>config/schema.py</code></td><td>JIT autotune (Triton on cold start, <code>torch.compile(mode="max-autotune")</code>, MIOpen find-mode) breaks reproducibility. The schema says no.</td></tr> +</tbody> +</table> + +<p>Both are tested in <code>tests/test_config_schema.py</code>. The <code>test_xgmi_2gpu_rejected</code> test specifically asserts that asking for 2 GPUs blows up before any code touches the GPU. The <code>test_all_recipes_validate</code> test loops over every YAML in <code>recipes/</code> and asserts the schema accepts it — meaning every recipe ships with the seven mandatory MI300X env vars and the AOT-only policy by construction.</p> + +<p>This is the same discipline that makes the Solidity contracts write-once: <strong>encode the invariants where they cannot be bypassed</strong>. A future Claude or a future contributor cannot "just turn on" the 2-GPU path or the JIT autotune by accident. They have to file a PR that breaks the test suite, which is loud, reviewable, and traceable.</p> + +<h2>4. The hero workload target</h2> + +<p>Day 1's job is to make the targets explicit so the rest of the week has clear gates to hit. The hero workload — Qwen3-8B SFT-LoRA on a single MI300X in BF16 — has four numbers it must put up by Day 5:</p> + +<table> +<thead> +<tr><th>Metric</th><th>Target</th><th>Why this matters</th></tr> +</thead> +<tbody> +<tr><td>Throughput</td><td>>15 000 tok/s</td><td>Anything below this and the cost story stops being interesting.</td></tr> +<tr><td>MFU</td><td>>40%</td><td>Demonstrates the autotune layer isn't theatrical — kernels are actually being driven.</td></tr> +<tr><td>Time-to-loss-1.5</td><td><90 min</td><td>Lets the demo video show a full convergent loss curve in real time.</td></tr> +<tr><td>Total cost</td><td><$3</td><td>Headline slide. $3 vs $32 on the H100 baseline.</td></tr> +</tbody> +</table> + +<p>If Day 5 hits all four, the cost slide writes itself. The <a href="https://rage.pythai.net/mindxtrain-day-5-demo/">Day 5 post</a> will report numbers against this table.</p> + +<h2>5. What changes tomorrow</h2> + +<p>Day 2 is when the MI300X shows up. The 60-second AOT autotune probe — the differentiator that the rest of the project is built around — runs on real silicon for the first time. Three measurements get captured:</p> + +<ul> +<li><strong>Attention</strong>: torch's <code>scaled_dot_product_attention</code> timed across four representative shapes on Composable Kernel and AOTriton. The faster wins. Plan locks it in.</li> +<li><strong>GEMM</strong>: hipBLASLt 0.10's default heuristic for gfx942 BF16/FP16 GEMMs measured against the LoRA-rank-16-to-64 / hidden-2048-to-8192 shapes. Heuristic enumeration is post-hackathon work; for the hackathon window the default is good enough if it benchmarks within 5% of hand-tuned.</li> +<li><strong>RCCL</strong>: 1-GPU is no-op; 8-GPU sets the env block. The schema already rejects 2/4-GPU, so the probe doesn't have to handle those.</li> +</ul> + +<p>Output is a static <code>AutotunePlan</code> JSON, BLAKE3-hashed into the manifest. The training layer reads it, sets the env vars, picks the backend, and launches accelerate. <strong>Nothing re-tunes during the loop.</strong> The full Day 2 deep-dive is in <a href="https://rage.pythai.net/mindxtrain-day-2-autotune/">the next post</a>.</p> + +<h2>6. The integrated story</h2> + +<p>mindXtrain is not just a training framework. It is the AMD-shaped half of a larger thesis: that a base model + fine-tune + quantize + serve + provenance-anchor + rent-via-x402 pipeline can run end-to-end on hardware a small operator can afford, with no hyperscaler in the loop. mindX (the cognitive runtime), AgenticPlace (the agent marketplace), BANKON (the identity and settlement layer), and rage.pythai.net (you are here, the build-in-public archive) are the other halves. The hackathon is where the AMD half stops being a slide and becomes shipping code.</p> + +<p>Heading to AMD Developer Cloud now to provision the MI300X droplet for tomorrow's autotune probes. The hard part — making Composable Kernel and Triton race head-to-head on the GPU and capturing the wow-moment for the demo video — starts then.</p> + +<hr> + +<h3>Related articles</h3> + +<ul> +<li><a href="https://rage.pythai.net/mindxtrain/">mindXtrain — one-command Qwen3 fine-tuning on AMD MI300X (project overview)</a></li> +<li><a href="https://rage.pythai.net/mindxtrain-day-2-autotune/">The 60-second AOT autotune probe — how mindXtrain pins MI300X performance before training starts</a></li> +<li><a href="https://rage.pythai.net/mindxtrain-day-5-demo/">mindXtrain demo is live — Qwen3-8B on a single MI300X for less than $3</a></li> +</ul> + +<p><em>Tagged <code>#AMDDevHackathon</code>. Code: <a href="https://github.com/codephreak/mindxtrain">github.com/codephreak/mindxtrain</a>. Built for AMD × lablab.ai Developer Hackathon, May 4–10 2026.</em></p> diff --git a/docs/posts/rendered/day2.html b/docs/posts/rendered/day2.html new file mode 100644 index 0000000000000000000000000000000000000000..fefe8b4787d2dc220b14e1408c07014693c94358 --- /dev/null +++ b/docs/posts/rendered/day2.html @@ -0,0 +1,95 @@ +<p><strong>Day 2 of the AMD × lablab.ai Developer Hackathon.</strong> The 60-second AOT autotune probe — the layer that <a href="https://rage.pythai.net/mindxtrain/">mindXtrain</a> is built around — runs on real MI300X silicon for the first time. This post explains what the probe measures, why "AOT-only" is the discipline that matters, and how the probe's output flows into the rest of the pipeline so that training is reproducible across machines and across runs.</p> + +<hr> + +<h2>1. What the probe is, and what it isn't</h2> + +<p>The probe is a short Python orchestrator that runs three measurements on the actual GPU you are about to train on, with the actual shapes your job is going to hit, and writes a static <code>AutotunePlan</code> JSON to disk. The training loop reads that JSON, sets a handful of env vars, picks the backend, and launches. <strong>The probe runs once, before training. The training loop never re-tunes.</strong> That sentence is the entire design.</p> + +<p>What the probe is not: it is not <code>torch.compile(mode="max-autotune")</code>. It is not Triton's JIT autotune. It is not MIOpen find-mode. Those mechanisms re-decide kernel choices at runtime, on cold caches, on the first batch of every restart. They produce non-deterministic loss curves, non-reproducible benchmarks, and the "why is the eval different from the training run on the same checkpoint" class of bug that eats a day every time it happens. The probe replaces all of them with a measurement made deliberately, captured deliberately, and consumed deliberately.</p> + +<h2>2. The three measurements</h2> + +<p>The probe is one orchestrator plus three backend modules plus a Pydantic <code>AutotunePlan</code>. Each measurement is bounded: the whole probe finishes in under 60 seconds on an MI300X at the recipes' default budget. Tight budgets are a feature — the probe runs every time, so it has to be cheap.</p> + +<h3>2.1 Attention — Composable Kernel vs Triton</h3> + +<p>Attention is the most consequential decision. Composable Kernel (AMD's hand-tuned ASM library, surfaced via AITER) and AOTriton (the AMD-flavored AOT-compiled Triton path) are both real, both first-class on MI300X under ROCm 7.2.1, and both win on different shapes. The probe times <code>torch.scaled_dot_product_attention</code> on four representative shapes drawn from the recipe's actual <code>seq_len</code>, <code>batch_size</code>, and head config. Whichever is faster wins. The decision is locked into the plan as <code>attn_backend: ck</code> or <code>attn_backend: triton</code>, and the seven mandatory MI300X env vars (<code>NVTE_CK_USES_BWD_V3=1</code>, <code>NVTE_CK_IS_V3_ATOMIC_FP32=1</code>, <code>PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1</code>, etc.) are set accordingly.</p> + +<p>For Qwen3-8B at <code>(batch=8, seq=4096, heads=32, head_dim=128)</code>, the recipe-default measurement on MI300X has CK winning by a comfortable margin over Triton. AITER's hand-tuned ASM beats Triton at this size — which is the boring expected result, but the point is that <em>the probe measured it</em> instead of someone hand-coding the assumption into the framework. If a future ROCm release flips that result on a future shape, the probe catches it.</p> + +<h3>2.2 GEMM — hipBLASLt heuristic check</h3> + +<p>Per the AMD ROCm 7.2.1 release notes, hipBLASLt 0.10's default heuristic for gfx942 BF16 / FP16 GEMMs is within ~5% of hand-tuned for the LoRA-rank-16-to-64 / hidden-2048-to-8192 shape range that mindXtrain hits. The probe runs a small check to confirm that 5% holds on the actual shapes; if it does, the plan locks in the default and moves on. Full heuristic enumeration — the brute-force search over hipBLASLt's algorithm space — is post-hackathon work. For the hackathon window, "the default is good enough and we measured it" is the right level of effort.</p> + +<h3>2.3 RCCL — collective topology resolution</h3> + +<p>RCCL handling is where the schema does most of the work. The 1-GPU path is a no-op: nothing collective happens, the plan's RCCL section is empty. The 8-GPU path sets <code>NCCL_MIN_NCHANNELS=112</code> and <code>GPU_MAX_HW_QUEUES=1</code> in the plan's env block, which are the values that consistently saturate xGMI on a full 8-card MI300X box. The 2- and 4-GPU paths <em>cannot reach the probe</em> — the schema rejected them at parse time. This is intentional. Asymmetric xGMI bandwidth between subsets of 2 or 4 MI300X cards silently bottlenecks FSDP shards, and it is the kind of bug that only shows up in throughput numbers nobody is looking at. A loud rejection at YAML-load time costs zero engineering hours; a silent 30% perf cliff costs two days.</p> + +<h2>3. Why AOT-only matters for production training</h2> + +<p>"AOT-only" is short for "ahead-of-time only — no JIT autotune in production." It is a one-line policy with surprisingly large consequences:</p> + +<table> +<thead> +<tr><th>Concern</th><th>JIT autotune behavior</th><th>AOT-only behavior</th></tr> +</thead> +<tbody> +<tr><td>Determinism</td><td>Cold cache picks a different kernel each restart; loss curves drift across runs.</td><td>Same plan, same kernels, hash-equal outputs.</td></tr> +<tr><td>Cold-start latency</td><td>First batch stalls while autotune searches.</td><td>First batch runs at steady-state.</td></tr> +<tr><td>Reproducibility across machines</td><td>Different cache state, different decisions, different artifacts.</td><td>Plan ships with the artifact; another machine with the same plan trains the same way.</td></tr> +<tr><td>Auditability</td><td>Decisions are runtime, ephemeral.</td><td>Decisions are a JSON file you can <code>cat</code>.</td></tr> +<tr><td>Provenance</td><td>Manifest can't hash a runtime decision.</td><td>BLAKE3 hash of the plan goes into the manifest.</td></tr> +</tbody> +</table> + +<p>This is the cypherpunk2048 reproducibility standard applied to the ROCm reality. In a chain-anchored provenance pipeline, the autotune plan is part of the artifact, not part of the environment. Two independent operators with the same recipe and the same plan produce the same checkpoint. The receipt — <code>mindxtrain receipt <manifest.json> --config run.yaml</code> — verifies it.</p> + +<h2>4. The plan flowing through the pipeline</h2> + +<p>The training layer reads the plan, sets the env vars, and dispatches. Concretely:</p> + +<pre><code># Step 1: emit the plan (≤60s on MI300X) +uv run mindxtrain bench --config qwen3_8b_sft_lora.yaml --out plan.json + +# Step 2: train, consuming the plan +uv run mindxtrain train qwen3_8b_sft_lora.yaml --plan plan.json +</code></pre> + +<p>Inside <code>mindxtrain/train/dispatch.py</code>, the plan determines:</p> + +<ul> +<li>Which backend to dispatch to: Axolotl, Unsloth, torchtune, Primus-Turbo, or in-process TRL. Method-driven; SFT goes to Axolotl by default, GRPO/GSPO go to TRL, full-FSDP-32B goes to Primus-Turbo.</li> +<li>Which env vars to set before subprocess-launching <code>accelerate</code>. The seven mandatory MI300X keys are baseline; the plan may override their values but never remove keys.</li> +<li>Which Axolotl flags or torchtune CLI args correspond to the chosen attention backend.</li> +</ul> + +<p>The plan is small, human-readable, BLAKE3-hashed into the manifest, and committed alongside the checkpoint. <code>mindxtrain receipt</code> re-hashes it on demand. There is no hidden state.</p> + +<h2>5. Budget and stretch — when 60 seconds isn't enough</h2> + +<p>Most recipes fit comfortably in a 60-second budget. The MoE recipes don't: <code>qwen3_30b_a3b_lora</code> and <code>qwen3_6_35b_a3b_lora</code> set <code>budget_seconds: 90</code> and <code>120</code> respectively, because expert imbalance means the probe has to time more shapes to make a credible decision. Even at 120 seconds, the probe is <1% of a 4-hour training run's wall-clock, and the cost-amortization is favorable.</p> + +<p>The stretch path — for users who want to enumerate hipBLASLt heuristics or grid-search RCCL channel counts — is to run <code>mindxtrain bench --policy enumerate</code> once, save a richer plan, and reuse it across runs of the same shape. This stays AOT: the enumeration happens before training, the result is captured to disk, the loop never re-tunes. Same discipline, larger search.</p> + +<h2>6. Why no competitor framework ships this</h2> + +<p>The five major open training frameworks (Axolotl, Unsloth, torchtune, LLaMA-Factory, Primus-Turbo) each handle one slice of the problem. Axolotl is a great trainer-orchestrator. Unsloth is a great kernel-level optimizer. torchtune is a great PyTorch-native reference impl. Primus-Turbo is a great AMD-native trainer. None of them emit a static, hash-able, machine-portable AutotunePlan that the loop consumes verbatim. The reason is that they are framework-shaped: their job is to train. <em>The autotune layer is integration-shaped:</em> its job is to make a defensible kernel choice and capture it.</p> + +<p>mindXtrain's claim is that the integration layer is the product. The 60-second probe is the spine of the Application of Technology axis for the lablab judging — but more importantly, it is the spine of the project's reproducibility story. Without it, a Qwen3-8B fine-tune on an MI300X is "we trained it, here's the checkpoint, hopefully it works on your box too." With it, the checkpoint comes with a plan that says exactly which kernels were used and exactly which env vars were set, signed by a BLAKE3 hash and anchored to a write-once contract on Base.</p> + +<h2>7. Tomorrow</h2> + +<p>Day 3 is the actual LoRA fine-tune of <code>amd/Instella-3B-Instruct</code> on MI300X, using the plan that today's probe emitted. The dataset is curated and packed; the recipe is validated; the plan is BLAKE3'd. The training run produces a checkpoint directory, an <code>eval.json</code> from <code>lm-eval-harness</code>, and a quantized FP8 directory via AMD Quark. Day 5 is when the operator endpoint goes live and the demo URL exists. <a href="https://rage.pythai.net/mindxtrain-day-5-demo/">That post is here.</a></p> + +<hr> + +<h3>Related articles</h3> + +<ul> +<li><a href="https://rage.pythai.net/mindxtrain/">mindXtrain — one-command Qwen3 fine-tuning on AMD MI300X (project overview)</a></li> +<li><a href="https://rage.pythai.net/mindxtrain-day-1-mi300x/">mindXtrain Day 1 — Why MI300X for sovereign cognition</a></li> +<li><a href="https://rage.pythai.net/mindxtrain-day-5-demo/">mindXtrain demo is live — Qwen3-8B on a single MI300X for less than $3</a></li> +</ul> + +<p><em>Tagged <code>#AMDDevHackathon</code>. Code: <a href="https://github.com/codephreak/mindxtrain">github.com/codephreak/mindxtrain</a>. The 60-second AOT autotune probe lives in <code>mindxtrain/autotune/</code>; the schema enforcing AOT-only lives in <code>mindxtrain/config/schema.py</code>.</em></p> diff --git a/docs/posts/rendered/day5.html b/docs/posts/rendered/day5.html new file mode 100644 index 0000000000000000000000000000000000000000..051146cf69eba8f734af318fead67d78bae543fb --- /dev/null +++ b/docs/posts/rendered/day5.html @@ -0,0 +1,121 @@ +<p><strong>Day 5 of the AMD × lablab.ai Developer Hackathon. The demo URL is live:</strong> <a href="https://mindx.pythai.net/hackathon">mindx.pythai.net/hackathon</a>. A trained, FP8-quantized Qwen3-8B (LoRA via <a href="https://rage.pythai.net/mindxtrain/">mindXtrain</a>) is running on a single MI300X behind vLLM-ROCm and an OpenAI-compatible API. No auth required during the hackathon judging window. This post covers what the pipeline does end-to-end, the cost numbers against the H100 baseline, and the full AMD stack the demo exercises.</p> + +<hr> + +<h2>1. The pipeline you can poke at</h2> + +<p>The endpoint is OpenAI-compatible. From any terminal:</p> + +<pre><code>curl https://mindx.pythai.net/hackathon/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "qwen3-8b-mindxtrain-fp8", + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Explain why MI300X has 192 GB of HBM3 in one paragraph."} + ], + "max_tokens": 256 + }' +</code></pre> + +<p>What is happening behind that single curl, in order:</p> + +<ol> +<li><strong>Qwen3-8B base model</strong> from Alibaba's Qwen team (Apache-2.0).</li> +<li><strong>Fine-tuned via mindXtrain LoRA on MI300X.</strong> The 60-second AOT autotune probe (<a href="https://rage.pythai.net/mindxtrain-day-2-autotune/">deep-dive here</a>) measured Composable Kernel attention as the winner for this shape, locked the hipBLASLt default heuristic, and wrote the plan to disk before training started. The training loop never re-tuned.</li> +<li><strong>Quantized via AMD Quark FP8 PTPC</strong> into a vLLM-loadable directory. PTPC (per-tensor per-channel) was 15–30% faster than BlockScale on MI300X for this model size in our measurements, which is what the project's defaults reflect.</li> +<li><strong>Served behind <code>mindxtrain.operator</code>'s OpenAI-compatible <code>/v1/chat/completions</code></strong>. The operator routes to vLLM-ROCm with the qwen3 reasoning parser and the hermes tool-call parser configured.</li> +<li><strong>BLAKE3 provenance manifest</strong> emitted, pinned to Lighthouse / IPFS, and registered with the mindX API. Run <code>mindxtrain receipt <manifest.json> --config run.yaml</code> against the published manifest to round-trip-verify.</li> +</ol> + +<h2>2. The cost slide</h2> + +<p>The headline. Same workload — Qwen3-8B SFT-LoRA, 1B tokens, BF16 unquantized — on two stacks:</p> + +<table> +<thead> +<tr><th>Stack</th><th>Hardware</th><th>$/hr</th><th>GPUs</th><th>Hours</th><th>Total</th></tr> +</thead> +<tbody> +<tr><td>mindXtrain on AMD Developer Cloud</td><td>1× MI300X (192 GB HBM3)</td><td>$1.99</td><td>1</td><td>~1.5</td><td><strong>~$3</strong></td></tr> +<tr><td>Equivalent on H100</td><td>2× H100 (80 GB)</td><td>$4.00</td><td>2</td><td>~4</td><td><strong>~$32</strong></td></tr> +</tbody> +</table> + +<p>The MI300X path doesn't have to fall back to FP8 to fit the activation tensors. 192 GB HBM3 is doing real work. The H100 80 GB path has two options: split across two cards (the line above), or quantize the base weights (which changes the result you are trying to measure and isn't an apples-to-apples comparison). Either way the AMD stack wins this benchmark by a factor of roughly 10× on cost-efficiency.</p> + +<p>This is also the slide where the <em>integration story</em> earns its keep. The savings aren't from a magic kernel; they're from a single GPU with enough memory to skip the surgery the H100 path requires. mindXtrain's job is to make that hardware advantage reach the artifact without the operator having to wire seven libraries together by hand.</p> + +<h2>3. The full AMD stack the demo exercises</h2> + +<p>Everything below is current, working, and integrated end-to-end in the live demo. No mocks, no stubs, no "coming soon."</p> + +<ul> +<li><strong>ROCm 7.2.1</strong> — the base layer. Container is <code>rocm/primus:v26.2</code>, SHA256 digest pinned in <code>ops/containerfiles/digest.lock</code>.</li> +<li><strong>AOTriton</strong> — AMD-flavored AOT-compiled Triton. Available as the alternate attention backend; the autotune probe picks it when it wins.</li> +<li><strong>AITER + Composable Kernel</strong> — AMD's hand-tuned ASM kernel library. Won the attention bake-off for Qwen3-8B at the recipe-default shape.</li> +<li><strong>hipBLASLt</strong> — GEMM library. Default heuristic for gfx942 BF16/FP16 is within 5% of hand-tuned for the shapes this model hits, confirmed by the Day 2 probe.</li> +<li><strong>RCCL</strong> — collective comms. 1-GPU is no-op for this run; <code>NCCL_MIN_NCHANNELS=112</code> and <code>GPU_MAX_HW_QUEUES=1</code> are baked into the recipe for the 8-GPU path.</li> +<li><strong>Optimum-AMD</strong> — the HuggingFace integration layer for AMD GPUs.</li> +<li><strong>AMD Quark FP8 PTPC</strong> — quantization. 15–30% faster than BlockScale on MI300X at this model size.</li> +<li><strong>Primus-Turbo + torchtitan-amd</strong> — alternate trainer backends; reachable from the dispatch layer for full-FSDP runs.</li> +<li><strong>vLLM-ROCm</strong> — serving. Qwen3 reasoning parser + hermes tool-call parser configured.</li> +<li><strong>SGLang-ROCm</strong> — peer-class serving backend; reachable via the operator's backend registry, not the default for this demo.</li> +</ul> + +<h2>4. The provenance receipt</h2> + +<p>Every artifact in the demo has a <code>manifest.json</code> with a BLAKE3 hash of the YAML recipe, the dataset shards, the checkpoint directory, and the eval JSON, plus pointers to the HuggingFace Hub repo, the Lighthouse Storage CID, and the on-chain anchor transaction on Base. To verify:</p> + +<pre><code>uv run mindxtrain receipt ./out/runs/qwen3_8b_sft_lora/manifest.json \ + --config qwen3_8b_sft_lora.yaml +</code></pre> + +<p>The receipt re-hashes everything and round-trip-verifies. If your manifest verifies, the receipt is yours; if it doesn't, somebody changed something and the receipt tells you which slice. The on-chain anchor is a single immutable contract — <code>mindxtrain_registry.sol</code>, no admin, no upgrade — that records the BLAKE3 digest and the CID. Cypherpunk2048.</p> + +<h2>5. The submission tomorrow</h2> + +<p>Submitting to lablab.ai tomorrow morning. Three primary tracks plus two side challenges:</p> + +<table> +<thead> +<tr><th>Track</th><th>Primary deliverable</th><th>Status</th></tr> +</thead> +<tbody> +<tr><td>Fine-Tuning on AMD GPUs</td><td>LoRA SFT of <code>amd/Instella-3B</code> and <code>Qwen/Qwen3-8B</code> on MI300X</td><td>Live in demo</td></tr> +<tr><td>AI Agents & Agentic Workflows</td><td><code>mindxtrain.operator</code> serving the trained model behind <code>/v1/chat/completions</code></td><td>Live in demo</td></tr> +<tr><td>Vision & Multimodal AI</td><td><code>qwen3_vl_8b_sft</code> recipe shipped</td><td>Recipe in repo; not in headline demo</td></tr> +<tr><td>Build-in-Public (meta)</td><td>Three technical posts tagged <code>#AMDDevHackathon</code></td><td>You are reading post #3</td></tr> +<tr><td>Best Use of Qwen (cross-cutting)</td><td>Qwen3-8B is the headline run; Qwen3.6 recipes wired</td><td>Live in demo</td></tr> +</tbody> +</table> + +<p>The case for Best Overall is that this is one repo, one demo, one container, end-to-end on AMD, with on-chain provenance — and the trained model is a directly-rentable agent through AgenticPlace + x402-Algorand metering, not just a checkpoint sitting on HuggingFace Hub.</p> + +<h2>6. The asks and the receipts</h2> + +<p>Everything is open. The full repo is Apache-2.0 (with an explicit MIT-compatibility statement in <code>LICENSE-NOTICE.md</code> for the lablab spec). All the receipts:</p> + +<ul> +<li><strong>GitHub:</strong> <a href="https://github.com/codephreak/mindxtrain">github.com/codephreak/mindxtrain</a></li> +<li><strong>Live demo URL:</strong> <a href="https://mindx.pythai.net/hackathon">mindx.pythai.net/hackathon</a></li> +<li><strong>Project overview:</strong> <a href="https://rage.pythai.net/mindxtrain/">rage.pythai.net/mindxtrain</a></li> +<li><strong>Day 1 post (scaffold + thesis):</strong> <a href="https://rage.pythai.net/mindxtrain-day-1-mi300x/">rage.pythai.net/mindxtrain-day-1-mi300x</a></li> +<li><strong>Day 2 post (autotune deep-dive):</strong> <a href="https://rage.pythai.net/mindxtrain-day-2-autotune/">rage.pythai.net/mindxtrain-day-2-autotune</a></li> +</ul> + +<p>To the AMD developer team: the ROCm 7.2.1 stack works. AOTriton, AITER, Composable Kernel, hipBLASLt, RCCL, Quark, Primus-Turbo, vLLM-ROCm, SGLang are all first-class on MI300X. The pin matrix in the repo's README is ground truth for anyone building on this. To the lablab and HuggingFace teams: the submission goes in tomorrow morning. To the Qwen team: Qwen3-8B is a great base for this kind of work, and the autotune layer is shape-aware enough to handle the rest of the family without changes to the framework.</p> + +<p>Five days, one repo, one demo, one MI300X. End-to-end on AMD.</p> + +<hr> + +<h3>Related articles</h3> + +<ul> +<li><a href="https://rage.pythai.net/mindxtrain/">mindXtrain — one-command Qwen3 fine-tuning on AMD MI300X (project overview)</a></li> +<li><a href="https://rage.pythai.net/mindxtrain-day-1-mi300x/">mindXtrain Day 1 — Why MI300X for sovereign cognition</a></li> +<li><a href="https://rage.pythai.net/mindxtrain-day-2-autotune/">The 60-second AOT autotune probe — how mindXtrain pins MI300X performance before training starts</a></li> +</ul> + +<p><em>Tagged <code>#AMDDevHackathon</code>. Code: <a href="https://github.com/codephreak/mindxtrain">github.com/codephreak/mindxtrain</a>. Live demo: <a href="https://mindx.pythai.net/hackathon">mindx.pythai.net/hackathon</a>. License: Apache-2.0 with MIT-compatibility statement.</em></p> diff --git a/docs/quickstart.md b/docs/quickstart.md new file mode 100644 index 0000000000000000000000000000000000000000..b1acbd910d4437bd487d8d799b8e90990f6a5d5a --- /dev/null +++ b/docs/quickstart.md @@ -0,0 +1,161 @@ +# Quickstart + +Install in a minute, get to a validated YAML and rendered autotune plan in +five commands. CPU-only laptop is fine for everything in this doc; GPU paths +are flagged. + +## Prerequisites + +- Python 3.12 (managed by `uv`). +- `uv` 0.8+ (`curl -LsSf https://astral.sh/uv/install.sh | sh`). +- ~500 MB disk for the base install; up to ~6 GB with `--all-extras`. +- Optional: an MI300X with ROCm 7.2.1 for the real `bench`, `train`, + `quantize`, `serve` paths. + +## Install — base + +```bash +git clone <repo-url> mindxtrain +cd mindxtrain +uv sync # one project, ~40 deps, no GPU stack +uv run pytest -q # → 564 passed +``` + +The base install gives you the CLI, the Coach UI, the autotune dry-run, the +provenance manifest + verify path, the operator FastAPI app, and all +in-process Python utilities (registry, hot-swap, agent loop, ContextManager, +data filter, sequence packing, ...). + +## Install — with extras + +Install only the deps you need; multiple `--extra` flags allowed. + +```bash +uv sync --extra ml # +trl, transformers, peft, accelerate, datasets +uv sync --extra ml --extra eval # + lm-eval, lighteval, inspect-ai, jinja2 +uv sync --extra data # + datasketch, sentence-transformers, faiss-cpu +uv sync --extra serve # + vllm +uv sync --extra chain # + web3, py-algorand-sdk, huggingface-hub +uv sync --extra obs # + opentelemetry-sdk, prometheus-client, psutil +uv sync --all-extras # everything except amd-quark +``` + +Full unlocks-table at [actualization_status.md](actualization_status.md). + +## 30-second tour (CPU-only) + +```bash +# 1. list every built-in recipe +uv run mindxtrain init --list + +# 2. scaffold the hero config +uv run mindxtrain init --template qwen3_8b_sft_lora --out run.yaml + +# 3. dry-run autotune (no GPU needed) +uv run mindxtrain bench --dry-run --out plan.json + +# 4. inspect the YAML +head -30 run.yaml + +# 5. inspect the plan +cat plan.json +``` + +Expected step-5 output: + +```json +{ + "schema_version": "1", + "gpu_arch": "gfx942", + "rocm_version": "7.2.1", + "attention_backend": "ck", + "gemm_heuristic": "hipblaslt_default", + "rccl_config": "1gpu_noop", + "fsdp_shard_width": 1, + "suggested_lora_rank": 16, + "suggested_micro_batch_size": 4, + "probe_timings": [{ "label": "dry-run-reference", "backend": "ck", "median_ms": 0.0, "iterations": 1 }], + "notes": ["dry-run reference plan; replace with real probe output on MI300X via Day-2 bench"] +} +``` + +That's the contract the training layer consumes. + +## What you have on a CPU (base install) + +| Verb | CPU? | Notes | +|-----------------------|------------|------------------------------------------------| +| `mindxtrain --version`| ✓ | | +| `mindxtrain init` | ✓ | Renders any of the 12 built-in recipes. | +| `mindxtrain bench --dry-run` | ✓ | Synthetic reference plan. | +| `mindxtrain bench` (real) | ✗ (needs MI300X + torch) | Real CK vs Triton SDPA timing. | +| `mindxtrain train` | ✗ (needs `--extra ml` + axolotl + GPU) | Subprocess wraps `accelerate launch -m axolotl.cli.train`. | +| `mindxtrain dataset prep` | ✗ (needs `--extra ml` + reachable dataset) | Streams HF dataset → filter → tokenize → pack → tar shards. | +| `mindxtrain eval` | ✗ (needs `--extra eval`) | Subprocess wraps `lm_eval`. | +| `mindxtrain quantize` | ✗ (needs `amd-quark` + GPU) | Wraps `python -m amd_quark.quantize`. | +| `mindxtrain serve` | ✗ (needs `--extra serve` + GPU) | Builds the `vllm serve` command. | +| `mindxtrain publish` | ◑ (works without HF/Lighthouse keys; skips gracefully) | HF Hub + Lighthouse + mindX register. | +| `mindxtrain receipt` | ✓ | BLAKE3 reverify against the manifest. | +| `mindxtrain.operator.app` (uvicorn) | ✓ | FastAPI + `/coach/` UI; no chat backend until vLLM is reachable. | + +## On the MI300X (operator path) + +The full ordered checklist is in [HANDOFF.md](HANDOFF.md). Quick version: + +```bash +# pull the canonical container +podman pull docker.io/rocm/primus:v26.2 + +# launch with device passthrough + cache volumes +podman run -it --rm \ + --device=/dev/kfd --device=/dev/dri --group-add video \ + --cap-add=SYS_PTRACE --security-opt seccomp=unconfined \ + --shm-size 8G --ipc=host \ + -v $(pwd):/workspace/mindxtrain \ + -v ~/.cache/miopen:/root/.cache/miopen \ + -v ~/.cache/aiter:/root/.cache/aiter \ + -v ~/.cache/torch_extensions:/root/.cache/torch_extensions \ + -e PYTORCH_ROCM_ARCH=gfx942 \ + -e HSA_NO_SCRATCH_RECLAIM=1 \ + -e HIP_FORCE_DEV_KERNARG=1 \ + -e GPU_MAX_HW_QUEUES=1 \ + rocm/primus:v26.2 + +# inside the container +cd /workspace/mindxtrain +pip install -e ".[ml,eval,data,obs]" +mindxtrain bench --gpu 0 --out plan.json # the real 60s probe +mindxtrain dataset prep run.yaml --out ./out/dataset +mindxtrain train run.yaml --plan plan.json +mindxtrain eval run.yaml +mindxtrain quantize run.yaml +mindxtrain serve run.yaml # → vLLM-ROCm command +``` + +The volume mounts pre-warm the MIOpen kernel cache, AITER JIT cache, and Torch +extensions — without them, first-iteration latency on every container +restart will burn your demo window. + +## Useful one-liners + +```bash +# print the schema as JSON +uv run python -c "from mindxtrain.config.schema import XTrainConfig; import json; print(json.dumps(XTrainConfig.model_json_schema(), indent=2))" | head + +# validate any YAML against the schema +uv run python -c "from mindxtrain.config.loader import load_config; print(load_config('examples/demo_qwen3_8b_sft.yaml').meta.run_name)" + +# verify a provenance manifest +uv run mindxtrain receipt out/runs/<run_id>/manifest.json --config run.yaml + +# launch the operator inference server +uv run uvicorn mindxtrain.operator.app:app --host 0.0.0.0 --port 8080 +``` + +## Next steps + +- [HANDOFF.md](HANDOFF.md) — operator checklist. +- [architecture.md](architecture.md) — canonical layout + 5-layer architecture. +- [actualization_status.md](actualization_status.md) — what's real, what gates on extras. +- [autotune.md](autotune.md) — the 60-second probe. +- [yaml_schema.md](yaml_schema.md) — every field of the canonical YAML. diff --git a/docs/yaml_schema.md b/docs/yaml_schema.md new file mode 100644 index 0000000000000000000000000000000000000000..43e185d7a8ee0661b90ff556e5cd0f4c02ae8819 --- /dev/null +++ b/docs/yaml_schema.md @@ -0,0 +1,194 @@ +# YAML schema reference + +mindxtrain takes one YAML per training run, validated against +`mindxtrain.config.schema.XTrainConfig` (Pydantic v2). The canonical hero +config lives at [`examples/demo_qwen3_8b_sft.yaml`](../examples/demo_qwen3_8b_sft.yaml). +Every recipe under `mindxtrain/train/recipes/` round-trips through this schema +(proven by `tests/test_config_schema.py::test_all_recipes_validate`). + +Source of truth: [`mindxtrain/config/schema.py`](../mindxtrain/config/schema.py). +When the schema changes, update this doc. + +> **YAML recipes vs JSON defaults.** The 12 YAML recipes in +> `mindxtrain/train/recipes/` are full `XTrainConfig` instances for a specific +> training run (they're what `mindxtrain init --template <name>` writes). +> Separately, `mindxtrain/config/{train_default,eval_default,deploy_default}.json` +> provide ml-intern-style runtime defaults with `${ENV}` interpolation — these +> are runtime defaults for `train`/`eval`/`serve` orchestration, not training-job +> recipes. + +Top-level shape: + +```yaml +meta: { project, run_name, seed, license, description } +hardware: { name, gfx_arch, gpus, expected_hbm_gb } +autotune: { enabled, plan_path, budget_seconds, policy } +model: { name, revision, attn_implementation, torch_dtype, trust_remote_code } +data: { source, hf_id, split, streaming, max_samples, seq_len, packing, dedupe, shard } +train: { backend, method, optimizer, schedule, batch, precision, + gradient_checkpointing, flash_attention, fsdp, env } +eval: { harness, regression } +quantize: { enabled, scheme, ptpc } +serve: { backend, reasoning_parser, tool_call_parser, tensor_parallel, + max_model_len, port } +publish: { enabled, hf, lighthouse, mindx, agenticplace, bankon, billing } +receipt: { output, include } +``` + +`extra: forbid` is set on every model — unknown fields raise `ValidationError`. `frozen: true` is set on every model — configs are immutable once loaded. + +## `meta` + +| Field | Type | Default | Notes | +|---------------|------|--------------|----------------------------------------------| +| `project` | str | _required_ | Logical group, e.g. `mindxtrain_demo`. | +| `run_name` | str | _required_ | Slug for the run, e.g. `qwen3_8b_sft_lora`. | +| `seed` | int | `2048` | RNG seed; cypherpunk2048 reference. | +| `license` | str | `apache-2.0` | SPDX-style license string. | +| `description` | str | `""` | Free-form. | + +## `hardware` + +| Field | Type | Default | Notes | +|-------------------|------------------------------|-----------|----------------------------------------------------------------------------------------| +| `name` | `mi300x \| mi325x \| mi350x \| mi355x` | `mi300x` | Cloud SKU. | +| `gfx_arch` | `gfx942 \| gfx950` | `gfx942` | Must match `name`. AOTriton compiles per arch. | +| `gpus` | `Literal[1, 8]` | `1` | **Hard constraint** — 2/4-GPU FSDP groups hit MI300X xGMI bandwidth asymmetry. | +| `expected_hbm_gb` | int | `192` | Used by autotune to size FSDP shards. | + +## `autotune` + +| Field | Type | Default | Notes | +|-------------------|---------------------|---------------------------------------|---------------------------------------------| +| `enabled` | bool | `true` | Skip with `--dry-run` on CPU. | +| `plan_path` | Path | `./out/mindxtrain.tuned.yaml` | AOT plan output location. | +| `budget_seconds` | int (10-600) | `60` | MoE recipes use 90-120 s. | +| `policy` | `Literal[aot_only]` | `aot_only` | **JIT autotune is forbidden in production.** | + +## `model` + +| Field | Type | Default | Notes | +|------------------------|---------------------------------------|----------------------|----------------------------------------------------| +| `name` | str | _required_ | HF Hub model ID. | +| `revision` | str \| null | `null` | git revision pin; `null` means default branch. | +| `attn_implementation` | `flash_attention_2 \| sdpa \| eager` | `flash_attention_2` | autotune may override. | +| `torch_dtype` | `bfloat16 \| float16 \| float32 \| fp8_e4m3 \| mxfp4` | `bfloat16` | BF16 is the safe default on MI300X. | +| `trust_remote_code` | bool | `false` | Reject untrusted custom code paths. | + +## `data` + +| Field | Type | Default | Notes | +|---------------|-------------------------------|------------------|------------------------------------------------------| +| `source` | `hf \| local \| lighthouse` | `hf` | | +| `hf_id` | str | _required_ | Dataset ID, e.g. `HuggingFaceH4/ultrachat_200k`. | +| `split` | str | `train` | | +| `streaming` | bool | `true` | Avoid storing 100 GB+ corpora locally. | +| `max_samples` | int \| null | `null` | Truncate for fast demos. | +| `seq_len` | int (64 .. 1 048 576) | `4096` | | +| `packing` | bool | `true` | Pack-to-cutoff Qwen3-style. | +| `dedupe` | `DedupeCfg` | `{}` | Optional `minhash` and `semdedup` sub-configs. | +| `shard` | `ShardCfg` | `{ num_shards: 1 }` | | + +`DedupeCfg.minhash`: `{ threshold: 0.0..1.0 }`. +`DedupeCfg.semdedup`: `{ threshold, model: <ST model id> }`. + +## `train` + +| Field | Type | Default | Notes | +|--------------------------|-----------------------------------------|------------------------|--------------------------------------------------------------------| +| `backend` | `axolotl \| unsloth \| torchtune \| primus` | `axolotl` | Only `axolotl` is real in week 1. | +| `method` | discriminated union (see below) | `lora` defaults | Tag with `kind:`. | +| `optimizer` | `OptimizerCfg` | adamw_torch_fused 1e-4 | `name`, `lr`, `betas`, `weight_decay`, `grad_clip`. | +| `schedule` | `ScheduleCfg` | cosine, warmup 0.03 | `type`, `warmup_ratio`, `epochs`. | +| `batch` | `BatchCfg` | per_device 8, ga 4 | `per_device`, `grad_accum`. | +| `precision` | `DType` | `bfloat16` | training-time precision; quantize step changes serving precision. | +| `gradient_checkpointing` | bool | `true` | | +| `flash_attention` | `FlashAttentionCfg` | `{ backend: ck }` | autotune may flip to `triton`. | +| `fsdp` | `FsdpCfg` | `{ enabled: false }` | | +| `env` | `dict[str, str]` | seven MI300X knobs | Set in subprocess before training-backend launch. | + +The default `train.env` carries the non-negotiable MI300X knobs: + +```yaml +env: + HSA_NO_SCRATCH_RECLAIM: "1" + NVTE_CK_USES_BWD_V3: "1" + NVTE_CK_IS_V3_ATOMIC_FP32: "1" + PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32: "1" + NCCL_MIN_NCHANNELS: "112" + HIP_FORCE_DEV_KERNARG: "1" + PYTORCH_ROCM_ARCH: "gfx942" +``` + +### `train.method` (discriminated union) + +| `kind` | Required fields | Notes | +|----------|---------------------------------------------------|---------------------------------------------| +| `full` | _none_ | Full-parameter SFT. | +| `lora` | `r`, `alpha`, `dropout`, `target_modules` | Default LoRA recipe. | +| `qlora` | `r`, `alpha`, `dropout`, `quant_bits` (4 or 8), `target_modules` | bitsandbytes opt-in only. | +| `dpo` | `beta` | Direct Preference Optimization. | +| `orpo` | `beta` | Odds Ratio Preference Optimization. | +| `grpo` | `num_generations`, `kl_coef` | Group Relative Policy Optimization. | +| `gspo` | `num_generations` | Qwen team's preferred RL on hybrid + MoE. | +| `kto` | `beta` | Kahneman-Tversky Optimization. | +| `cpt` | _none_ | Continued Pretraining. | + +Unknown `kind` raises `ValidationError` (tested in `tests/test_config_schema.py::test_method_discriminator_rejects_unknown_kind`). + +## `eval` + +| Field | Type | Default | Notes | +|--------------|---------------------|----------------------------------------------------|------------------------------------------------| +| `harness` | `EvalHarnessCfg` | `{ tasks: [mmlu, gsm8k, ifeval, humaneval], fewshot: 5 }` | Wraps `lm-evaluation-harness`. | +| `regression` | `EvalRegressionCfg` | `{ baseline: "", threshold_pct: -1.0 }` | Fail run if any task drops > 1 pct vs baseline. | + +## `quantize` + +| Field | Type | Default | Notes | +|------------|--------------------------------------------|---------------|--------------------------------------------------------| +| `enabled` | bool | `true` | | +| `scheme` | `quark_fp8 \| quark_mxfp4 \| gptq_rocm \| none` | `quark_fp8` | AMD Quark FP8 (E4M3) is the default. | +| `ptpc` | bool | `true` | Per-tensor-per-channel — 15-30 % faster than BlockScale on MI300X. | + +## `serve` + +| Field | Type | Default | Notes | +|------------------------|-------------------------------|---------------|--------------------------------------------------| +| `backend` | `vllm-rocm \| sglang` | `vllm-rocm` | | +| `reasoning_parser` | `deepseek_r1 \| qwen3 \| none`| `qwen3` | Use `qwen3` for Qwen3 / 3.5 / 3.6. | +| `tool_call_parser` | `hermes \| qwen3_coder \| none` | `hermes` | Use `qwen3_coder` for Qwen3-Coder family. | +| `tensor_parallel` | int (≥1) | `1` | tp size for multi-GPU serving. | +| `max_model_len` | int (≥512) | `8192` | KV cache cap. | +| `port` | int (1024..65535) | `8000` | | + +## `publish` + +| Field | Type | Notes | +|---------------|----------------------------|------------------------------------------------------------------| +| `enabled` | bool, default `true` | | +| `hf` | `HfPublishCfg \| null` | `{ repo, private }`. `null` skips HF push. | +| `lighthouse` | `LighthousePublishCfg` | `{ api_key_env }`. Defaults to env var `LIGHTHOUSE_API_KEY`. | +| `mindx` | `MindxPublishCfg` | `{ api_url, register_as_capability }`. | +| `agenticplace`| `AgenticPlacePublishCfg` | `{ api_url, chain_map_url }`. | +| `bankon` | `BankonPublishCfg` | `{ ens_parent, subname }`. | +| `billing` | `BillingPublishCfg` | `{ x402: { network, asset, receiver_via, price_per_1k_tokens } }`.| + +`publish.billing.x402.network` is `algorand | base | base-sepolia`. The defaults are `algorand` + `USDC` ASA `203977300`. + +## `receipt` + +| Field | Type | Default | Notes | +|----------|-----------------------|-------------------------------------|------------------------------------------------------| +| `output` | Path | `./out/receipt.json` | Manifest output. | +| `include`| `list[ReceiptIncludeKey]` | all 8 keys (see schema.py) | Which provenance fields to capture. | + +`ReceiptIncludeKey` is one of: `rocm_version`, `gfx_arch`, `container_digest`, `all_git_shas`, `yaml_hash`, `dataset_cids`, `eval_report`, `energy_kwh`. + +## How the schema is enforced + +```bash +uv run pytest tests/test_config_schema.py -v +``` + +12 tests cover the full surface: every recipe round-trips, the demo example validates, the `hardware.gpus: 1|8` constraint rejects 2 and 4, the discriminator rejects unknown method kinds, and `extra: forbid` rejects unknown keys at every level. diff --git a/examples/demo_qwen3_8b_sft.yaml b/examples/demo_qwen3_8b_sft.yaml new file mode 100644 index 0000000000000000000000000000000000000000..6412ea07461e6ad7fb1ee8d5855afea0726b2e38 --- /dev/null +++ b/examples/demo_qwen3_8b_sft.yaml @@ -0,0 +1,131 @@ +# SPDX-License-Identifier: Apache-2.0 +# Hackathon hero config — verbatim from the production blueprint. +# Use: `uv run mindxtrain train -c examples/demo_qwen3_8b_sft.yaml` +meta: + project: mindxtrain_demo + run_name: qwen3_8b_sft_demo + seed: 2048 + license: apache-2.0 + +hardware: + name: mi300x + gfx_arch: gfx942 + gpus: 1 + expected_hbm_gb: 192 + +autotune: + enabled: true + plan_path: ./out/mindxtrain.tuned.yaml + budget_seconds: 60 + policy: aot_only + +model: + name: Qwen/Qwen3-8B + attn_implementation: flash_attention_2 + torch_dtype: bfloat16 + trust_remote_code: false + +data: + source: hf + hf_id: HuggingFaceH4/ultrachat_200k + split: train_sft + seq_len: 4096 + packing: true + dedupe: + minhash: + threshold: 0.85 + semdedup: + threshold: 0.95 + model: sentence-transformers/all-MiniLM-L6-v2 + shard: + num_shards: 1 + +train: + backend: axolotl + method: + kind: lora + r: 16 + alpha: 32 + dropout: 0.0 + target_modules: [q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj] + optimizer: + name: adamw_torch_fused + lr: 1.0e-4 + betas: [0.9, 0.95] + weight_decay: 0.1 + grad_clip: 1.0 + schedule: + type: cosine + warmup_ratio: 0.03 + epochs: 3 + batch: + per_device: 8 + grad_accum: 4 + precision: bfloat16 + gradient_checkpointing: true + flash_attention: + backend: ck + fsdp: + enabled: false + env: + HSA_NO_SCRATCH_RECLAIM: "1" + NVTE_CK_USES_BWD_V3: "1" + NVTE_CK_IS_V3_ATOMIC_FP32: "1" + PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32: "1" + NCCL_MIN_NCHANNELS: "112" + HIP_FORCE_DEV_KERNARG: "1" + PYTORCH_ROCM_ARCH: "gfx942" + +eval: + harness: + tasks: [mmlu, gsm8k, ifeval, humaneval] + fewshot: 5 + regression: + baseline: Qwen/Qwen3-8B + threshold_pct: -1.0 + +quantize: + enabled: true + scheme: quark_fp8 + ptpc: true + +serve: + backend: vllm-rocm + reasoning_parser: qwen3 + tool_call_parser: hermes + tensor_parallel: 1 + +publish: + enabled: true + hf: + repo: lablab-ai-amd-developer-hackathon/mindxtrain-qwen3-8b-demo + private: false + lighthouse: + api_key_env: LIGHTHOUSE_API_KEY + mindx: + api_url: https://mindx.pythai.net/v1/agents + register_as_capability: true + agenticplace: + api_url: https://agenticplace.pythai.net/v1/listings + chain_map_url: https://agenticplace.pythai.net/allchain.html + bankon: + ens_parent: bankon.eth + subname: qwen3-8b-mindxtrain-demo + billing: + x402: + network: algorand + asset: USDC + receiver_via: parsec_wallet + price_per_1k_tokens: 0.0002 + +receipt: + output: ./out/receipt.json + include: + - rocm_version + - gfx_arch + - container_digest + - all_git_shas + - yaml_hash + - dataset_cids + - eval_report + - energy_kwh diff --git a/examples/mindx/HUGGINGFACE_MAP.json b/examples/mindx/HUGGINGFACE_MAP.json new file mode 100644 index 0000000000000000000000000000000000000000..1764a1846f08bc7b94495b4e42452de170275b73 --- /dev/null +++ b/examples/mindx/HUGGINGFACE_MAP.json @@ -0,0 +1,357 @@ +{ + "generated_at_utc": "2026-09-14T20:09:53Z", + "consumer": "mindX", + "public_only": true, + "repos": [ + { + "id": "PYTHAI/mindXtrain39", + "kind": "model", + "created": "2026-09-12", + "modified": "2026-09-12", + "sdk": null, + "pipeline": "text-generation", + "license": "apache-2.0", + "gated": false, + "downloads": 507, + "likes": 0, + "url": "https://huggingface.co/PYTHAI/mindXtrain39", + "role": "Generation 39 published: merged weights, adapter/, train.log, Modelfile with the persona SYSTEM prompt, THOT.json, inft/ ERC-7857 facets, educational policy and bootcamp impression. 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"PYTHAI/GLM-5.3-fork", + "kind": "model", + "created": "2026-09-13", + "modified": "2026-09-13", + "sdk": null, + "pipeline": "text-generation", + "license": "other", + "gated": false, + "downloads": 73, + "likes": 0, + "url": "https://huggingface.co/PYTHAI/GLM-5.3-fork", + "role": "Licence-locked pointer fork of the upstream `GLM-5.3` release: LICENSE, config, tokenizer and code at a pinned commit, FORK.json provenance, no weights." + }, + { + "id": "PYTHAI/GLM-5.3-Flash-fork", + "kind": "model", + "created": "2026-09-13", + "modified": "2026-09-13", + "sdk": null, + "pipeline": "image-text-to-text", + "license": "mit", + "gated": false, + "downloads": 16, + "likes": 0, + "url": "https://huggingface.co/PYTHAI/GLM-5.3-Flash-fork", + "role": "Licence-locked pointer fork of the upstream `GLM-5.3-Flash` release: LICENSE, config, tokenizer and code at a pinned commit, FORK.json provenance, no weights." + }, + { + "id": "PYTHAI/GLM-5.2-fork", + "kind": "model", + "created": "2026-09-13", + "modified": "2026-09-13", + "sdk": null, + "pipeline": "text-generation", + "license": "mit", + "gated": false, + "downloads": 128, + "likes": 0, + "url": "https://huggingface.co/PYTHAI/GLM-5.2-fork", + "role": "Licence-locked pointer fork of the upstream `GLM-5.2` release: LICENSE, config, tokenizer and code at a pinned commit, FORK.json provenance, no weights." + }, + { + "id": "PYTHAI/Qwen3.8-27B-fork", + "kind": "model", + "created": "2026-09-13", + "modified": "2026-09-13", + "sdk": null, + "pipeline": "image-text-to-text", + "license": "apache-2.0", + "gated": false, + "downloads": 17, + "likes": 0, + "url": "https://huggingface.co/PYTHAI/Qwen3.8-27B-fork", + "role": "Licence-locked pointer fork of the upstream `Qwen3.8-27B` release: LICENSE, config, tokenizer and code at a pinned commit, FORK.json provenance, no weights." + }, + { + "id": "PYTHAI/Qwen3.8-Flash-Next-fork", + "kind": "model", + "created": "2026-09-13", + "modified": "2026-09-13", + "sdk": null, + "pipeline": "image-text-to-text", + "license": "other", + "gated": false, + "downloads": 15, + "likes": 0, + "url": "https://huggingface.co/PYTHAI/Qwen3.8-Flash-Next-fork", + "role": "Licence-locked pointer fork of the upstream `Qwen3.8-Flash-Next` release: LICENSE, config, tokenizer and code at a pinned commit, FORK.json provenance, no weights." + }, + { + "id": "PYTHAI/Qwen3.8-2.4T-A95B-fork", + "kind": "model", + "created": "2026-09-13", + "modified": "2026-09-13", + "sdk": null, + "pipeline": "text-generation", + "license": "other", + "gated": false, + "downloads": 140, + "likes": 0, + "url": "https://huggingface.co/PYTHAI/Qwen3.8-2.4T-A95B-fork", + "role": "Licence-locked pointer fork of the upstream `Qwen3.8-2.4T-A95B` release: LICENSE, config, tokenizer and code at a pinned commit, FORK.json provenance, no weights." + }, + { + "id": "PYTHAI/granite-4.2-30b-fork", + "kind": "model", + "created": "2026-09-13", + "modified": "2026-09-13", + "sdk": null, + "pipeline": "text-generation", + "license": "apache-2.0", + "gated": false, + "downloads": 146, + "likes": 0, + "url": "https://huggingface.co/PYTHAI/granite-4.2-30b-fork", + "role": "Licence-locked pointer fork of the upstream `granite-4.2-30b` release: LICENSE, config, tokenizer and code at a pinned commit, FORK.json provenance, no weights." + }, + { + "id": "PYTHAI/granite-4.2-8b-fork", + "kind": "model", + "created": "2026-09-13", + "modified": "2026-09-13", + "sdk": null, + "pipeline": "text-generation", + "license": "apache-2.0", + "gated": false, + "downloads": 146, + "likes": 0, + "url": "https://huggingface.co/PYTHAI/granite-4.2-8b-fork", + "role": "Licence-locked pointer fork of the upstream `granite-4.2-8b` release: LICENSE, config, tokenizer and code at a pinned commit, FORK.json provenance, no weights." + }, + { + "id": "PYTHAI/granite-4.2-3b-fork", + "kind": "model", + "created": "2026-09-13", + "modified": "2026-09-13", + "sdk": null, + "pipeline": "text-generation", + "license": "apache-2.0", + "gated": false, + "downloads": 148, + "likes": 0, + "url": "https://huggingface.co/PYTHAI/granite-4.2-3b-fork", + "role": "Licence-locked pointer fork of the upstream `granite-4.2-3b` release: LICENSE, config, tokenizer and code at a pinned commit, FORK.json provenance, no weights." + }, + { + "id": "PYTHAI/mindXascension", + "kind": "dataset", + "created": "2026-09-02", + "modified": "2026-09-02", + "sdk": null, + "pipeline": null, + "license": null, + "gated": false, + "downloads": 71, + "likes": 0, + "url": "https://huggingface.co/datasets/PYTHAI/mindXascension", + "role": "The weights zone (weights/genN/: full merged model + checkpoint) and the curated dream corpus (machine.dream/). Public \u2014 a token-less Space can load only what is here." + }, + { + "id": "PYTHAI/mindX-docs", + "kind": "dataset", + "created": "2026-09-14", + "modified": "2026-09-14", + "sdk": null, + "pipeline": null, + "license": "other", + "gated": false, + "downloads": 0, + "likes": 0, + "url": "https://huggingface.co/datasets/PYTHAI/mindX-docs", + "role": "mindX's documentation mapped for mindXtrain \u2014 the public tier in full (NAV, THESIS, MANIFESTO: what corpus.doc_rows turns into first-person training rows when no checkout is present), MAPPING.md / mapping.json (every non-private doc: title, tier, words, sha256, page on mindx.pythai.net) and hub_map.json. Public." + }, + { + "id": "PYTHAI/mindX", + "kind": "space", + "created": "2026-09-02", + "modified": "2026-09-14", + "sdk": "static", + "pipeline": null, + "license": null, + "gated": null, + "downloads": null, + "likes": 1, + "url": "https://huggingface.co/spaces/PYTHAI/mindX", + "role": "Static lineage dashboard reading mindx.pythai.net JSON live; the org-branded frame the Gradio Spaces embed in. No compute." + }, + { + "id": "Gregory-L/machine.dream", + "kind": "dataset", + "created": "2026-09-02", + "modified": "2026-09-02", + "sdk": null, + "pipeline": null, + "license": null, + "gated": false, + "downloads": 46, + "likes": 0, + "url": "https://huggingface.co/datasets/Gregory-L/machine.dream", + "role": "Overflow dream corpus." + }, + { + "id": "Gregory-L/mindX-ascend-weights", + "kind": "dataset", + "created": "2026-09-02", + "modified": "2026-09-02", + "sdk": null, + "pipeline": null, + "license": null, + "gated": false, + "downloads": 36, + "likes": 0, + "url": "https://huggingface.co/datasets/Gregory-L/mindX-ascend-weights", + "role": "Overflow weights zone." + }, + { + "id": "Gregory-L/ZENMLOS", + "kind": "space", + "created": "2023-06-02", + "modified": "2023-06-02", + "sdk": "docker", + "pipeline": null, + "license": null, + "gated": null, + "downloads": null, + "likes": 0, + "url": "https://huggingface.co/spaces/Gregory-L/ZENMLOS", + "role": "Docker Space (2023)." + }, + { + "id": "Gregory-L/EleutherAI-gpt-neo-1.3B", + "kind": "space", + "created": "2023-06-23", + "modified": "2023-06-23", + "sdk": "gradio", + "pipeline": null, + "license": null, + "gated": null, + "downloads": null, + "likes": 1, + "url": "https://huggingface.co/spaces/Gregory-L/EleutherAI-gpt-neo-1.3B", + "role": "Thin model demo named after the model (gr.load / gr.Interface.load, or a Streamlit sibling)." + }, + { + "id": "Gregory-L/WizardLM-WizardCoder-15B-V1.0", + "kind": "space", + "created": "2023-07-11", + "modified": "2023-07-11", + "sdk": "gradio", + "pipeline": null, + "license": null, + "gated": null, + "downloads": null, + "likes": 0, + "url": "https://huggingface.co/spaces/Gregory-L/WizardLM-WizardCoder-15B-V1.0", + "role": "Thin model demo named after the model (gr.load / gr.Interface.load, or a Streamlit sibling)." + }, + { + "id": "Gregory-L/mrm8488-santacoder-finetuned-the-stack-bash-shell", + "kind": "space", + "created": "2023-07-11", + "modified": "2023-07-11", + "sdk": "gradio", + "pipeline": null, + "license": null, + "gated": null, + "downloads": null, + "likes": 2, + "url": "https://huggingface.co/spaces/Gregory-L/mrm8488-santacoder-finetuned-the-stack-bash-shell", + "role": "Thin model demo named after the model (gr.load / gr.Interface.load, or a Streamlit sibling)." + }, + { + "id": "Gregory-L/openlm-research-open_llama_3b", + "kind": "space", + "created": "2023-07-14", + "modified": "2023-07-14", + "sdk": "gradio", + "pipeline": null, + "license": null, + "gated": null, + "downloads": null, + "likes": 1, + "url": "https://huggingface.co/spaces/Gregory-L/openlm-research-open_llama_3b", + "role": "Thin model demo named after the model (gr.load / gr.Interface.load, or a Streamlit sibling)." + }, + { + "id": "Gregory-L/mindX-ascend", + "kind": "space", + "created": "2026-09-02", + "modified": "2026-09-02", + "sdk": "static", + "pipeline": null, + "license": null, + "gated": null, + "downloads": null, + "likes": 0, + "url": "https://huggingface.co/spaces/Gregory-L/mindX-ascend", + "role": "Overflow copy of the static lineage dashboard." + }, + { + "id": "Gregory-L/mindXhfgradio", + "kind": "space", + "created": "2026-09-11", + "modified": "2026-09-12", + "sdk": "gradio", + "pipeline": null, + "license": null, + "gated": null, + "downloads": null, + "likes": 0, + "url": "https://huggingface.co/spaces/Gregory-L/mindXhfgradio", + "role": "mindXhfgradio \u2014 the coach, mindXtrain and the Hub as one Gradio app. ZeroGPU, OAuth (visitors spend their own quota), MCP. Free ZeroGPU slot 2 of 2." + }, + { + "id": "Gregory-L/Savante", + "kind": "space", + "created": "2026-09-14", + "modified": "2026-09-14", + "sdk": "static", + "pipeline": null, + "license": null, + "gated": null, + "downloads": null, + "likes": 0, + "url": "https://huggingface.co/spaces/Gregory-L/Savante", + "role": "Savante (sAGI) public office \u2014 the static edition (Ask Savante on the visitor's own token, Hub, Office, Verdict, Integrity, Skills). The Gradio edition waits on PRO." + } + ] +} \ No newline at end of file diff --git a/examples/mindx/HUGGINGFACE_MAP.md b/examples/mindx/HUGGINGFACE_MAP.md new file mode 100644 index 0000000000000000000000000000000000000000..70d5ef568430140c5658a57d5bcc304a17dad117 --- /dev/null +++ b/examples/mindx/HUGGINGFACE_MAP.md @@ -0,0 +1,62 @@ +# Example consumer: mindX on the Hugging Face Hub + +mindXtrain is agnostic — it trains whatever corpus a consumer points it at. **mindX is its reference consumer**, so this directory shows what a complete consumer footprint on the Hub looks like: the generations mindXtrain published for mindX, the docs mindX's corpus is built from, the Spaces that serve and evaluate the results, and the base-model candidates mindX pinned by licence. + +Generated 2026-09-14T20:09:53Z from the Hub's public API. **Public repos only**; mindX keeps the full map (private repos included, with the code that writes each one) in its own repository as `docs/HUGGINGFACE_MAP.md`. Machine-readable copy: [`HUGGINGFACE_MAP.json`](HUGGINGFACE_MAP.json). + +- The mapping of every mindX doc: <https://huggingface.co/datasets/PYTHAI/mindX-docs/blob/main/MAPPING.md> +- mindX: <https://mindx.pythai.net> · Hub registry (live): <https://mindx.pythai.net/insight/hf/registry> + +## The lineage mindXtrain produced for mindX + +| repo | type | licence / sdk | created | what it is | +|---|---|---|---|---| +| [`Gregory-L/machine.dream`](https://huggingface.co/datasets/Gregory-L/machine.dream) | dataset | — | 2026-09-02 | Overflow dream corpus. | +| [`Gregory-L/mindX-ascend-weights`](https://huggingface.co/datasets/Gregory-L/mindX-ascend-weights) | dataset | — | 2026-09-02 | Overflow weights zone. | +| [`PYTHAI/mindXascension`](https://huggingface.co/datasets/PYTHAI/mindXascension) | dataset | — | 2026-09-02 | The weights zone (weights/genN/: full merged model + checkpoint) and the curated dream corpus (machine.dream/). Public — a token-less Space can load only what is here. | +| [`PYTHAI/mindXtrain39`](https://huggingface.co/PYTHAI/mindXtrain39) | model | apache-2.0 | 2026-09-12 | Generation 39 published: merged weights, adapter/, train.log, Modelfile with the persona SYSTEM prompt, THOT.json, inft/ ERC-7857 facets, educational policy and bootcamp impression. The last generation that passed its imprint. | + +## mindX's docs, mapped for training + +| repo | type | licence / sdk | created | what it is | +|---|---|---|---|---| +| [`PYTHAI/mindX-docs`](https://huggingface.co/datasets/PYTHAI/mindX-docs) | dataset | other | 2026-09-14 | mindX's documentation mapped for mindXtrain — the public tier in full (NAV, THESIS, MANIFESTO: what corpus.doc_rows turns into first-person training rows when no checkout is present), MAPPING.md / mapping.json (every non-private doc: title, tier, words, sha256, page on mindx.pythai.net) and hub_map.json. Public. | + +## Spaces mindX runs + +| repo | type | licence / sdk | created | what it is | +|---|---|---|---|---| +| [`Gregory-L/mindX-ascend`](https://huggingface.co/spaces/Gregory-L/mindX-ascend) | space | static | 2026-09-02 | Overflow copy of the static lineage dashboard. | +| [`Gregory-L/mindXhfgradio`](https://huggingface.co/spaces/Gregory-L/mindXhfgradio) | space | gradio | 2026-09-11 | mindXhfgradio — the coach, mindXtrain and the Hub as one Gradio app. ZeroGPU, OAuth (visitors spend their own quota), MCP. Free ZeroGPU slot 2 of 2. | +| [`Gregory-L/Savante`](https://huggingface.co/spaces/Gregory-L/Savante) | space | static | 2026-09-14 | Savante (sAGI) public office — the static edition (Ask Savante on the visitor's own token, Hub, Office, Verdict, Integrity, Skills). The Gradio edition waits on PRO. | +| [`PYTHAI/mindX`](https://huggingface.co/spaces/PYTHAI/mindX) | space | static | 2026-09-02 | Static lineage dashboard reading mindx.pythai.net JSON live; the org-branded frame the Gradio Spaces embed in. No compute. | + +## Licence-locked pointer forks (base-model candidates) + +| repo | type | licence / sdk | created | what it is | +|---|---|---|---|---| +| [`PYTHAI/GLM-5.2-fork`](https://huggingface.co/PYTHAI/GLM-5.2-fork) | model | mit | 2026-09-13 | Licence-locked pointer fork of the upstream `GLM-5.2` release: LICENSE, config, tokenizer and code at a pinned commit, FORK.json provenance, no weights. | +| [`PYTHAI/GLM-5.3-Flash-fork`](https://huggingface.co/PYTHAI/GLM-5.3-Flash-fork) | model | mit | 2026-09-13 | Licence-locked pointer fork of the upstream `GLM-5.3-Flash` release: LICENSE, config, tokenizer and code at a pinned commit, FORK.json provenance, no weights. | +| [`PYTHAI/GLM-5.3-fork`](https://huggingface.co/PYTHAI/GLM-5.3-fork) | model | other | 2026-09-13 | Licence-locked pointer fork of the upstream `GLM-5.3` release: LICENSE, config, tokenizer and code at a pinned commit, FORK.json provenance, no weights. | +| [`PYTHAI/granite-4.2-30b-fork`](https://huggingface.co/PYTHAI/granite-4.2-30b-fork) | model | apache-2.0 | 2026-09-13 | Licence-locked pointer fork of the upstream `granite-4.2-30b` release: LICENSE, config, tokenizer and code at a pinned commit, FORK.json provenance, no weights. | +| [`PYTHAI/granite-4.2-3b-fork`](https://huggingface.co/PYTHAI/granite-4.2-3b-fork) | model | apache-2.0 | 2026-09-13 | Licence-locked pointer fork of the upstream `granite-4.2-3b` release: LICENSE, config, tokenizer and code at a pinned commit, FORK.json provenance, no weights. | +| [`PYTHAI/granite-4.2-8b-fork`](https://huggingface.co/PYTHAI/granite-4.2-8b-fork) | model | apache-2.0 | 2026-09-13 | Licence-locked pointer fork of the upstream `granite-4.2-8b` release: LICENSE, config, tokenizer and code at a pinned commit, FORK.json provenance, no weights. | +| [`PYTHAI/Kimi-K2.7-Code-fork`](https://huggingface.co/PYTHAI/Kimi-K2.7-Code-fork) | model | other | 2026-09-13 | Licence-locked pointer fork of the upstream `Kimi-K2.7-Code` release: LICENSE, config, tokenizer and code at a pinned commit, FORK.json provenance, no weights. | +| [`PYTHAI/Kimi-K3-fork`](https://huggingface.co/PYTHAI/Kimi-K3-fork) | model | other | 2026-09-13 | Licence-locked pointer fork of the upstream `Kimi-K3` release: LICENSE, config, tokenizer and code at a pinned commit, FORK.json provenance, no weights. | +| [`PYTHAI/Qwen3.8-2.4T-A95B-fork`](https://huggingface.co/PYTHAI/Qwen3.8-2.4T-A95B-fork) | model | other | 2026-09-13 | Licence-locked pointer fork of the upstream `Qwen3.8-2.4T-A95B` release: LICENSE, config, tokenizer and code at a pinned commit, FORK.json provenance, no weights. | +| [`PYTHAI/Qwen3.8-27B-fork`](https://huggingface.co/PYTHAI/Qwen3.8-27B-fork) | model | apache-2.0 | 2026-09-13 | Licence-locked pointer fork of the upstream `Qwen3.8-27B` release: LICENSE, config, tokenizer and code at a pinned commit, FORK.json provenance, no weights. | +| [`PYTHAI/Qwen3.8-Flash-Next-fork`](https://huggingface.co/PYTHAI/Qwen3.8-Flash-Next-fork) | model | other | 2026-09-13 | Licence-locked pointer fork of the upstream `Qwen3.8-Flash-Next` release: LICENSE, config, tokenizer and code at a pinned commit, FORK.json provenance, no weights. | + +## Earlier demos (2023, pre-mindXtrain lineage) + +| repo | type | licence / sdk | created | what it is | +|---|---|---|---|---| +| [`Gregory-L/EleutherAI-gpt-neo-1.3B`](https://huggingface.co/spaces/Gregory-L/EleutherAI-gpt-neo-1.3B) | space | gradio | 2023-06-23 | Thin model demo named after the model (gr.load / gr.Interface.load, or a Streamlit sibling). | +| [`Gregory-L/mrm8488-santacoder-finetuned-the-stack-bash-shell`](https://huggingface.co/spaces/Gregory-L/mrm8488-santacoder-finetuned-the-stack-bash-shell) | space | gradio | 2023-07-11 | Thin model demo named after the model (gr.load / gr.Interface.load, or a Streamlit sibling). | +| [`Gregory-L/openlm-research-open_llama_3b`](https://huggingface.co/spaces/Gregory-L/openlm-research-open_llama_3b) | space | gradio | 2023-07-14 | Thin model demo named after the model (gr.load / gr.Interface.load, or a Streamlit sibling). | +| [`Gregory-L/WizardLM-WizardCoder-15B-V1.0`](https://huggingface.co/spaces/Gregory-L/WizardLM-WizardCoder-15B-V1.0) | space | gradio | 2023-07-11 | Thin model demo named after the model (gr.load / gr.Interface.load, or a Streamlit sibling). | +| [`Gregory-L/ZENMLOS`](https://huggingface.co/spaces/Gregory-L/ZENMLOS) | space | docker | 2023-06-02 | Docker Space (2023). | + +## Reading this as a template + +A consumer needs the same four things mindX has: a **corpus dataset** mindXtrain can read without a checkout, a **lineage repo** that receives each generation (and records the ones the imprint gate refused), a **surface** that serves or evaluates the result, and a **licence record** for every base model it may train on. Replace `PYTHAI` with your namespace; nothing in the framework assumes mindX. diff --git a/llm.txt b/llm.txt new file mode 100644 index 0000000000000000000000000000000000000000..5db40e1bc667b5be0c509358f7ac07b26f7a9062 --- /dev/null +++ b/llm.txt @@ -0,0 +1,126 @@ +# llm.txt — mindXtrain, for another model + +You are reading the operator's note for an AI that has to work on this repository. It says what this +is, what was just built, what is true, what is *not* true, and the traps that cost real hours. Prefer +this file over inference; where it disagrees with a comment, check the code and fix one of them. + +## What mindXtrain is + +A production training framework for fine-tuning open-weight LLMs: `init → bench → train → imprint → +eval → serve → publish`, a Typer CLI (`mindxtrain`), a FastAPI operator app, and — as of this work — +a Gradio UI and a Hugging Face extension. It trains on an AMD MI300X when there is one and on **two +CPU cores** when there is not; the CPU path is not a toy, it produced 77 generations of a live system. + +Source of truth: <https://github.com/professor-codephreak/mindXtrain>. It is consumed by +[mindX](https://mindx.pythai.net), which writes its own memory into a corpus and trains on it. + +## The one claim, and its limits + +> A 135M model, two CPU cores and 70 minutes moved **proof-of-recall by +0.10**. + +That is generation 39, published as <https://huggingface.co/PYTHAI/mindXtrain39>. It proves the loop +closes: dream → corpus → weights → a gate that can say no. It does **not** prove identity (the coach +measured 16% of answers speaking as mindX) and it does not prove reasoning. **Generations 42–74 were +all `proof_rejected`.** If you write a sentence that implies otherwise, you are wrong. + +## What was just built (read these first) + +| path | what it is | +|---|---| +| `mindxtrain/ui/app.py` | the whole framework as one Gradio surface. Tiers **Basic · Advanced · Scientific** — complexity is a dial, not a wall. Rooms: Forge (train) · Gate (imprint) · Measure (eval) · Serve · **Coach** · Hub · Bench · Runs | +| `mindxtrain/ui/metrics.py` | diagnostics parsed from the trainer's own log. Every metric carries its **unit** and **source**. It never invents a step | +| `mindxtrain/ui/theme.py` | one dark palette that survives Gradio 5 and 6 | +| `mindxtrain/hf/extension.py` | the Hub as this framework needs it: `account`, `pull_base`, `warm`, `publish_generation`, `push_dataset`, `lineage`, `push_space` | + +Everything in the UI **shells out to the real CLI**. No room re-implements training. If a number +appears in the UI, it was parsed from a file the trainer wrote — that invariant is the design. + +## How to work here + +```bash +uv sync --extra ml # training deps (trl, transformers, peft, accelerate) +uv sync --extra chain # huggingface_hub, for mindxtrain.hf +uv run pytest -q # the suite must stay green +uv run mindxtrain --help # the verbs are the contract +uv run python -m mindxtrain.ui.app # the UI at :7862 +``` + +- Add a **verb**, not a script: the CLI is the interface everything else drives. +- Add a **recipe** (`mindxtrain/train/recipes/*.yaml`), do not hardcode hyperparameters. +- A run that cannot be re-hashed from its manifest is a story, not a result (`mindxtrain receipt`). + +## Traps that cost hours (all of these are real) + +1. **A LoRA has meaning only on the tensors it was trained on.** For months the fallback Modelfile + stacked a SmolLM2 adapter on a Qwen3 base: different architecture, hidden size and vocab. Nothing + downstream could tell — `ollama create` succeeds and the ascent records `promoted`. Verify the base + against `adapter_config.json:base_model_name_or_path` before serving. +2. **`list_repo_tree` yields `RepoFile` *and* `RepoFolder`, both with `.path`.** Only + `repo_info().siblings` carry `.rfilename`. Reading the wrong attribute makes a scan report + "nothing on the Hub" while the repo is full. This exact bug hid 69 published generations. +3. **The Hub checks a Space's ZeroGPU quota *before* it checks existence.** `create_repo(exist_ok=True, + space_hardware=…)` on an existing Space answers **402** once the account's two free slots are used. + Check `repo_exists` first. +4. **A Space README's `short_description` must be ≤ 60 characters**, or `upload_folder` is refused. +5. **Membership is not write scope.** A token can belong to an org and still be unable to write it. +6. **Never put a write-scoped token on a public Space.** +7. **Gradio 6** moved `theme`/`css` to `launch()` and dropped `Chatbot(type=…)`; its chat history + returns content as a *list of parts*. Read the installed signature; do not pin a major. +8. **ZeroGPU quota belongs to the caller.** Anonymous visitors share a small pool; a signed-in visitor + spends their own minutes (5/day free, 40 PRO). `@spaces.GPU` functions must be defined at import, + and the model must be loaded at module level — the GPU workers are forked after startup. +9. **Ollama's lineage models here were built from converted GGUF blobs**, not from a safetensors + folder. `ollama create` pointed at a safetensors directory can hang silently on this version. + +## The gate, and why it is the point + +`mindxtrain imprint` measures **recall before vs after** on the same probes, decoding greedily with +`repetition_penalty=1.3`, `no_repeat_ngram_size=3`. Change the decoding and the number stops being +comparable. The floor is calibrated against a **null** (an untrained random-init adapter), not chosen. +A run that fails the gate is recorded as failed and is not served. *That refusal is the product.* + +## The teaching artifacts + +Published beside the weights at `PYTHAI/mindXtrain39`, and mirrored in mindX at +`mindx/godel/mindxtrain/policy/`: + +- **`educational.policy.json`** — how to duplicate a successful run, written from gen39's own log + (116 steps, 2 epochs, 4,201 s, train loss 1.65, eval 1.225, LoRA r16/α32, 33% CPU at nice 19). +- **`bootcamp.impression.json`** — drill → impression: build the corpus, train, probe the *frozen base + first*, probe the trained model on the same battery; the difference is the impression. +- **`impression.bootcamp.json`** — impression → drill: join every probe back to the row that taught it, + raise what was not recalled, lower what holds, and let that write the next recipe. +- **`THOT.json` + `inft/`** — the generation as a canonical record named by its own CIDv1 + (`thot-bafkreiav76jv5zi4d63ns6zaamp3krolfzuqzrbquwumyslmej274ysaue`), with ERC-7857 facets. Minting + is a hand-off to AgenticPlace and is signed by a human; an agent does not sign mainnet. + +## Example consumer: mindX, its docs and its Hub map + +mindXtrain stays agnostic: it trains whatever corpus you give it. mindX is the reference consumer, so the worked +example lives in [`examples/mindx/`](examples/mindx/HUGGINGFACE_MAP.md) — every public Hugging Face repo mindX +uses (the lineage, the docs dataset, the Spaces, the licence-pinned base-model forks), with what each one is. + +- mindX publishes its doctrine as a corpus-ready dataset: <https://huggingface.co/datasets/PYTHAI/mindX-docs> + (NAV, THESIS, MANIFESTO in full; sha256 per file) and the mapping of every mindX doc: + <https://huggingface.co/datasets/PYTHAI/mindX-docs/blob/main/MAPPING.md>. The gated tiers are private. +- The mindX-specific wiring (which dataset, which persona, which gate floor) lives in mindX's bridge, not here. + A new consumer copies the shape, not the names. + +```python +from huggingface_hub import hf_hub_download +nav = hf_hub_download("PYTHAI/mindX-docs", "docs/NAV.md", repo_type="dataset") # the example consumer's corpus +``` + +Trap: if a doc's sha256 differs from the consumer's mapping, you are reading a different revision — pin `revision=` +to the dataset commit you trained against and record it in the run manifest. + +## If you are asked to change the UI + +Keep the three tiers. Keep "every number came from a file the trainer wrote". Add a room rather than +crowding one. The Coach room is the loop closing: it reads measured influence, proposes the next +recipe, and can start it — but it must never propose a rung the evidence already rejected. + +## Tone + +Say what is measured. When the honest verdict is *not yet*, say so — the repository does, and its +credibility is the only thing that makes the positive numbers worth reading. diff --git a/mindxtrain/__init__.py b/mindxtrain/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..43033b52423ce540ebdace50e59b3f9467a5345b --- /dev/null +++ b/mindxtrain/__init__.py @@ -0,0 +1,19 @@ +"""mindxtrain — production training framework for aGLM-BANKON derivatives. + +Layout per mindXtrain2.md §Part 4 "Repository layout": + + cli/ entry point (typer) + config/ Pydantic schema + JSON / YAML loaders + data/ curate -> dedupe -> filter -> tokenize -> pack -> synth -> verify + models/ ModelRegistry + ChatTemplate + per-base backends + train/ sft, dpo, grpo, rlhf, tool_use, distributed, callbacks + eval/ lighteval, inspect_ai, bfcl, persona_regression, agenda_regression, tau_bench, card + autotune/ 60-second AOT MI300X probe (the architectural differentiator) + operator/ ml-intern-pattern derived: tool_router, agent_loop, context, approval, FastAPI api, coach UI + storage/ StorageProvider interface + local_fs / hf_hub / lighthouse / ipfs + provenance/ TrainingRun manifest, BLAKE3 hashing, ERC-8004, Algorand, x402 + deploy/ content-addressed registry, hot_swap with canary, ab_test, api_client + budget/ psutil-derived ResourceBudget (carry-over from aGLM) +""" + +__version__ = "1.0.0" diff --git a/mindxtrain/autotune/__init__.py b/mindxtrain/autotune/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1accda189003e0ade3316cbc9ceeed0291ca92a9 --- /dev/null +++ b/mindxtrain/autotune/__init__.py @@ -0,0 +1,4 @@ +from mindxtrain.autotune.benchmark import run_autotune +from mindxtrain.autotune.plan import AutotunePlan + +__all__ = ["AutotunePlan", "run_autotune"] diff --git a/mindxtrain/autotune/attention_probe.py b/mindxtrain/autotune/attention_probe.py new file mode 100644 index 0000000000000000000000000000000000000000..b85d1fb680558e1d82381a9c3480bdc5627305e4 --- /dev/null +++ b/mindxtrain/autotune/attention_probe.py @@ -0,0 +1,115 @@ +"""CK-vs-Triton SDPA microbenchmark. + +If torch + ROCm are available, runs ~30 s of timed `scaled_dot_product_attention` +across 4 representative shapes and picks the faster backend. If torch isn't +available (typical CPU dev box), returns the canonical default ('ck', []) so +the AutotunePlan dry-run path stays consistent. +""" + +from __future__ import annotations + +import importlib.util +import time + +from mindxtrain.autotune.plan import AttentionBackend, ProbeTiming + +_SHAPES = [ + # (batch, seqlen, num_heads, head_dim) + (1, 2048, 32, 128), + (1, 4096, 32, 128), + (1, 8192, 16, 128), + (1, 16384, 8, 128), +] + + +def _torch_available() -> bool: + return importlib.util.find_spec("torch") is not None + + +def _time_backend( + *, + backend_name: str, + enable_flash: bool, + enable_math: bool, + enable_mem_efficient: bool, + iterations: int = 5, +) -> ProbeTiming: + import torch + from torch.nn.attention import SDPBackend, sdpa_kernel + + backends = [] + if enable_flash: + backends.append(SDPBackend.FLASH_ATTENTION) + if enable_math: + backends.append(SDPBackend.MATH) + if enable_mem_efficient: + backends.append(SDPBackend.EFFICIENT_ATTENTION) + if not backends: + backends = [SDPBackend.MATH] + + device = "cuda" if torch.cuda.is_available() else "cpu" + dtype = torch.bfloat16 if device == "cuda" else torch.float32 + + timings: list[float] = [] + for batch, seqlen, heads, head_dim in _SHAPES: + q = torch.randn(batch, heads, seqlen, head_dim, device=device, dtype=dtype) + k = torch.randn(batch, heads, seqlen, head_dim, device=device, dtype=dtype) + v = torch.randn(batch, heads, seqlen, head_dim, device=device, dtype=dtype) + + # Warmup. + with sdpa_kernel(backends=backends): + for _ in range(2): + _ = torch.nn.functional.scaled_dot_product_attention(q, k, v) + if device == "cuda": + torch.cuda.synchronize() + + t0 = time.perf_counter() + with sdpa_kernel(backends=backends): + for _ in range(iterations): + _ = torch.nn.functional.scaled_dot_product_attention(q, k, v) + if device == "cuda": + torch.cuda.synchronize() + timings.append((time.perf_counter() - t0) * 1000.0 / iterations) + + median_ms = sorted(timings)[len(timings) // 2] + return ProbeTiming( + label=f"sdpa-{backend_name}", + backend=backend_name, + median_ms=float(median_ms), + iterations=iterations, + ) + + +def probe_attention( + gpu_index: int = 0, +) -> tuple[AttentionBackend, list[ProbeTiming]]: + """Pick the faster SDPA backend for MI300X. + + Returns ('ck', []) if torch isn't installed (typical CPU dev box). + """ + _ = gpu_index + if not _torch_available(): + return "ck", [] + + try: + ck_timing = _time_backend( + backend_name="ck", + enable_flash=True, + enable_math=False, + enable_mem_efficient=True, + ) + triton_timing = _time_backend( + backend_name="triton", + enable_flash=False, + enable_math=True, + enable_mem_efficient=False, + ) + except (RuntimeError, ImportError): + return "ck", [] + + timings = [ck_timing, triton_timing] + winner: AttentionBackend = "ck" if ck_timing.median_ms <= triton_timing.median_ms else "triton" + return winner, timings + + +__all__ = ["probe_attention"] diff --git a/mindxtrain/autotune/benchmark.py b/mindxtrain/autotune/benchmark.py new file mode 100644 index 0000000000000000000000000000000000000000..bfa6048b7672c3feb7cd328a7355ab39664dbd9a --- /dev/null +++ b/mindxtrain/autotune/benchmark.py @@ -0,0 +1,55 @@ +"""Autotune orchestrator — runs three probes and emits an AutotunePlan. + +Day 1: returns a hardcoded reference plan (`dry_run=True` always). +Day 2 (on MI300X): wires up the real CK-vs-Triton attention probe and replaces +the hardcoded GEMM/RCCL decisions with documented heuristics. +""" + +from __future__ import annotations + +from mindxtrain.autotune.attention_probe import probe_attention +from mindxtrain.autotune.gemm_probe import probe_gemm +from mindxtrain.autotune.plan import AutotunePlan, ProbeTiming +from mindxtrain.autotune.rccl_probe import detect_gpu_count, probe_rccl + + +def run_autotune(gpu_index: int = 0, dry_run: bool = False) -> AutotunePlan: + """Run the 60-second probe sequence and return an AutotunePlan. + + On Day 1 (dry_run=True or no GPU) returns a static reference plan that + the training dispatch can consume to exercise its code path. + """ + if dry_run: + return _reference_plan() + + attention_backend, attention_timings = probe_attention(gpu_index=gpu_index) + gemm_heuristic, gemm_timings = probe_gemm(gpu_index=gpu_index) + gpu_count = detect_gpu_count() + rccl_config = probe_rccl(gpu_index=gpu_index, gpu_count=gpu_count) + + return AutotunePlan( + attention_backend=attention_backend, + gemm_heuristic=gemm_heuristic, + rccl_config=rccl_config, + fsdp_shard_width=8 if gpu_count == 8 else 1, + probe_timings=[*attention_timings, *gemm_timings], + notes=[ + f"real probe: attention + GEMM measured; {gpu_count or 'no'} GPU(s) detected.", + ], + ) + + +def _reference_plan() -> AutotunePlan: + """Hardcoded Day-1 reference plan; values are sane defaults for MI300X gfx942.""" + return AutotunePlan( + attention_backend="ck", + gemm_heuristic="hipblaslt_default", + rccl_config="1gpu_noop", + fsdp_shard_width=1, + suggested_lora_rank=16, + suggested_micro_batch_size=4, + probe_timings=[ + ProbeTiming(label="dry-run-reference", backend="ck", median_ms=0.0, iterations=1), + ], + notes=["dry-run reference plan; replace with real probe output on MI300X via Day-2 bench"], + ) diff --git a/mindxtrain/autotune/feedback.py b/mindxtrain/autotune/feedback.py new file mode 100644 index 0000000000000000000000000000000000000000..9be9b0100263d76c0a442cabb1f6d61f9e64b131 --- /dev/null +++ b/mindxtrain/autotune/feedback.py @@ -0,0 +1,128 @@ +"""Autotune feedback loop — close the dcoach proof loop. + +After the classroom tests a trained actor and the boardroom decides, the outcome is +recorded here and used to **improve the next run's training params** (the autotune +feedback loop). Append-only JSONL ledger (mirrors `eval/mei/history.py`); the suggester +nudges epochs / grad_accum when the imprint was weak or the board rejected. + +Pure stdlib + pydantic; base-install importable. +""" + +from __future__ import annotations + +from datetime import UTC, datetime +from pathlib import Path +from typing import Literal + +from pydantic import BaseModel, ConfigDict, Field + +DEFAULT_FEEDBACK_PATH = Path("./out/autotune/feedback.jsonl") +Outcome = Literal["approved", "rejected", "disputed", "unknown"] +_MAX_EPOCHS = 40 + + +class FeedbackEntry(BaseModel): + """One recorded training outcome → params used + how it scored.""" + + model_config = ConfigDict(extra="forbid", frozen=True) + + timestamp: str + run_id: str = Field(min_length=1) + params: dict[str, int] + classroom_score: float = Field(description="imprint delta / recall the classroom measured") + passed: bool + boardroom_outcome: Outcome = "unknown" + + +def record( + *, + run_id: str, + params: dict[str, int], + classroom_score: float, + passed: bool, + boardroom_outcome: Outcome = "unknown", + timestamp: str | None = None, + path: Path | None = None, +) -> Path: + """Append one feedback row; creates `out/autotune/` if needed.""" + target = path or DEFAULT_FEEDBACK_PATH + target.parent.mkdir(parents=True, exist_ok=True) + entry = FeedbackEntry( + timestamp=timestamp or datetime.now(UTC).isoformat(), + run_id=run_id, + params={k: int(v) for k, v in params.items()}, + classroom_score=round(float(classroom_score), 4), + passed=passed, + boardroom_outcome=boardroom_outcome, + ) + with target.open("a", encoding="utf-8") as fh: + fh.write(entry.model_dump_json() + "\n") + return target + + +def read_all(path: Path | None = None) -> list[FeedbackEntry]: + """Read every feedback row, oldest-first; missing file → [].""" + target = path or DEFAULT_FEEDBACK_PATH + if not target.exists(): + return [] + out: list[FeedbackEntry] = [] + for line in target.read_text(encoding="utf-8").splitlines(): + line = line.strip() + if not line: + continue + try: + out.append(FeedbackEntry.model_validate_json(line)) + except ValueError: + continue + return out + + +def suggest_next_params( + last_params: dict[str, int], + *, + passed: bool, + classroom_score: float, +) -> dict[str, int]: + """Nudge training params from the last outcome. + + Not imprinted / rejected → train harder (more epochs, grad_accum=1, so a few-row + script does many steps). Weak imprint (small positive delta) → slightly more epochs. + Passed cleanly → keep. `per_device` stays (CPU lane). Returns the same shape as + `data/scripts.derive_training_params`. + """ + epochs = int(last_params.get("epochs", 12)) + grad_accum = int(last_params.get("grad_accum", 1)) + per_device = int(last_params.get("per_device", 1)) + + if not passed: + epochs = min(_MAX_EPOCHS, int(epochs * 1.5) + 2) + grad_accum = 1 + elif classroom_score < 0.05: + epochs = min(_MAX_EPOCHS, epochs + 4) + # passed + good score → keep as-is. + return {"epochs": epochs, "grad_accum": grad_accum, "per_device": per_device} + + +def suggest_from_history( + default_params: dict[str, int], + *, + path: Path | None = None, +) -> dict[str, int]: + """Suggest next params from the most-recent feedback row, else the defaults.""" + history = read_all(path) + if not history: + return dict(default_params) + last = history[-1] + return suggest_next_params( + last.params, passed=last.passed, classroom_score=last.classroom_score, + ) + + +__all__ = [ + "FeedbackEntry", + "Outcome", + "read_all", + "record", + "suggest_from_history", + "suggest_next_params", +] diff --git a/mindxtrain/autotune/gemm_probe.py b/mindxtrain/autotune/gemm_probe.py new file mode 100644 index 0000000000000000000000000000000000000000..277726278672f31b28c7fbcafe58c2520cc18d2a --- /dev/null +++ b/mindxtrain/autotune/gemm_probe.py @@ -0,0 +1,73 @@ +"""hipBLASLt GEMM heuristic selection. + +Previously this returned `hipblaslt_default` unconditionally. It now runs a short +GEMM microbenchmark on a representative MI300X LoRA shape when torch + a GPU are +available, records the timing into the AutotunePlan, and promotes the heuristic +to `hipblaslt_tuned` when ROCm TunableOp tuning is active (PYTORCH_TUNABLEOP_ENABLED). +On a CPU dev box (no torch GPU) it stays at the documented default with no timing. + +Reference: AMD ROCm 7.2.1 release notes — hipBLASLt 0.10 default heuristic for +gfx942 BF16/FP16 GEMMs is within ~5% of hand-tuned variants for the shapes +mindXtrain hits (LoRA rank 16-64 on hidden 2048-8192); TunableOp closes the rest. +""" + +from __future__ import annotations + +import importlib.util +import os +import time + +from mindxtrain.autotune.plan import GemmHeuristic, ProbeTiming + +# Representative MI300X training GEMM: (M, K) x (K, N) — a hidden=8192 projection +# at batch*seq = 4096 tokens, the dominant LoRA-base shape. +_GEMM_SHAPE = (4096, 8192, 8192) +_ITERATIONS = 10 + + +def _tunableop_active() -> bool: + return os.environ.get("PYTORCH_TUNABLEOP_ENABLED", "0") not in {"", "0", "false", "False"} + + +def probe_gemm(gpu_index: int = 0) -> tuple[GemmHeuristic, list[ProbeTiming]]: + """Return (heuristic, timings) for the autotune plan. + + Returns ('hipblaslt_default', []) when torch + GPU aren't available so the + dry-run / CPU path stays deterministic. + """ + _ = gpu_index + if importlib.util.find_spec("torch") is None: + return "hipblaslt_default", [] + + try: + import torch + + if not torch.cuda.is_available(): + return "hipblaslt_default", [] + + m, k, n = _GEMM_SHAPE + device = "cuda" + dtype = torch.bfloat16 + a = torch.randn(m, k, device=device, dtype=dtype) + b = torch.randn(k, n, device=device, dtype=dtype) + + for _ in range(3): # warmup (also primes TunableOp tuning) + _ = a @ b + torch.cuda.synchronize() + + t0 = time.perf_counter() + for _ in range(_ITERATIONS): + _ = a @ b + torch.cuda.synchronize() + median_ms = (time.perf_counter() - t0) * 1000.0 / _ITERATIONS + except (RuntimeError, ImportError, OSError): + return "hipblaslt_default", [] + + heuristic: GemmHeuristic = "hipblaslt_tuned" if _tunableop_active() else "hipblaslt_default" + timing = ProbeTiming( + label=f"gemm-{m}x{k}x{n}", + backend=heuristic, + median_ms=float(median_ms), + iterations=_ITERATIONS, + ) + return heuristic, [timing] diff --git a/mindxtrain/autotune/plan.py b/mindxtrain/autotune/plan.py new file mode 100644 index 0000000000000000000000000000000000000000..d2179013a6be8928851e563ef55b911fe3d704eb --- /dev/null +++ b/mindxtrain/autotune/plan.py @@ -0,0 +1,49 @@ +"""AutotunePlan — output of the 60s MI300X probe, consumed by the training layer. + +The plan is the contract between the autotune module (Day 2 work) and the +training dispatch (Day 3 work). It must be pure data so a plan generated on +one MI300X can be replayed on another, and so a plan can be hand-written for +testing. +""" + +from __future__ import annotations + +from typing import Literal + +from pydantic import BaseModel, ConfigDict, Field + +AttentionBackend = Literal["ck", "triton"] +GemmHeuristic = Literal["hipblaslt_default", "hipblaslt_tuned", "rocblas_fallback"] +RcclConfig = Literal["1gpu_noop", "8gpu_xgmi", "unsupported_2_4_gpu"] + + +class ProbeTiming(BaseModel): + """Single probe measurement (one shape, one backend).""" + + model_config = ConfigDict(extra="forbid", frozen=True) + + label: str + backend: str + median_ms: float = Field(ge=0.0) + iterations: int = Field(ge=1) + + +class AutotunePlan(BaseModel): + """Static AOT plan written by `mindxtrain bench` and read by `mindxtrain train`.""" + + model_config = ConfigDict(extra="forbid") + + schema_version: Literal["1"] = "1" + gpu_arch: str = Field(default="gfx942") + rocm_version: str = Field(default="7.2.1") + + attention_backend: AttentionBackend = "ck" + gemm_heuristic: GemmHeuristic = "hipblaslt_default" + rccl_config: RcclConfig = "1gpu_noop" + + fsdp_shard_width: Literal[1, 8] = 1 + suggested_lora_rank: int = Field(default=16, ge=1, le=512) + suggested_micro_batch_size: int = Field(default=4, ge=1) + + probe_timings: list[ProbeTiming] = Field(default_factory=list) + notes: list[str] = Field(default_factory=list) diff --git a/mindxtrain/autotune/rccl_probe.py b/mindxtrain/autotune/rccl_probe.py new file mode 100644 index 0000000000000000000000000000000000000000..dd127a405798648cf0d34acf618d69c59a001525 --- /dev/null +++ b/mindxtrain/autotune/rccl_probe.py @@ -0,0 +1,76 @@ +"""RCCL collective config selection. + +MI300X xGMI bandwidth is asymmetric on 2- and 4-GPU groupings; FSDP shard +topology must be 1-GPU or 8-GPU. We hard-fail anything else here so the +training dispatch refuses to launch a misconfigured run. + +GPU count is auto-detected at probe time (torch first, then `rocminfo`), so +`mindxtrain bench` self-selects '1gpu_noop' vs '8gpu_xgmi' on the box it runs +on. Pass an explicit `gpu_count` to override (used by tests). +""" + +from __future__ import annotations + +import importlib.util +import shutil +import subprocess + +from mindxtrain.autotune.plan import RcclConfig + + +def detect_gpu_count() -> int: + """Best-effort GPU count: torch.cuda first, then `rocminfo`, else 0. + + On ROCm, `torch.cuda.device_count()` reports HIP devices. When torch isn't + installed (typical CPU dev box), fall back to counting GPU agents in + `rocminfo`. Returns 0 when nothing is detectable. + """ + if importlib.util.find_spec("torch") is not None: + try: + import torch + + if torch.cuda.is_available(): + return int(torch.cuda.device_count()) + except (RuntimeError, ImportError, OSError): + pass + + rocminfo = shutil.which("rocminfo") + if rocminfo is not None: + try: + out = subprocess.run( + [rocminfo], + capture_output=True, + text=True, + timeout=15.0, + check=False, + ) + except (OSError, subprocess.SubprocessError): + return 0 + if out.returncode == 0: + # Each GPU agent block reports `Device Type: GPU`. + return sum( + 1 + for line in out.stdout.splitlines() + if "Device Type:" in line and "GPU" in line + ) + return 0 + + +def probe_rccl(gpu_index: int = 0, gpu_count: int | None = None) -> RcclConfig: + """Pick the RCCL config; refuse 2/4-GPU sharding. + + `gpu_count=None` auto-detects. A detected count of 0 (no GPU, CPU dev box) + maps to the single-device no-op config so the plan stays consumable. + """ + _ = gpu_index + if gpu_count is None: + gpu_count = detect_gpu_count() + if gpu_count in (0, 1): + return "1gpu_noop" + if gpu_count == 8: + return "8gpu_xgmi" + msg = ( + f"FSDP on {gpu_count} GPUs is unsafe on MI300X due to xGMI bandwidth asymmetry. " + "Use 1 or 8 GPUs (mindXtrain2.md §13)." + ) + raise RuntimeError(msg) diff --git a/mindxtrain/budget/__init__.py b/mindxtrain/budget/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/mindxtrain/budget/pricing.py b/mindxtrain/budget/pricing.py new file mode 100644 index 0000000000000000000000000000000000000000..a1b27e99d5b5a4ae6cb66c9ef5ba1c45381661cf --- /dev/null +++ b/mindxtrain/budget/pricing.py @@ -0,0 +1,15 @@ +"""Pricing function for x402 invoices. + +Per blueprint: per-GPU-hour rate * estimated_steps * safety_margin. +$1.99/hr is the AMD Developer Cloud single-MI300X rate (May 2026). +""" + +from __future__ import annotations + +MI300X_USDC_PER_HOUR = 1.99 +SAFETY_MARGIN = 1.15 + + +def gpu_hour_price(gpus: int, hours: float, *, safety_margin: float = SAFETY_MARGIN) -> float: + """Quote in USDC for `gpus` MI300X x `hours`.""" + return gpus * hours * MI300X_USDC_PER_HOUR * safety_margin diff --git a/mindxtrain/budget/providers/__init__.py b/mindxtrain/budget/providers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/mindxtrain/budget/providers/akash.py b/mindxtrain/budget/providers/akash.py new file mode 100644 index 0000000000000000000000000000000000000000..87aa72be8f3c472b9689d8f366f370a2a46bdc26 --- /dev/null +++ b/mindxtrain/budget/providers/akash.py @@ -0,0 +1,8 @@ +"""Akash Network decentralized compute fallback. Stub.""" + +from __future__ import annotations + + +def provision_akash(job_spec: dict[str, str]) -> dict[str, str]: + msg = "provision_akash is a Day-1 stub; post-hackathon wiring." + raise NotImplementedError(msg) diff --git a/mindxtrain/budget/providers/amd_dev_cloud.py b/mindxtrain/budget/providers/amd_dev_cloud.py new file mode 100644 index 0000000000000000000000000000000000000000..b7686523dadb8029b2e2b40a18544ebbb72d1c2a --- /dev/null +++ b/mindxtrain/budget/providers/amd_dev_cloud.py @@ -0,0 +1,37 @@ +"""AMD Developer Cloud (DigitalOcean-hosted MI300X) — superseded. + +The real implementation lives at `mindxtrain.deploy.amd_dev_cloud`. This +file is kept as a back-compat re-export so older budget-pricing callers +keep working. New code should import from `mindxtrain.deploy.amd_dev_cloud` +directly. +""" + +from __future__ import annotations + +from mindxtrain.deploy.amd_dev_cloud import ( + AmdDevCloudClient, + AmdDevCloudConfig, + AmdDevCloudError, +) + + +def provision_mi300x(gpu_count: int = 1, region: str = "atl1") -> dict[str, str]: + """Back-compat entry point. Real provisioning lives in the operator. + + Use `mindxtrain droplet provision` (CLI) or the Coach UI's "Provision + MI300X droplet" button instead. + """ + msg = ( + "provision_mi300x has moved: use `mindxtrain droplet provision` or the " + "Coach UI's deploy panel. Programmatic API: " + "mindxtrain.deploy.amd_dev_cloud.AmdDevCloudClient.create()." + ) + raise NotImplementedError(msg) + + +__all__ = [ + "AmdDevCloudClient", + "AmdDevCloudConfig", + "AmdDevCloudError", + "provision_mi300x", +] diff --git a/mindxtrain/budget/providers/bacalhau.py b/mindxtrain/budget/providers/bacalhau.py new file mode 100644 index 0000000000000000000000000000000000000000..8a45e5b81b456f3f2a29c9e11cd705d05e1fea98 --- /dev/null +++ b/mindxtrain/budget/providers/bacalhau.py @@ -0,0 +1,8 @@ +"""Bacalhau decentralized compute fallback. Stub.""" + +from __future__ import annotations + + +def provision_bacalhau(job_spec: dict[str, str]) -> dict[str, str]: + msg = "provision_bacalhau is a Day-1 stub; post-hackathon wiring." + raise NotImplementedError(msg) diff --git a/mindxtrain/budget/providers/ionet.py b/mindxtrain/budget/providers/ionet.py new file mode 100644 index 0000000000000000000000000000000000000000..a40a7542f98b312604e5fe04fd62268a1c0eb76b --- /dev/null +++ b/mindxtrain/budget/providers/ionet.py @@ -0,0 +1,8 @@ +"""io.net decentralized compute fallback. Stub.""" + +from __future__ import annotations + + +def provision_ionet(job_spec: dict[str, str]) -> dict[str, str]: + msg = "provision_ionet is a Day-1 stub; post-hackathon wiring." + raise NotImplementedError(msg) diff --git a/mindxtrain/budget/providers/tensorwave.py b/mindxtrain/budget/providers/tensorwave.py new file mode 100644 index 0000000000000000000000000000000000000000..a95e8afbb77aad748b86bb0859a7db76c1f6664c --- /dev/null +++ b/mindxtrain/budget/providers/tensorwave.py @@ -0,0 +1,8 @@ +"""TensorWave bare-metal MI300X provisioning hook. Stub.""" + +from __future__ import annotations + + +def provision_tensorwave(gpu_count: int = 1) -> dict[str, str]: + msg = "provision_tensorwave is a Day-1 stub; post-hackathon wiring." + raise NotImplementedError(msg) diff --git a/mindxtrain/budget/resource.py b/mindxtrain/budget/resource.py new file mode 100644 index 0000000000000000000000000000000000000000..273eac1d2a65cd96e0cec83ebddcafc086bbed1d --- /dev/null +++ b/mindxtrain/budget/resource.py @@ -0,0 +1,110 @@ +"""ResourceBudget — psutil + rocm-smi-derived training-job sizing helper. + +Reads host RAM, CPU, GPU memory; emits a recommended `micro_batch_size` +that fits under the budget for a given `seq_len`. psutil + rocm-smi are +both lazy / optional — falls back to conservative defaults if neither is +available. +""" + +from __future__ import annotations + +import json +import shutil +import subprocess +from typing import cast + +from pydantic import BaseModel, ConfigDict, Field + + +class ResourceBudget(BaseModel): + model_config = ConfigDict(extra="forbid") + + host_ram_gb: float = Field(ge=0.0) + cpu_cores: int = Field(ge=1) + gpu_count: int = Field(ge=0) + gpu_mem_gb_each: float = Field(ge=0.0) + + +def _host_ram_gb() -> float: + try: + import psutil + + return float(psutil.virtual_memory().total) / (1024**3) + except ImportError: + return 0.0 + + +def _cpu_cores() -> int: + try: + import psutil + + return int(psutil.cpu_count(logical=True) or 1) + except ImportError: + import os + + return os.cpu_count() or 1 + + +def _gpu_mem_gb() -> tuple[int, float]: + """Return (gpu_count, gpu_mem_gb_each).""" + if shutil.which("rocm-smi") is None: + return (0, 0.0) + try: + out = subprocess.run( + ["rocm-smi", "--showmeminfo", "vram", "--json"], + capture_output=True, + text=True, + timeout=10.0, + check=False, + ) + except (subprocess.TimeoutExpired, OSError): + return (0, 0.0) + if out.returncode != 0: + return (0, 0.0) + try: + data = json.loads(out.stdout) + except json.JSONDecodeError: + return (0, 0.0) + cards = [k for k in data if k.startswith("card")] + if not cards: + return (0, 0.0) + total_bytes = 0.0 + for c in cards: + for k, v in (data[c] or {}).items(): + if "total" in k.lower(): + try: + total_bytes = max(total_bytes, float(v)) + break + except ValueError: + continue + if total_bytes == 0.0: + return (len(cards), 0.0) + return (len(cards), total_bytes / (1024**3)) + + +def detect() -> ResourceBudget: + """Return a `ResourceBudget` snapshot of the current host.""" + gpu_count, gpu_mem = _gpu_mem_gb() + return ResourceBudget( + host_ram_gb=_host_ram_gb(), + cpu_cores=_cpu_cores(), + gpu_count=gpu_count, + gpu_mem_gb_each=gpu_mem, + ) + + +def recommend_micro_batch(budget: ResourceBudget, seq_len: int, *, dtype_bytes: int = 2) -> int: + """Return a conservative `micro_batch_size` that fits under the budget. + + Heuristic: each token consumes ~`dtype_bytes` bytes for activations + a + factor for KV cache + gradients. We leave 30% headroom. + """ + if budget.gpu_mem_gb_each <= 0: + return 1 + bytes_per_token = dtype_bytes * 16 # rough activations + grads + KV factor + available = budget.gpu_mem_gb_each * 0.7 * (1024**3) + bs = int(available / (seq_len * bytes_per_token)) + return cast(int, max(1, bs)) + + +__all__ = ["ResourceBudget", "detect", "recommend_micro_batch"] diff --git a/mindxtrain/cli/__init__.py b/mindxtrain/cli/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/mindxtrain/cli/main.py b/mindxtrain/cli/main.py new file mode 100644 index 0000000000000000000000000000000000000000..7855c226b66c63485757316aaf21306c423fbee7 --- /dev/null +++ b/mindxtrain/cli/main.py @@ -0,0 +1,942 @@ +"""mindxtrain CLI — Typer entry point for all 8 verbs.""" + +from __future__ import annotations + +from pathlib import Path + +import typer +from rich.console import Console + +from mindxtrain import __version__ +from mindxtrain.autotune.benchmark import run_autotune +from mindxtrain.autotune.plan import AutotunePlan +from mindxtrain.config.loader import list_recipes, load_config, render_recipe + +app = typer.Typer( + name="mindxtrain", + help="mindxtrain: 60s AOT autotune + multi-backend training + Quark FP8 quantize for MI300X.", + no_args_is_help=True, +) +dataset_app = typer.Typer(name="dataset", help="Dataset preparation subcommands.", no_args_is_help=True) +github_app = typer.Typer(name="github", help="GitHub source-tree publishing.", no_args_is_help=True) +droplet_app = typer.Typer(name="droplet", help="AMD Dev Cloud MI300X provision + sync.", no_args_is_help=True) +mei_app = typer.Typer( + name="mei", + help="mindX Efficiency Index — score, history, promotion checks.", + no_args_is_help=True, +) +app.add_typer(dataset_app) +app.add_typer(github_app) +app.add_typer(droplet_app) +app.add_typer(mei_app) +console = Console() + + +def _version_cb(value: bool) -> None: + if value: + console.print(f"mindxtrain {__version__}") + raise typer.Exit + + +@app.callback() +def _root( + version: bool = typer.Option( + False, + "--version", + callback=_version_cb, + is_eager=True, + help="Print version and exit.", + ), +) -> None: + """mindxtrain entry point.""" + + +# ---- init / bench --------------------------------------------------------- + + +@app.command() +def init( + template: str = typer.Option( + "qwen3_8b_sft_lora", + "--template", + "-t", + help="recipe name (run `mindxtrain init --list` to see all)", + ), + out: Path = typer.Option( + Path("run.yaml"), + "--out", + "-o", + help="output YAML path", + ), + list_only: bool = typer.Option( + False, + "--list", + help="list all built-in recipe names and exit", + ), +) -> None: + """Write a starter YAML config from a built-in recipe.""" + if list_only: + for name in list_recipes(): + console.print(f" {name}") + raise typer.Exit + yaml_text = render_recipe(template) + out.write_text(yaml_text) + console.print(f"[green]wrote[/green] {out} ({len(yaml_text)} bytes, recipe={template!r})") + + +@app.command() +def bench( + out: Path = typer.Option(Path("autotune_plan.json"), "--out", "-o"), + gpu: int = typer.Option(0, "--gpu", help="HIP/ROCm device index"), + dry_run: bool = typer.Option( + False, + "--dry-run", + help="Skip GPU probes; emit a hardcoded reference plan.", + ), +) -> None: + """Run the 60-second AOT autotune probe and write autotune_plan.json.""" + plan: AutotunePlan = run_autotune(gpu_index=gpu, dry_run=dry_run) + out.write_text(plan.model_dump_json(indent=2)) + console.print( + f"[green]wrote[/green] {out} (dry_run={dry_run}, " + f"attention={plan.attention_backend}, gemm={plan.gemm_heuristic})", + ) + + +# ---- train / eval / quantize / serve -------------------------------------- + + +def _load_plan(plan_path: Path | None) -> AutotunePlan: + if plan_path and plan_path.exists(): + return AutotunePlan.model_validate_json(plan_path.read_text()) + return run_autotune(gpu_index=0, dry_run=True) + + +@app.command() +def train( + config: Path = typer.Argument(..., help="path to XTrainConfig YAML"), + plan_path: Path = typer.Option(None, "--plan", help="autotune plan JSON; falls back to dry-run."), + out: Path = typer.Option(Path("./out/runs"), "--out", "-o", help="run output root"), + cpu_percent: int | None = typer.Option( + None, "--cpu-percent", + help=( + "Override `train.cpu_throttle.percent` at runtime. Applies " + "only to the trl_cpu backend. 1-100; below 1 or above 100 errors." + ), + ), + cpu_nice: int | None = typer.Option( + None, "--cpu-nice", + help="Override `train.cpu_throttle.nice_level`. -20..19.", + ), +) -> None: + """Dispatch a training run via the configured backend. + + With --cpu-percent N, the trl_cpu backend caps every thread pool + (torch, OpenMP, MKL, OpenBLAS) at N% of the host's cores. Useful for + leaving cycles free for the rest of the laptop while training runs in + the background. + """ + from mindxtrain.config.schema import CPUThrottleCfg + from mindxtrain.train import dispatch_training + + cfg = load_config(config) + # CLI override: rebuild train.cpu_throttle if either knob was passed. + if cpu_percent is not None or cpu_nice is not None: + throttle = cfg.train.cpu_throttle + new_throttle = CPUThrottleCfg( + percent=cpu_percent if cpu_percent is not None else throttle.percent, + nice_level=cpu_nice if cpu_nice is not None else throttle.nice_level, + omp_proc_bind=throttle.omp_proc_bind, + ) + # Pydantic frozen=True forbids in-place mutation; rebuild via model_copy. + new_train = cfg.train.model_copy(update={"cpu_throttle": new_throttle}) + cfg = cfg.model_copy(update={"train": new_train}) + console.print( + f"[dim]cpu_throttle overridden: percent={new_throttle.percent} " + f"nice={new_throttle.nice_level}[/dim]", + ) + + plan = _load_plan(plan_path) + run_dir = out / cfg.meta.run_name + try: + ckpt = dispatch_training(cfg, plan, run_dir) + except RuntimeError as exc: + console.print(f"[red]training failed:[/red] {exc}") + raise typer.Exit(code=3) from exc + console.print(f"[green]checkpoint:[/green] {ckpt}") + + +@app.command(name="eval") +def eval_( + config: Path = typer.Argument(...), + checkpoint: Path = typer.Option(None, "--checkpoint", "-c", help="checkpoint dir; default = ./out/runs/<run_name>/checkpoint"), +) -> None: + """Run lm-eval-harness against a checkpoint.""" + from mindxtrain.eval.harness import parse_summary, run_lm_eval + + cfg = load_config(config) + ckpt = checkpoint or Path("./out/runs") / cfg.meta.run_name / "checkpoint" + if not ckpt.exists(): + console.print(f"[red]checkpoint not found:[/red] {ckpt}") + raise typer.Exit(code=1) + tasks = list(cfg.eval.harness.tasks) if cfg.eval and cfg.eval.harness else ["mmlu"] + try: + results = run_lm_eval(ckpt, tasks) + except RuntimeError as exc: + console.print(f"[red]eval failed:[/red] {exc}") + raise typer.Exit(code=3) from exc + console.print(f"[green]results:[/green] {results}") + console.print_json(data=parse_summary(results)) + + +@app.command(name="eval-checkpoint") +def eval_checkpoint( + config: Path = typer.Argument(...), + checkpoint: Path = typer.Option( + None, "--checkpoint", "-c", + help="LoRA adapter dir; default = ./out/runs/<run_name>/checkpoint", + ), + jsonl: Path = typer.Option( + None, "--jsonl", + help=( + "Path to a *_training.jsonl held-out file. Defaults to picking " + "the newest one under `data.path/ltm/**/*_training.jsonl` " + "(works for source='mindx_dreams')." + ), + ), + max_samples: int = typer.Option( + 32, "--max-samples", "-n", + help="Cap on how many rows to evaluate. Held-out CE is averaged.", + ), +) -> None: + """Compare base-model vs base+adapter cross-entropy on held-out chat rows. + + The training trainer_state.json gives a train-loss curve but doesn't + answer whether the adapter generalises — this verb does. Prints + `{base_loss, adapter_loss, delta}` where `delta < 0` means the + adapter is actually moving the model toward the held-out dreams. + + The default jsonl picker walks the recipe's `data.path` for the + newest `*_training.jsonl` — for mindX dreams this is the corpus the + recipe trained on. For a rigorous held-out check, point `--jsonl` at + a file the training run never saw. + """ + from mindxtrain.eval.held_out_loss import score_checkpoint + + cfg = load_config(config) + ckpt = checkpoint or Path("./out/runs") / cfg.meta.run_name / "checkpoint" + if not ckpt.exists(): + console.print(f"[red]adapter dir not found:[/red] {ckpt}") + raise typer.Exit(code=1) + + jsonl_path = jsonl + if jsonl_path is None: + if cfg.data.source != "mindx_dreams" or cfg.data.path is None: + console.print( + "[red]--jsonl required when data.source != mindx_dreams " + "(no default picker for non-dream sources).", + ) + raise typer.Exit(code=2) + candidates = sorted( + Path(cfg.data.path).glob("ltm/**/*_training.jsonl"), + key=lambda p: p.stat().st_mtime, + reverse=True, + ) + if not candidates: + console.print(f"[red]no *_training.jsonl under {cfg.data.path}/ltm") + raise typer.Exit(code=2) + jsonl_path = candidates[0] + console.print(f"[dim]using newest dream file: {jsonl_path}[/dim]") + + try: + score = score_checkpoint( + adapter_dir=ckpt, + base_model=cfg.model.name, + jsonl_path=jsonl_path, + max_samples=max_samples, + sink=lambda line: console.print(line), + ) + except (ImportError, ValueError) as exc: + console.print(f"[red]eval-checkpoint failed:[/red] {exc}") + raise typer.Exit(code=3) from exc + + verdict = ( + "[green]adapter improved[/green]" if score.delta < 0 + else "[yellow]adapter regressed[/yellow]" + ) + console.print( + f"\n{verdict} on {score.n} held-out rows: " + f"base={score.base_loss:.4f} adapter={score.adapter_loss:.4f} " + f"delta={score.delta:+.4f}", + ) + console.print_json(data=score.as_dict()) + + +@app.command() +def quantize( + config: Path = typer.Argument(...), + checkpoint: Path = typer.Option(None, "--checkpoint", "-c"), +) -> None: + """Quark FP8 / MXFP4 quantize the trained checkpoint.""" + from mindxtrain.deploy.quark import quark_fp8, quark_mxfp4 + + cfg = load_config(config) + ckpt = checkpoint or Path("./out/runs") / cfg.meta.run_name / "checkpoint" + if not ckpt.exists(): + console.print(f"[red]checkpoint not found:[/red] {ckpt}") + raise typer.Exit(code=1) + out_dir = ckpt.parent / "quantized" + fn = quark_fp8 if cfg.quantize.scheme == "fp8_e4m3" else quark_mxfp4 + try: + path = fn(ckpt, out_dir) + except RuntimeError as exc: + console.print(f"[red]quantize failed:[/red] {exc}") + raise typer.Exit(code=3) from exc + console.print(f"[green]quantized:[/green] {path}") + + +@app.command() +def serve( + config: Path = typer.Argument(...), + checkpoint: Path = typer.Option(None, "--checkpoint", "-c"), + to: str = typer.Option( + "vllm", "--to", + help="Serve target: vllm (default, builds vllm-rocm launch cmd), " + "sglang (builds sglang-rocm launch cmd), or " + "ollama (merges LoRA + calls `ollama create`).", + ), + tag: str = typer.Option( + None, "--tag", + help="Ollama tag for `--to ollama`. Defaults to run_name when omitted.", + ), + ollama_bin: str = typer.Option( + None, "--ollama-bin", + help="Override the ollama binary path (defaults to PATH lookup).", + ), + register_as_fallback: bool = typer.Option( + False, "--register-as-fallback", + help=( + "After --to ollama succeeds, PATCH the new tag into mindX as " + "the local-fallback model (PATCH /v1/config/fallback-model). " + "Best-effort — a failure logs but does NOT fail the push." + ), + ), + mindx_base_url: str = typer.Option( + None, "--mindx-base-url", + help=( + "Override the mindX base URL for --register-as-fallback. " + "Defaults to MINDXTRAIN_API_BASE_URL env or " + "https://mindx.pythai.net." + ), + ), +) -> None: + """Serve the trained checkpoint locally. + + `--to vllm` (default) prints a vllm-rocm launch command against the + quantized checkpoint — wire it into your orchestrator. + + `--to ollama` runs the local-learning loop: merges the LoRA adapter + into the base weights, writes an ollama Modelfile, and calls + `ollama create <tag>` so the trained model is immediately available + on the loopback (the same backend Coach probes for its chat card). + """ + cfg = load_config(config) + + if to == "ollama": + from mindxtrain.deploy.ollama_push import push_to_ollama + + # The LoRA adapter is at <run_dir>/checkpoint/ — same location + # the trl_cpu / axolotl backends save to. + adapter_dir = checkpoint or Path("./out/runs") / cfg.meta.run_name / "checkpoint" + if not adapter_dir.exists(): + console.print(f"[red]checkpoint not found:[/red] {adapter_dir}") + raise typer.Exit(code=1) + + resolved_tag = tag or cfg.meta.run_name + try: + result = push_to_ollama( + base_model=cfg.model.name, + adapter_dir=adapter_dir, + tag=resolved_tag, + sink=lambda line: console.print(line), + ollama_bin=ollama_bin, + register_with_mindx=register_as_fallback, + mindx_base_url=mindx_base_url, + ) + except (FileNotFoundError, ImportError) as exc: + console.print(f"[red]push-to-ollama failed:[/red] {exc}") + raise typer.Exit(code=2) from exc + console.print( + f"[green]pushed:[/green] {result.tag} " + f"(merged: {result.merged_dir}, Modelfile: {result.modelfile})", + ) + if result.mindx_fallback_swap: + console.print( + f"[green]mindX fallback swapped:[/green] " + f"{result.mindx_fallback_swap.get('previous', '?')} -> " + f"{result.mindx_fallback_swap.get('current', '?')}", + ) + return + + if to not in ("vllm", "sglang"): + console.print(f"[red]unknown serve target:[/red] {to}") + raise typer.Exit(code=2) + + ckpt = checkpoint or Path("./out/runs") / cfg.meta.run_name / "quantized" + if not ckpt.exists(): + console.print(f"[red]quantized checkpoint not found:[/red] {ckpt}") + raise typer.Exit(code=1) + + if to == "sglang": + from mindxtrain.deploy.sglang_rocm import build_sglang_command + + cmd = build_sglang_command(cfg.serve, ckpt) + console.print(f"[green]sglang cmd:[/green] {' '.join(cmd)}") + return + + from mindxtrain.deploy.vllm_launcher import build_vllm_command + + cmd = build_vllm_command(cfg.serve, ckpt, cfg.quantize) + console.print(f"[green]vllm cmd:[/green] {' '.join(cmd)}") + # Caller can pipe the cmd into their orchestrator; we don't exec by default. + + +# ---- dataset prep --------------------------------------------------------- + + +@dataset_app.command("prep") +def dataset_prep( + config: Path = typer.Argument(..., help="path to XTrainConfig YAML"), + out: Path = typer.Option(Path("./out/dataset"), "--out", "-o"), +) -> None: + """Run the dataset pipeline: curate -> filter -> tokenize -> pack -> shard.""" + from mindxtrain.data.curate import load_streaming_dataset + from mindxtrain.data.filter import quality_filter + from mindxtrain.data.pack import emit_shards, pack_sequences + from mindxtrain.data.tokenize import tokenize_stream + + cfg = load_config(config) + out.mkdir(parents=True, exist_ok=True) + try: + rows = load_streaming_dataset(cfg.data) + texts = (row.get("text") or row.get("content") or "" for row in rows) + clean = quality_filter(texts) + tokenized = tokenize_stream(clean, cfg.model.name) + packed = pack_sequences(tokenized, cfg.data.seq_len) + shard_dir = emit_shards(packed, out) + except RuntimeError as exc: + console.print(f"[red]dataset prep failed:[/red] {exc}") + raise typer.Exit(code=3) from exc + console.print(f"[green]shards:[/green] {shard_dir}") + + +# ---- publish / receipt ---------------------------------------------------- + + +@app.command() +def publish( + config: Path = typer.Argument(...), + manifest: Path = typer.Option(..., "--manifest", "-m", help="path to provenance manifest.json"), + skip_hf: bool = typer.Option(False, "--skip-hf"), + skip_pin: bool = typer.Option(False, "--skip-pin"), + force: bool = typer.Option( + False, "--force", + help="Skip the MEI promotion gate. The manifest records promotion_bypassed=true.", + ), +) -> None: + """Push to HF Hub + Lighthouse + register the provenance manifest with the mindX API. + + By default this verb consults the historical MEI ledger: if there's a + score for this run_id and it doesn't pass the §8 promotion gates, the + push is refused with the failing-gate reasons surfaced. `--force` + skips the gate (records `promotion_bypassed=true` in the manifest). + """ + from mindxtrain.deploy.api_client import register_with_mindx + from mindxtrain.eval.mei import history as _mei_history + from mindxtrain.eval.mei.score import is_promotable + from mindxtrain.provenance.manifest import Manifest + from mindxtrain.storage.hf_hub import publish_to_hf + from mindxtrain.storage.lighthouse import publish_to_lighthouse + + cfg = load_config(config) + m = Manifest.model_validate_json(manifest.read_text()) + ckpt_dir = Path("./out/runs") / cfg.meta.run_name / "checkpoint" + + # MEI promotion gate. Skip silently when there's no MEI score yet — + # the gate is informational, not mandatory at intake (so existing + # publish flows pre-MEI continue to work). With --force, we proceed + # regardless and stamp the manifest so the bypass is auditable. + mei_entries = [e for e in _mei_history.read_all() if e.run_id == m.run_id] + if mei_entries: + latest = mei_entries[-1] + prior = _mei_history.currently_promoted() + prior_score = ( + prior.score if prior is not None and prior.run_id != m.run_id else None + ) + ok, reasons = is_promotable(latest.score, prior_promoted=prior_score) + if ok: + console.print( + f"[green]MEI gate:[/green] {latest.score.composite:.3f} ≥ 0.55, " + "all sub-indices ≥ 0.30 — promotable.", + ) + elif force: + console.print( + "[yellow]MEI gate failed but --force given; " + "marking promotion_bypassed=true in manifest:[/yellow]", + ) + for reason in reasons: + console.print(f" • {reason}") + m.promotion_bypassed = True + m.promotion_bypass_reasons = reasons + else: + console.print("[red]MEI gate refused promotion:[/red]") + for reason in reasons: + console.print(f" • {reason}") + console.print( + "Pass --force to publish anyway (the bypass is recorded " + "in the manifest).", + ) + raise typer.Exit(code=4) + elif force: + console.print( + "[yellow]No MEI score on file; --force given. " + "Recommend running `mindxtrain mei score <record.json>` first.[/yellow]", + ) + + hf_url = "" + if not skip_hf and ckpt_dir.exists(): + try: + hf_url = publish_to_hf(ckpt_dir, f"{cfg.meta.run_name}", private=False) + m.hf_repo_id = hf_url + console.print(f"[green]HF:[/green] {hf_url}") + except RuntimeError as exc: + console.print(f"[yellow]hf upload skipped:[/yellow] {exc}") + + cid = "" + if not skip_pin and ckpt_dir.exists(): + try: + cid = publish_to_lighthouse(ckpt_dir) + m.lighthouse_cid = cid + console.print(f"[green]Lighthouse:[/green] {cid}") + except RuntimeError as exc: + console.print(f"[yellow]lighthouse pin skipped:[/yellow] {exc}") + + try: + receipt = register_with_mindx(run_id=m.run_id, hf_url=hf_url, cid=cid) + console.print(f"[green]mindX:[/green] {receipt}") + except (RuntimeError, Exception) as exc: + console.print(f"[yellow]mindX register skipped:[/yellow] {exc}") + + manifest.write_text(m.model_dump_json(indent=2)) + console.print(f"[green]updated manifest:[/green] {manifest}") + + +@app.command() +def ui( + host: str = typer.Option("127.0.0.1", help="bind address"), + port: int = typer.Option(7862, help="port"), + share: bool = typer.Option(False, help="also expose a public gradio.live link"), + mcp: bool = typer.Option(True, help="serve the rooms as MCP tools too"), +) -> None: + """Open the Gradio UI: the whole framework on one surface (Basic / Advanced / Scientific).""" + try: + from mindxtrain.ui import main as _ui_main + except ImportError as exc: # pragma: no cover - depends on the extra + msg = "the UI needs gradio: `uv sync --extra ui`" + raise SystemExit(msg) from exc + _ui_main(host=host, port=port, share=share, mcp=mcp) + + +@app.command() +def receipt( + manifest: Path = typer.Argument(..., help="path to provenance manifest.json"), + config: Path = typer.Option(None, "--config"), +) -> None: + """Verify a provenance manifest's BLAKE3 hashes against on-disk artifacts.""" + from mindxtrain.provenance.manifest import Manifest + from mindxtrain.provenance.verify import verify_receipt + + if not manifest.is_file(): + console.print(f"[red]manifest not found:[/red] {manifest}") + raise typer.Exit(code=1) + m = Manifest.model_validate_json(manifest.read_text()) + console.print_json(data={"run_id": m.run_id, "blake3": m.blake3.model_dump()}) + + if config is None: + return + + cfg = load_config(config) + run_dir = Path("./out/runs") / cfg.meta.run_name + + # A run-emitted manifest snapshots the validated config to + # config.snapshot.yaml and persists the exact AutotunePlan bytes it hashed. + # Prefer those when present; fall back to the user-supplied --config for + # legacy manifests produced by `emit_receipt`. + snapshot = run_dir / "config.snapshot.yaml" + config_yaml_path = snapshot if snapshot.is_file() else config + plan_path = run_dir / "autotune_plan.json" + plan_json = plan_path.read_bytes() if plan_path.is_file() else None + + try: + result = verify_receipt( + m, + config_yaml_path=config_yaml_path, + dataset_manifest_path=run_dir / "dataset_manifest.json", + checkpoint_dir=run_dir / "checkpoint", + eval_json_path=run_dir / "eval/lm_eval.json", + plan_json=plan_json, + ) + except FileNotFoundError as exc: + console.print(f"[red]missing artifact:[/red] {exc}") + raise typer.Exit(code=1) from exc + console.print_json(data=result) + if not all(result.values()): + raise typer.Exit(code=2) + + +@app.command() +def imprint( + config: Path = typer.Argument(..., help="recipe whose checkpoint to measure"), + out: Path = typer.Option(Path("./out/runs"), "--out", "-o"), + max_inquiries: int = typer.Option(5, "--n", help="number of recall probes"), + trigger_dream: bool = typer.Option( + False, "--trigger-dream", + help="hand the imprinted actor to mindX's machine.dream 8hr cycle", + ), +) -> None: + """Measure a persona imprint: recall before vs after training. + + Poses the script's own user-turns back to the actor, comparing the base + model (before) and the trained adapter (after) against the script's + assistant voice. Prints an ImprintReport; exit 4 if no imprint was detected. + """ + import json as _json + + cfg = load_config(config) + run_dir = (out / cfg.meta.run_name) if out.name == "runs" else out + adapter_dir = run_dir / "checkpoint" + if not adapter_dir.exists(): + console.print(f"[red]no checkpoint to measure:[/red] {adapter_dir}") + raise typer.Exit(code=1) + + # Build inquiries (user-turns) + baseline voice (assistant-turns) from the + # local script the actor trained on. Falls back to default probes. + from mindxtrain.eval.imprint import default_inquiries, probe_recall, score_imprint + + inquiries: list[str] = [] + baseline: list[str] = [] + path = cfg.data.path + if path is not None and Path(path).exists(): + files = [Path(path)] if Path(path).is_file() else sorted(Path(path).rglob("*.jsonl")) + for f in files: + for line in f.read_text().splitlines(): + line = line.strip() + if not line: + continue + try: + row = _json.loads(line) + except _json.JSONDecodeError: + continue + msgs = row.get("messages", []) + u = next((m["content"] for m in msgs if m.get("role") == "user"), None) + a = next((m["content"] for m in msgs if m.get("role") == "assistant"), None) + if u and len(inquiries) < max_inquiries: + inquiries.append(u) + if a: + baseline.append(a) + if not inquiries: + inquiries = default_inquiries(cfg.meta.project)[:max_inquiries] + + console.print(f"[cyan]probing {len(inquiries)} inquiries (before/after)…[/cyan]") + try: + before = probe_recall(cfg.model.name, inquiries, force_cpu=True) + after = probe_recall(cfg.model.name, inquiries, adapter_dir=adapter_dir, force_cpu=True) + except RuntimeError as exc: + console.print(f"[red]imprint probe failed:[/red] {exc}") + raise typer.Exit(code=3) from exc + + report = score_imprint(inquiries, before, after, baseline or before) + console.print_json(data=report.model_dump()) + + if trigger_dream: + from mindxtrain.deploy.api_client import trigger_dream_ingestion + + res = trigger_dream_ingestion( + run_id=cfg.meta.run_name, + adapter_dir=str(adapter_dir), + base_model=cfg.model.name, + persona_name=cfg.meta.project, + imprint_delta=report.imprint_delta, + ) + console.print(f"[green]dream trigger:[/green] {res}") + + if not report.imprinted: + console.print("[yellow]no imprint detected (delta<=0 or no shift)[/yellow]") + raise typer.Exit(code=4) + + +# ---- research (autoresearch search over one editable file) -------------- + + +@app.command() +def research( + contract: Path = typer.Argument(..., help="Path to the AttemptContract TOML."), + researcher: str = typer.Option("codephreak", "--researcher", help="Researcher id."), + max_attempts: int = typer.Option(10, "--max-attempts", "-n", help="Edits to try."), + log_root: Path = typer.Option(Path("./out/research"), "--log-root", help="Ledger root."), + anchor: bool = typer.Option( + False, "--anchor", help="Anchor the champion lineage on Base (needs --extra chain)." + ), +) -> None: + """Run an autoresearch search: iterate edits on one file, keep iff the metric improves. + + Each attempt is fenced to the contract's editable file and committed before measuring, + so the search trail is a sequence of re-checkable git commits recorded in a durable + ledger (`<log-root>/attempts.jsonl`). Losers are `git reset --hard` to the champion. + """ + from mindxtrain.research.search import search_from_contract + + try: + result = search_from_contract( + contract, researcher=researcher, max_attempts=max_attempts, + log_root=log_root, do_anchor=anchor, + ) + except NotImplementedError as exc: + console.print(f"[yellow]{exc}[/yellow]") + raise typer.Exit(code=2) from exc + except Exception as exc: # ResearchAbort, git failures, etc. + console.print(f"[red]research aborted:[/red] {exc}") + raise typer.Exit(code=1) from exc + console.print(f"[green]{result.summary()}[/green]") + + +# ---- github / droplet (source-tree publishing + remote provision) ------- + + +@github_app.command("push") +def github_push_cmd( + commit_message: str = typer.Option( + "mindXtrain initial push", "--message", "-m", help="commit message" + ), + force: bool = typer.Option(False, "--force", help="use --force-with-lease on push"), +) -> None: + """Bootstrap a git repo, create the GitHub remote (via `gh`), push the working tree. + + Requires GITHUB_TOKEN + GITHUB_REPO in the environment. Reuses the same + builders as the Coach UI's "Push to GitHub" button — output is local-shell + rather than SSE-streamed. + """ + import os + import subprocess + + from mindxtrain.deploy.github_push import GithubConfig, bootstrap_steps, status_missing + + missing = status_missing() + if missing: + console.print(f"[red]missing:[/red] {', '.join(missing)}") + console.print("[yellow]hint:[/yellow] set GITHUB_TOKEN and GITHUB_REPO, install gh + git") + raise typer.Exit(code=2) + + cfg = GithubConfig( + token=os.environ["GITHUB_TOKEN"], + repo=os.environ["GITHUB_REPO"], + branch=os.environ.get("GITHUB_DEFAULT_BRANCH", "main"), + author_name=os.environ.get("GITHUB_AUTHOR_NAME", "mindXtrain bot"), + author_email=os.environ.get("GITHUB_AUTHOR_EMAIL", "noreply@pythai.net"), + ) + rcs: dict[str, int] = {} + for step in bootstrap_steps(cfg, commit_message=commit_message, force=force): + if step.predicate_step is not None: + gate = rcs.get(step.predicate_step) + if gate is None or gate not in step.predicate_rc_in: + console.print(f"[dim]skip[/dim] {step.label}") + rcs[step.label] = -1 + continue + console.print(f"[cyan]→ {step.label}[/cyan]: {' '.join(step.cmd[:6])}…") + proc = subprocess.run(step.cmd, env=step.env or None, check=False) + rcs[step.label] = proc.returncode + if proc.returncode != 0 and not step.allow_failure: + console.print(f"[red]{step.label} failed (rc={proc.returncode}); aborting[/red]") + raise typer.Exit(code=3) + console.print("[green]push complete[/green]") + + +@droplet_app.command("provision") +def droplet_provision_cmd( + name: str = typer.Option("mindxtrain", "--name"), + repo: str = typer.Option(None, "--repo", help="defaults to $GITHUB_REPO"), + branch: str = typer.Option(None, "--branch", help="defaults to $GITHUB_DEFAULT_BRANCH or 'main'"), + container: str = typer.Option(None, "--container", help="defaults to $DROPLET_CONTAINER"), + extras: str = typer.Option("ml,eval,data,obs", "--extras"), + wait: bool = typer.Option(True, "--wait/--no-wait", help="poll for cloud-init bootstrap completion"), +) -> None: + """POST a new MI300X droplet to AMD Dev Cloud + wait for cloud-init bootstrap. + + Requires AMD_DEV_CLOUD_TOKEN + AMD_DEV_CLOUD_SSH_KEY_ID. The droplet's + `user_data` clones from GitHub and runs `mindxtrain bench` as it boots, so + by the time SSH is reachable the autotune plan is on disk. + """ + import os + import time + + from mindxtrain.deploy import amd_dev_cloud as adc + from mindxtrain.deploy.cloud_init import render + + missing = adc.missing_env() + if missing: + console.print(f"[red]missing:[/red] {', '.join(missing)}") + raise typer.Exit(code=2) + cloud_cfg = adc.from_env() + user_data = render( + repo=repo or os.environ.get("GITHUB_REPO", "professor-codephreak/mindXtrain"), + branch=branch or os.environ.get("GITHUB_DEFAULT_BRANCH", "main"), + container=container or os.environ.get("DROPLET_CONTAINER", "rocm/primus:v26.2"), + extras=extras, + ) + + log = console.print + with adc.AmdDevCloudClient(cloud_cfg) as client: + droplet = client.create(name=name, user_data=user_data, log=lambda line: log(f"[cyan]{line}[/cyan]")) + droplet_id = int(droplet["id"]) + if not wait: + console.print(f"[green]droplet_id={droplet_id}[/green] — exiting before bootstrap (--no-wait)") + return + droplet = client.poll_until_active( + droplet_id, log=lambda line: log(f"[dim]{line}[/dim]"), sleep=time.sleep, now=time.monotonic + ) + ip = adc.extract_public_ip(droplet) or "" + console.print(f"[green]droplet_id={droplet_id} public_ip={ip}[/green]") + + +@droplet_app.command("sync") +def droplet_sync_cmd( + no_bench: bool = typer.Option(False, "--no-bench", help="rsync + provision only, skip bench"), + no_fetch: bool = typer.Option(False, "--no-fetch", help="don't scp plan.json back"), +) -> None: + """Rsync the working tree to $DROPLET_HOST + run bench inside rocm/primus. + + Requires DROPLET_HOST + DROPLET_USER. Reuses the same builders as the + Coach UI's "Sync to existing droplet" button — output is local-shell. + """ + import subprocess + + from mindxtrain.deploy.droplet import from_env, status_missing, sync_steps + + missing = status_missing() + if missing: + console.print(f"[red]missing:[/red] {', '.join(missing)}") + raise typer.Exit(code=2) + cfg = from_env() + plan_dest = Path("./out/plan.remote.json") + plan_dest.parent.mkdir(parents=True, exist_ok=True) + for step in sync_steps( + cfg, + repo_root=Path.cwd(), + run_bench=not no_bench, + fetch_plan=not no_fetch, + plan_dest=plan_dest, + ): + console.print(f"[cyan]→ {step.label}[/cyan]") + proc = subprocess.run(step.cmd, env=step.env or None, check=False) + if proc.returncode != 0 and not step.allow_failure: + console.print(f"[red]{step.label} failed (rc={proc.returncode})[/red]") + raise typer.Exit(code=3) + console.print("[green]sync complete[/green]") + + +# ---- mei verbs -------------------------------------------------------------- + + +@mei_app.command("score") +def mei_score( + record: Path = typer.Argument( + ..., help="Path to a JSON MEIRecord file (output of the measurement orchestrator).", + ), + out: Path | None = typer.Option( + None, "--out", help="Optional path to write the MEIScore JSON. Defaults to stdout.", + ), + append_history: bool = typer.Option( + True, "--history/--no-history", + help="Append the score to the historical-comparison ledger.", + ), +) -> None: + """Score a MEIRecord against the v0.1 anchors. Prints MEIScore JSON. + + The record JSON must conform to `mindxtrain.eval.mei.record.MEIRecord`. + Generate one via the measurement orchestrator (Phase 1.4) or hand-craft + against the schema for demos. + """ + + from mindxtrain.eval.mei.history import append as _hist_append + from mindxtrain.eval.mei.record import MEIRecord + from mindxtrain.eval.mei.score import score_record + + rec = MEIRecord.model_validate_json(record.read_text()) + sc = score_record(rec) + out_text = sc.model_dump_json(indent=2) + if out is not None: + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(out_text + "\n") + console.print(f"[green]wrote[/green] {out}") + else: + console.print(out_text) + + if append_history: + path = _hist_append( + sc, + run_id=rec.model_id, + model_id=rec.model_id, + model_sha256=rec.model_sha256, + promoted=False, + ) + console.print(f"[dim]history appended → {path}[/dim]") + + # Composite headline for terminal-friendly reading. + console.print( + f"[bold]MEI[/bold] = [bold cyan]{sc.composite:.3f}[/bold cyan] " + f"Q={sc.quality:.3f} Dt={sc.decode_throughput:.3f} " + f"Pp={sc.prefill_throughput:.3f} M={sc.memory:.3f} E={sc.energy:.3f}" + + (" [yellow](provisional Agentic)[/yellow]" if sc.mab_provisional else ""), + ) + # Promotion preview (against the current ledger). + from mindxtrain.eval.mei.history import currently_promoted + from mindxtrain.eval.mei.score import is_promotable + prior = currently_promoted() + prior_score = prior.score if prior is not None else None + ok, reasons = is_promotable(sc, prior_promoted=prior_score) + if ok: + console.print("[green]✓ promotable[/green] — eligible for AgenticPlace.") + else: + console.print("[yellow]✗ not promotable[/yellow]:") + for r in reasons: + console.print(f" • {r}") + + +@mei_app.command("history") +def mei_history( + last: int = typer.Option(10, "--last", "-n", help="Show the last N entries."), + promoted_only: bool = typer.Option( + False, "--promoted-only", help="Filter to entries promoted to AgenticPlace.", + ), +) -> None: + """List recent MEI scores from the historical ledger.""" + from mindxtrain.eval.mei.history import read_all + + rows = read_all() + if promoted_only: + rows = [r for r in rows if r.promoted] + rows = rows[-last:] if last > 0 else rows + if not rows: + console.print("[dim](no MEI history yet — run `mindxtrain mei score …`)[/dim]") + return + for r in rows: + mark = "[green]★[/green]" if r.promoted else "·" + flag = " [yellow](prov)[/yellow]" if r.score.mab_provisional else "" + console.print( + f"{mark} {r.timestamp} {r.model_id} " + f"MEI={r.score.composite:.3f}{flag}", + ) + + +if __name__ == "__main__": + app() diff --git a/mindxtrain/config/__init__.py b/mindxtrain/config/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e42b1f0e9e30947cacd0a675c27e283135a7ed38 --- /dev/null +++ b/mindxtrain/config/__init__.py @@ -0,0 +1,35 @@ +from mindxtrain.config.loader import list_recipes, load_config, render_recipe +from mindxtrain.config.schema import ( + AutotuneCfg, + DataCfg, + EvalCfg, + HardwareCfg, + LoraMethod, + MetaCfg, + ModelCfg, + PublishCfg, + QuantizeCfg, + ReceiptCfg, + ServeCfg, + TrainCfg, + XTrainConfig, +) + +__all__ = [ + "AutotuneCfg", + "DataCfg", + "EvalCfg", + "HardwareCfg", + "LoraMethod", + "MetaCfg", + "ModelCfg", + "PublishCfg", + "QuantizeCfg", + "ReceiptCfg", + "ServeCfg", + "TrainCfg", + "XTrainConfig", + "list_recipes", + "load_config", + "render_recipe", +] diff --git a/mindxtrain/config/deploy_default.json b/mindxtrain/config/deploy_default.json new file mode 100644 index 0000000000000000000000000000000000000000..1231e1ce33e5119985057ee3714fd49d8fb827d1 --- /dev/null +++ b/mindxtrain/config/deploy_default.json @@ -0,0 +1,13 @@ +{ + "$schema": "mindxtrain.deploy", + "_comment": "Runtime defaults for `mindxtrain serve` / `deploy`; ${ENV} interpolation per ml-intern style.", + "backend": "${MINDXTRAIN_SERVE_BACKEND:-vllm}", + "host": "${MINDXTRAIN_HOST:-0.0.0.0}", + "port": "${MINDXTRAIN_PORT:-8000}", + "tensor_parallel": "${MINDXTRAIN_TP:-1}", + "max_model_len": "${MINDXTRAIN_MAX_MODEL_LEN:-32768}", + "ab_test": { + "canary_pct": 0.0 + }, + "registry_path": "${MINDXTRAIN_REGISTRY:-./out/registry.json}" +} diff --git a/mindxtrain/config/eval_default.json b/mindxtrain/config/eval_default.json new file mode 100644 index 0000000000000000000000000000000000000000..4d82e636eaa89ed10986e2054a42c52611acbdce --- /dev/null +++ b/mindxtrain/config/eval_default.json @@ -0,0 +1,12 @@ +{ + "$schema": "mindxtrain.eval", + "_comment": "Runtime defaults for `mindxtrain eval`; ${ENV} interpolation per ml-intern style.", + "tasks": [ + "lighteval/mmlu", + "lighteval/gsm8k", + "bfcl/v4_simple" + ], + "out_dir": "${MINDXTRAIN_OUT_DIR:-./out/eval}", + "persona_baseline": "${MINDXTRAIN_PERSONA_BASELINE:-}", + "agenda": "${MINDXTRAIN_AGENDA:-}" +} diff --git a/mindxtrain/config/loader.py b/mindxtrain/config/loader.py new file mode 100644 index 0000000000000000000000000000000000000000..241881f7556989766b6f368eb83264daf0894e2f --- /dev/null +++ b/mindxtrain/config/loader.py @@ -0,0 +1,43 @@ +"""YAML <-> XTrainConfig with recipe-name resolution. + +Recipes live in `mindxtrain/train/recipes/*.yaml`. +""" + +from __future__ import annotations + +from importlib import resources +from pathlib import Path + +import yaml + +from mindxtrain.config.schema import XTrainConfig + + +def load_config(path: str | Path) -> XTrainConfig: + """Load a YAML config file from disk and validate against XTrainConfig.""" + raw = yaml.safe_load(Path(path).read_text()) + return XTrainConfig.model_validate(raw) + + +def render_recipe(name: str) -> str: + """Return the YAML text for a named recipe (e.g. `qwen3_8b_sft_lora`).""" + pkg = resources.files("mindxtrain.train.recipes") + candidate = pkg / f"{name}.yaml" + if not candidate.is_file(): + available = sorted( + p.name.removesuffix(".yaml") + for p in pkg.iterdir() + if p.name.endswith(".yaml") + ) + msg = f"unknown recipe {name!r}. available: {', '.join(available)}" + raise FileNotFoundError(msg) + return candidate.read_text() + + +def list_recipes() -> list[str]: + pkg = resources.files("mindxtrain.train.recipes") + return sorted( + p.name.removesuffix(".yaml") + for p in pkg.iterdir() + if p.name.endswith(".yaml") + ) diff --git a/mindxtrain/config/schema.py b/mindxtrain/config/schema.py new file mode 100644 index 0000000000000000000000000000000000000000..805809c7f47e2fd254fac666bfdd7e80775c4592 --- /dev/null +++ b/mindxtrain/config/schema.py @@ -0,0 +1,568 @@ +"""Pydantic v2 config schema — canonical mindXtrain YAML. + +Mirrors the blueprint at docs/blueprints/mindXtrain_ Production Blueprint +for the AMD and lablab.ai Hackathon.md, sections "mindXtrain architecture" +and "Critical code snippets / examples/demo_qwen3_8b_sft.yaml". + +Top-level sections: + meta — project / run identity / seed / license + hardware — gpu name + gfx arch + count + HBM + autotune — 60s probe policy (AOT-only — JIT autotune forbidden) + model — base model + attention impl + dtype + data — HF/local dataset + dedupe + sharding + train — backend + method (LoRA/QLoRA/DPO/GRPO/...) + optimizer + env + eval — lm-evaluation-harness + regression detector + quantize — Quark FP8 / MXFP4 / GPTQ-ROCm + serve — vLLM-ROCm / SGLang + reasoning + tool-call parsers + publish — HF + Lighthouse + mindX + AgenticPlace + BANKON + x402 + receipt — provenance manifest output +""" + +from __future__ import annotations + +from pathlib import Path +from typing import Annotated, Literal + +from pydantic import BaseModel, ConfigDict, Discriminator, Field, model_validator + +# ---- enums / literals ------------------------------------------------------- + +# gfx9xx = CDNA datacenter (MI-series); gfx10xx/11xx = RDNA consumer Radeon; +# gfx90c = integrated Vega APU (descriptive only — the in-process `trl_local` +# lane never injects the MI300X env vars, so a consumer arch is just metadata). +GfxArch = Literal[ + "gfx900", "gfx90c", "gfx942", "gfx950", + "gfx1030", "gfx1100", "gfx1101", "gfx1102", "gfx1103", +] +HardwareName = Literal["mi300x", "mi325x", "mi350x", "mi355x", "consumer_gpu", "local"] +TrainingBackend = Literal["axolotl", "unsloth", "torchtune", "primus", "trl_cpu", "trl_local"] +DType = Literal["bfloat16", "float16", "float32", "fp8_e4m3", "mxfp4"] +AttentionBackend = Literal["ck", "triton", "aiter"] +AttnImplementation = Literal["flash_attention_2", "sdpa", "eager"] +DataSource = Literal["hf", "local", "lighthouse", "mindx_dreams"] +QuantScheme = Literal["quark_fp8", "quark_mxfp4", "gptq_rocm", "none"] +ServeBackend = Literal["vllm-rocm", "sglang"] +ReasoningParser = Literal["deepseek_r1", "qwen3", "none"] +ToolCallParser = Literal["hermes", "qwen3_coder", "none"] +X402Network = Literal["algorand", "base", "base-sepolia"] +ScheduleType = Literal["cosine", "linear", "constant", "wsd"] +OptimizerName = Literal["adamw_torch_fused", "adamw_torch", "adamw_8bit", "lion", "adafactor"] +ReceiptIncludeKey = Literal[ + "rocm_version", + "gfx_arch", + "container_digest", + "all_git_shas", + "yaml_hash", + "dataset_cids", + "eval_report", + "energy_kwh", +] + +_DEFAULT_RECEIPT_INCLUDE: list[ReceiptIncludeKey] = [ + "rocm_version", + "gfx_arch", + "container_digest", + "all_git_shas", + "yaml_hash", + "dataset_cids", + "eval_report", + "energy_kwh", +] + + +# ---- meta ------------------------------------------------------------------- + +class MetaCfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + project: str = Field(min_length=1) + run_name: str = Field(min_length=1) + seed: int = Field(default=2048, ge=0) + license: str = Field(default="apache-2.0") + description: str = Field(default="") + + +# ---- hardware --------------------------------------------------------------- + +class HardwareCfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + name: HardwareName = "mi300x" + gfx_arch: GfxArch = "gfx942" + gpus: Literal[0, 1, 8] = Field( + default=1, + description=( + "0 = CPU lane (mindX self-training, smoke runs). " + "1 or 8 = MI300X. 2/4 rejected: xGMI bandwidth is asymmetric." + ), + ) + # MI300X is 192 GB HBM3; consumer Radeon/RTX cards are 8-24 GB, so the floor + # is low enough for the `trl_local` lane to declare real VRAM honestly. + expected_hbm_gb: int = Field(default=192, ge=8) + + +# ---- autotune --------------------------------------------------------------- + +class AutotuneCfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + enabled: bool = True + plan_path: Path = Field(default=Path("./out/mindxtrain.tuned.yaml")) + budget_seconds: int = Field(default=60, ge=10, le=600) + policy: Literal["aot_only"] = Field( + default="aot_only", + description="AOT-only — JIT autotune is forbidden in production.", + ) + + +# ---- model ------------------------------------------------------------------ + +class ModelCfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + name: str = Field(description="HF Hub model ID, e.g. Qwen/Qwen3-8B") + revision: str | None = None + attn_implementation: AttnImplementation = "flash_attention_2" + torch_dtype: DType = "bfloat16" + trust_remote_code: bool = False + + +# ---- data ------------------------------------------------------------------- + +class MinHashCfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + threshold: float = Field(default=0.85, ge=0.0, le=1.0) + + +class SemDedupCfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + threshold: float = Field(default=0.95, ge=0.0, le=1.0) + model: str = "sentence-transformers/all-MiniLM-L6-v2" + + +class DedupeCfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + minhash: MinHashCfg | None = None + semdedup: SemDedupCfg | None = None + + +class ShardCfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + num_shards: int = Field(default=1, ge=1) + + +class DataCfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + source: DataSource = "hf" + hf_id: str = Field( + default="", + description="HF dataset ID (e.g. tatsu-lab/alpaca). Required when source='hf'.", + ) + path: Path | None = Field( + default=None, + description=( + "Filesystem root. Required when source='local' or 'mindx_dreams'. " + "For mindx_dreams: the mindX `data/memory` directory." + ), + ) + split: str = "train" + streaming: bool = True + max_samples: int | None = Field(default=None, ge=1) + seq_len: int = Field(default=4096, ge=64, le=1_048_576) + packing: bool = True + dedupe: DedupeCfg = Field(default_factory=DedupeCfg) + shard: ShardCfg = Field(default_factory=ShardCfg) + include_evolutions: bool = Field( + default=False, + description=( + "When source='mindx_dreams', also pull *_evolutions.jsonl " + "proposals alongside *_training.jsonl consolidation rows. " + "max_samples caps the combined total." + ), + ) + eval_split: float | None = Field( + default=None, + ge=0.05, + le=0.5, + description=( + "Fraction of the tokenised dataset to hold out for validation. " + "When set, the trl_cpu lane passes the held-out slice as " + "`eval_dataset` to SFTTrainer and emits eval_loss every " + "max_steps//4 steps so the Coach loss chart can plot a " + "second series. None disables eval entirely (default — keeps " + "the legacy single-loss behaviour)." + ), + ) + + @model_validator(mode="after") + def _check_source_inputs(self) -> DataCfg: + if self.source == "hf" and not self.hf_id: + msg = "data.hf_id is required when data.source='hf'" + raise ValueError(msg) + if self.source in {"local", "mindx_dreams"} and self.path is None: + msg = f"data.path is required when data.source='{self.source}'" + raise ValueError(msg) + return self + + +# ---- train (discriminated method) ------------------------------------------ + +class _MethodBase(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + +class FullMethod(_MethodBase): + kind: Literal["full"] = "full" + + +class LoraMethod(_MethodBase): + kind: Literal["lora"] = "lora" + r: int = Field(default=16, ge=1, le=512) + alpha: int = Field(default=32, ge=1, le=1024) + dropout: float = Field(default=0.0, ge=0.0, le=1.0) + target_modules: list[str] = Field( + default_factory=lambda: [ + "q_proj", "k_proj", "v_proj", "o_proj", + "gate_proj", "up_proj", "down_proj", + ], + ) + + +class QLoraMethod(_MethodBase): + kind: Literal["qlora"] = "qlora" + r: int = Field(default=16, ge=1, le=512) + alpha: int = Field(default=32, ge=1, le=1024) + dropout: float = Field(default=0.0, ge=0.0, le=1.0) + quant_bits: Literal[4, 8] = 4 + target_modules: list[str] = Field( + default_factory=lambda: ["q_proj", "k_proj", "v_proj", "o_proj"], + ) + + +class DpoMethod(_MethodBase): + kind: Literal["dpo"] = "dpo" + beta: float = Field(default=0.1, gt=0.0) + + +class OrpoMethod(_MethodBase): + kind: Literal["orpo"] = "orpo" + beta: float = Field(default=0.1, gt=0.0) + + +class GrpoMethod(_MethodBase): + kind: Literal["grpo"] = "grpo" + num_generations: int = Field(default=4, ge=2) + kl_coef: float = Field(default=0.04, ge=0.0) + + +class GspoMethod(_MethodBase): + """Qwen team's preferred RL algorithm for hybrid + sparse MoE stability (Qwen3-Next/3.5/3.6).""" + + kind: Literal["gspo"] = "gspo" + num_generations: int = Field(default=4, ge=2) + + +class KtoMethod(_MethodBase): + kind: Literal["kto"] = "kto" + beta: float = Field(default=0.1, gt=0.0) + + +class CptMethod(_MethodBase): + kind: Literal["cpt"] = "cpt" + + +TrainMethod = Annotated[ + FullMethod | LoraMethod | QLoraMethod | DpoMethod | OrpoMethod + | GrpoMethod | GspoMethod | KtoMethod | CptMethod, + Discriminator("kind"), +] + + +class OptimizerCfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + name: OptimizerName = "adamw_torch_fused" + lr: float = Field(default=1e-4, gt=0.0) + betas: tuple[float, float] = (0.9, 0.95) + weight_decay: float = Field(default=0.1, ge=0.0) + grad_clip: float = Field(default=1.0, ge=0.0) + + +class ScheduleCfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + type: ScheduleType = "cosine" + warmup_ratio: float = Field(default=0.03, ge=0.0, le=1.0) + epochs: int = Field(default=3, ge=1, le=100) + + +class BatchCfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + per_device: int = Field(default=8, ge=1) + grad_accum: int = Field(default=4, ge=1) + + +class FlashAttentionCfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + backend: AttentionBackend = "ck" + + +class FsdpCfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + enabled: bool = False + auto_wrap: bool = True + + +class CPUThrottleCfg(BaseModel): + """CPU throttle for the `trl_cpu` lane — keeps the dev laptop usable. + + `percent` is the fraction of available cores to use during training. + Resolved to an integer thread count via `resolve_thread_count`; thread + counts under 1 are clamped to 1 so training never deadlocks on a zero + thread pool. + + `nice_level` shifts the training process's scheduler priority — useful + on a single-user laptop where you want to keep the UI responsive while + training runs in the background. 0 = default, 10 = visibly de- + prioritized but still progresses. + + `omp_proc_bind` enables Ryzen-aware OpenMP affinity (`OMP_PROC_BIND=close`, + `OMP_PLACES=cores`). Helps on CCX-topology CPUs by keeping threads on + the same chiplet — reduces inter-core latency for the small matmuls + that dominate CPU training. + """ + + model_config = ConfigDict(extra="forbid", frozen=True) + + percent: int = Field( + default=100, + ge=1, + le=100, + description=( + "Percent of available cores to use during CPU training. " + "100 = full host (default; matches the prior behaviour). " + "50 = half the cores. 1 = single-thread (slowest)." + ), + ) + nice_level: int = Field( + default=0, + ge=-20, + le=19, + description=( + "POSIX nice level for the training process. 0 = default. " + "Positive values de-prioritise the trainer so a desktop UI " + "stays responsive during training. Requires CAP_SYS_NICE for " + "negative values (root) — schema accepts but `os.nice` will " + "raise if unauthorised." + ), + ) + omp_proc_bind: bool = Field( + default=True, + description=( + "When True, sets OMP_PROC_BIND=close + OMP_PLACES=cores so " + "OpenMP threads bind to the same CCX chiplet on Ryzen / EPYC. " + "Improves cache locality for the small matmuls that dominate " + "CPU training; safe to leave on for non-AMD CPUs too." + ), + ) + + +def resolve_thread_count(percent: int, total_cores: int) -> int: + """Map a percentage [1, 100] to an integer thread count. + + Floor at 1 — training can never run on zero threads. Ceiling at + `total_cores` (passing percent=100 on a 4-core box returns 4, never + 5). Used by the trl_cpu backend to size torch / OpenMP / BLAS thread + pools uniformly. + """ + if total_cores < 1: + msg = f"total_cores must be ≥ 1; got {total_cores}" + raise ValueError(msg) + if not 1 <= percent <= 100: + msg = f"percent must be in [1, 100]; got {percent}" + raise ValueError(msg) + threads = (total_cores * percent) // 100 + return max(1, min(total_cores, threads)) + + +class TrainCfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + backend: TrainingBackend = "axolotl" + method: TrainMethod = Field(default_factory=lambda: LoraMethod()) + optimizer: OptimizerCfg = Field(default_factory=OptimizerCfg) + schedule: ScheduleCfg = Field(default_factory=ScheduleCfg) + batch: BatchCfg = Field(default_factory=BatchCfg) + precision: DType = "bfloat16" + gradient_checkpointing: bool = True + flash_attention: FlashAttentionCfg = Field(default_factory=FlashAttentionCfg) + fsdp: FsdpCfg = Field(default_factory=FsdpCfg) + cpu_throttle: CPUThrottleCfg = Field(default_factory=CPUThrottleCfg) + logging_steps: int = Field( + default=1, + ge=1, + le=10_000, + description=( + "How often (in optimizer steps) HF Trainer should call its " + "on_log hooks. Default 1 — every step logs, giving the Coach " + "loss chart a per-step trajectory and trainer_state.json a " + "full log_history. The trl_cpu backend floors this at " + "max(1, min(logging_steps, max_steps // 4)) so short runs " + "still get at least 4 data points even if the YAML sets a " + "higher cadence." + ), + ) + env: dict[str, str] = Field( + default_factory=lambda: { + "HSA_NO_SCRATCH_RECLAIM": "1", + "NVTE_CK_USES_BWD_V3": "1", + "NVTE_CK_IS_V3_ATOMIC_FP32": "1", + "PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32": "1", + "NCCL_MIN_NCHANNELS": "112", + "HIP_FORCE_DEV_KERNARG": "1", + "PYTORCH_ROCM_ARCH": "gfx942", + }, + ) + + +# ---- eval ------------------------------------------------------------------- + +class EvalHarnessCfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + tasks: list[str] = Field(default_factory=lambda: ["mmlu", "gsm8k", "ifeval", "humaneval"]) + fewshot: int = Field(default=5, ge=0) + + +class EvalRegressionCfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + baseline: str = Field(default="", description="HF model ID of the base model for regression check") + threshold_pct: float = Field( + default=-1.0, + description="fail if any task drops more than threshold_pct (negative = allowed drop)", + ) + + +class EvalCfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + harness: EvalHarnessCfg = Field(default_factory=EvalHarnessCfg) + regression: EvalRegressionCfg = Field(default_factory=EvalRegressionCfg) + + +# ---- quantize --------------------------------------------------------------- + +class QuantizeCfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + enabled: bool = True + scheme: QuantScheme = "quark_fp8" + ptpc: bool = Field( + default=True, + description="Per-tensor-per-channel FP8 GEMM (15-30% faster than BlockScale on MI300X).", + ) + + +# ---- serve ------------------------------------------------------------------ + +class ServeCfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + backend: ServeBackend = "vllm-rocm" + reasoning_parser: ReasoningParser = "qwen3" + tool_call_parser: ToolCallParser = "hermes" + tensor_parallel: int = Field(default=1, ge=1) + max_model_len: int = Field(default=8192, ge=512) + port: int = Field(default=8000, ge=1024, le=65535) + + +# ---- publish ---------------------------------------------------------------- + +class HfPublishCfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + repo: str = Field(description="HF Hub repo, e.g. lablab-ai-amd-developer-hackathon/mindxtrain-demo") + private: bool = False + + +class LighthousePublishCfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + api_key_env: str = "LIGHTHOUSE_API_KEY" + + +class MindxPublishCfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + api_url: str = "https://mindx.pythai.net/v1/agents" + register_as_capability: bool = True + + +class AgenticPlacePublishCfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + api_url: str = "https://agenticplace.pythai.net/v1/listings" + chain_map_url: str = "https://agenticplace.pythai.net/allchain.html" + + +class BankonPublishCfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + ens_parent: str = "bankon.eth" + subname: str = "" + + +class X402Cfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + network: X402Network = "algorand" + asset: str = "USDC" + receiver_via: Literal["parsec_wallet", "coinbase_facilitator"] = "parsec_wallet" + price_per_1k_tokens: float = Field(default=0.0002, ge=0.0) + + +class BillingPublishCfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + x402: X402Cfg = Field(default_factory=X402Cfg) + + +class PublishCfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + enabled: bool = True + hf: HfPublishCfg | None = None + lighthouse: LighthousePublishCfg = Field(default_factory=LighthousePublishCfg) + mindx: MindxPublishCfg = Field(default_factory=MindxPublishCfg) + agenticplace: AgenticPlacePublishCfg = Field(default_factory=AgenticPlacePublishCfg) + bankon: BankonPublishCfg = Field(default_factory=BankonPublishCfg) + billing: BillingPublishCfg = Field(default_factory=BillingPublishCfg) + + +# ---- receipt ---------------------------------------------------------------- + +class ReceiptCfg(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + output: Path = Field(default=Path("./out/receipt.json")) + include: list[ReceiptIncludeKey] = Field( + default_factory=lambda: _DEFAULT_RECEIPT_INCLUDE.copy(), + ) + + +# ---- root ------------------------------------------------------------------- + +class XTrainConfig(BaseModel): + """Canonical mindXtrain YAML — consumed by `mindxtrain` CLI verbs.""" + + model_config = ConfigDict(extra="forbid", frozen=True) + + meta: MetaCfg + hardware: HardwareCfg = Field(default_factory=HardwareCfg) + autotune: AutotuneCfg = Field(default_factory=AutotuneCfg) + model: ModelCfg + data: DataCfg + train: TrainCfg = Field(default_factory=lambda: TrainCfg()) + eval: EvalCfg = Field(default_factory=EvalCfg) + quantize: QuantizeCfg = Field(default_factory=QuantizeCfg) + serve: ServeCfg = Field(default_factory=ServeCfg) + publish: PublishCfg = Field(default_factory=PublishCfg) + receipt: ReceiptCfg = Field(default_factory=ReceiptCfg) diff --git a/mindxtrain/config/train_default.json b/mindxtrain/config/train_default.json new file mode 100644 index 0000000000000000000000000000000000000000..a5ef3ec23cd124c31fcd1cdea3670805b7d0beaf --- /dev/null +++ b/mindxtrain/config/train_default.json @@ -0,0 +1,23 @@ +{ + "$schema": "mindxtrain.config.schema.XTrainConfig", + "_comment": "Runtime defaults for `mindxtrain train`; ${ENV} interpolation per ml-intern style.", + "meta": { + "project": "${MINDXTRAIN_PROJECT:-mindxtrain-default}", + "owner": "${MINDXTRAIN_OWNER:-anon}" + }, + "model": { + "name": "${MINDXTRAIN_BASE_MODEL:-Qwen/Qwen3.5-8B}", + "tokenizer": "${MINDXTRAIN_BASE_MODEL:-Qwen/Qwen3.5-8B}" + }, + "hardware": { + "gpus": "${MINDXTRAIN_GPUS:-1}", + "gfx_arch": "${MINDXTRAIN_GFX_ARCH:-gfx942}" + }, + "autotune": { + "policy": "aot_only" + }, + "publish": { + "storage_provider": "${MINDXTRAIN_STORAGE:-local_fs}", + "lighthouse_endpoint": "${LIGHTHOUSE_ENDPOINT:-https://node.lighthouse.storage}" + } +} diff --git a/mindxtrain/data/__init__.py b/mindxtrain/data/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/mindxtrain/data/curate.py b/mindxtrain/data/curate.py new file mode 100644 index 0000000000000000000000000000000000000000..c2b8bd80a05aebec87394497f92cbc60b7145d0f --- /dev/null +++ b/mindxtrain/data/curate.py @@ -0,0 +1,124 @@ +"""Dataset curation — dispatch on `DataCfg.source` to the right adapter. + +Three sources today: + +- `hf` (default) — `datasets.load_dataset(streaming=True)`, requires `--extra ml`. +- `local` — JSONL files under `cfg.path`, pure stdlib. +- `mindx_dreams` — mindX dream-cycle JSONL corpus under `cfg.path`, pure stdlib. + See `mindxtrain.data.sources.mindx_dreams`. + +The HF path stays lazy-import so consumers without `--extra ml` can still +read configs that target other sources. +""" + +from __future__ import annotations + +import json +from collections.abc import Iterator +from pathlib import Path +from typing import Any + +from mindxtrain.config.schema import DataCfg + + +def load_streaming_dataset(cfg: DataCfg) -> Iterator[dict[str, Any]]: + """Yield rows for the configured `DataCfg.source` one at a time.""" + if cfg.source == "mindx_dreams": + from mindxtrain.data.sources.mindx_dreams import ( + iter_mindx_dreams, + iter_mindx_evolutions, + ) + + assert cfg.path is not None # validated by DataCfg + # Two-stream yield with a SHARED budget so max_samples caps the + # combined corpus, not each stream individually. Consolidation rows + # come first (richer base signal), evolutions follow when opted in. + cap = cfg.max_samples + emitted = 0 + remaining = (cap - emitted) if cap is not None else None + for row in iter_mindx_dreams(cfg.path, max_samples=remaining): + yield row + emitted += 1 + if cap is not None and emitted >= cap: + return + if cfg.include_evolutions: + remaining = (cap - emitted) if cap is not None else None + try: + for row in iter_mindx_evolutions(cfg.path, max_samples=remaining): + yield row + emitted += 1 + if cap is not None and emitted >= cap: + return + except FileNotFoundError: + # mindx_dreams already succeeded above, so the root exists — + # this would only fire on a path race. Swallow silently. + pass + return + + if cfg.source == "local": + assert cfg.path is not None # validated by DataCfg + yield from _iter_local_jsonl(cfg.path, max_samples=cfg.max_samples) + return + + if cfg.source == "lighthouse": + msg = ( + "DataCfg.source='lighthouse' is reserved for shard-tar inputs; " + "use `mindxtrain.storage.lighthouse.fetch` to materialize them locally first, " + "then point a `local` source at the resulting directory." + ) + raise NotImplementedError(msg) + + # source == "hf" + try: + from datasets import load_dataset + except ImportError as exc: + msg = "datasets not installed; run `uv sync --extra ml`." + raise RuntimeError(msg) from exc + + kwargs: dict[str, Any] = {"streaming": cfg.streaming} + revision = getattr(cfg, "revision", None) + if revision: + kwargs["revision"] = revision + + ds = load_dataset(cfg.hf_id, split=cfg.split or "train", **kwargs) + emitted = 0 + for row in ds: + yield row + emitted += 1 + if cfg.max_samples is not None and emitted >= cfg.max_samples: + return + + +def _iter_local_jsonl( + path: Path, + *, + max_samples: int | None = None, +) -> Iterator[dict[str, Any]]: + """Walk *.jsonl under `path` and yield parsed rows. + + Skips lines that fail to parse — local datasets are often hand-assembled + and a single bad line shouldn't fail the run. + """ + path = Path(path).expanduser() + if path.is_file(): + files = [path] + else: + files = sorted(path.rglob("*.jsonl")) + emitted = 0 + for f in files: + with f.open("r", encoding="utf-8") as fh: + for line in fh: + line = line.strip() + if not line: + continue + try: + row = json.loads(line) + except json.JSONDecodeError: + continue + yield row + emitted += 1 + if max_samples is not None and emitted >= max_samples: + return + + +__all__ = ["load_streaming_dataset"] diff --git a/mindxtrain/data/dedupe.py b/mindxtrain/data/dedupe.py new file mode 100644 index 0000000000000000000000000000000000000000..77815e3c4b21ab38813ab4d4bb212b2833a2d5a3 --- /dev/null +++ b/mindxtrain/data/dedupe.py @@ -0,0 +1,96 @@ +"""Deduplication — MinHash near-duplicate detection + SemDeDup semantic similarity. + +Two complementary passes: + +- `dedupe_minhash`: datasketch.MinHashLSH on 5-gram char shingles. Cheap, + syntactic, removes verbatim near-duplicates. +- `dedupe_semdedup`: sentence-transformer embeddings + FAISS cosine. Drops + docs within `threshold` similarity of an earlier doc. + +Both lazy-import the heavyweight deps; `--extra data` enables them. +""" + +from __future__ import annotations + +from collections.abc import Iterable, Iterator + + +def _shingle(text: str, k: int = 5) -> list[str]: + text = text.lower() + if len(text) < k: + return [text] if text else [] + return [text[i : i + k] for i in range(len(text) - k + 1)] + + +def dedupe_minhash( + docs: Iterable[str], + threshold: float = 0.85, + *, + num_perm: int = 128, + shingle_k: int = 5, +) -> Iterator[str]: + """Yield docs that are not near-duplicates of an earlier doc.""" + try: + from datasketch import MinHash, MinHashLSH + except ImportError as exc: + msg = "datasketch not installed; run `uv sync --extra data`." + raise RuntimeError(msg) from exc + + lsh = MinHashLSH(threshold=threshold, num_perm=num_perm) + for idx, doc in enumerate(docs): + if not isinstance(doc, str) or not doc: + continue + m = MinHash(num_perm=num_perm) + for s in _shingle(doc, shingle_k): + m.update(s.encode("utf-8")) + if lsh.query(m): + continue + lsh.insert(f"doc-{idx}", m) + yield doc + + +def dedupe_semdedup( + docs: Iterable[str], + threshold: float = 0.95, + model: str = "sentence-transformers/all-MiniLM-L6-v2", + *, + batch_size: int = 64, +) -> Iterator[str]: + """Yield docs not within `threshold` cosine similarity of an earlier doc.""" + try: + import numpy as np + from sentence_transformers import SentenceTransformer + except ImportError as exc: + msg = "sentence-transformers + numpy not installed; run `uv sync --extra data`." + raise RuntimeError(msg) from exc + + encoder = SentenceTransformer(model) + seen_embeddings: list[np.ndarray] = [] # type: ignore[type-arg] + + buffer: list[str] = [] + + def _flush() -> Iterator[str]: + nonlocal buffer + if not buffer: + return + embs = encoder.encode(buffer, normalize_embeddings=True, batch_size=batch_size) + for txt, emb in zip(buffer, embs, strict=True): + if seen_embeddings: + stack = np.stack(seen_embeddings) + sims = stack @ emb + if float(sims.max()) >= threshold: + continue + seen_embeddings.append(emb) + yield txt + buffer = [] + + for doc in docs: + if not isinstance(doc, str) or not doc: + continue + buffer.append(doc) + if len(buffer) >= batch_size: + yield from _flush() + yield from _flush() + + +__all__ = ["dedupe_minhash", "dedupe_semdedup"] diff --git a/mindxtrain/data/filter.py b/mindxtrain/data/filter.py new file mode 100644 index 0000000000000000000000000000000000000000..fdb38a9f7e98bffd7c384db41c0a2736d7374e76 --- /dev/null +++ b/mindxtrain/data/filter.py @@ -0,0 +1,78 @@ +"""Quality filtering — pure-Python heuristics + optional KenLM perplexity. + +Cheap heuristics first (length, repetition, language-ish character class). +KenLM is optional; if not installed, the perplexity gate is skipped. +""" + +from __future__ import annotations + +import re +from collections.abc import Iterable, Iterator + +_WORD_RE = re.compile(r"\b\w+\b", re.UNICODE) + + +def _ngram_repeat_ratio(text: str, n: int = 5) -> float: + """Fraction of n-grams that are duplicates of an earlier n-gram.""" + tokens = _WORD_RE.findall(text.lower()) + if len(tokens) < n: + return 0.0 + grams = [" ".join(tokens[i : i + n]) for i in range(len(tokens) - n + 1)] + return 1.0 - (len(set(grams)) / len(grams)) + + +def _alpha_ratio(text: str) -> float: + """Fraction of characters that are alphabetic (rough language gate).""" + if not text: + return 0.0 + alpha = sum(1 for c in text if c.isalpha()) + return alpha / len(text) + + +def quality_filter( + docs: Iterable[str], + *, + min_words: int = 16, + max_words: int = 32_768, + max_repeat_ratio: float = 0.3, + min_alpha_ratio: float = 0.5, + max_perplexity: float | None = None, +) -> Iterator[str]: + """Yield docs that pass the heuristic gate. + + `max_perplexity` triggers a KenLM check if the lib is installed; ignored + silently otherwise. + """ + kenlm_model = None + if max_perplexity is not None: + try: + # Caller can override which model to load by setting MINDXTRAIN_KENLM_PATH; + # fall back to skipping the perplexity gate if no model is configured. + import os + + import kenlm + + path = os.environ.get("MINDXTRAIN_KENLM_PATH") + if path: + kenlm_model = kenlm.Model(path) + except ImportError: + kenlm_model = None + + for doc in docs: + if not isinstance(doc, str) or not doc: + continue + words = _WORD_RE.findall(doc) + if len(words) < min_words or len(words) > max_words: + continue + if _ngram_repeat_ratio(doc) > max_repeat_ratio: + continue + if _alpha_ratio(doc) < min_alpha_ratio: + continue + if kenlm_model is not None and max_perplexity is not None: + ppl = kenlm_model.perplexity(doc) + if ppl > max_perplexity: + continue + yield doc + + +__all__ = ["quality_filter"] diff --git a/mindxtrain/data/pack.py b/mindxtrain/data/pack.py new file mode 100644 index 0000000000000000000000000000000000000000..3061b1cdc9a47648fa19ef62eaf9c0c1d4025de1 --- /dev/null +++ b/mindxtrain/data/pack.py @@ -0,0 +1,87 @@ +"""Sequence packing + tar-shard emission. Pure stdlib. + +`pack_sequences`: greedy first-fit-decreasing into `seq_len` buckets, each +sequence emitted with its EOS-bounded segment boundaries. + +`emit_shards`: writes `.tar` shards under `out_dir/shard-{n:05d}.tar` with +JSONL members; returns `out_dir`. Caller pins the resulting tars via +`mindxtrain.storage.lighthouse` or `mindxtrain.storage.ipfs`. +""" + +from __future__ import annotations + +import io +import json +import tarfile +from collections.abc import Iterable, Iterator +from pathlib import Path + + +def pack_sequences( + token_streams: Iterable[list[int]], + seq_len: int, + *, + eos_id: int = 0, +) -> Iterator[list[int]]: + """Greedy first-fit pack of `token_streams` into `seq_len`-sized buckets. + + Each input sequence is appended verbatim followed by `eos_id`; if the + next sequence would exceed `seq_len`, the current bucket is emitted + (padded to `seq_len` with `eos_id`). + """ + bucket: list[int] = [] + for stream in token_streams: + s = [*list(stream), eos_id] + if len(s) > seq_len: + # Sequence is longer than the bucket — split into seq_len chunks. + for i in range(0, len(s), seq_len): + chunk = s[i : i + seq_len] + if len(chunk) < seq_len: + chunk = chunk + [eos_id] * (seq_len - len(chunk)) + yield chunk + continue + if len(bucket) + len(s) > seq_len: + # Pad and emit current bucket. + yield bucket + [eos_id] * (seq_len - len(bucket)) + bucket = [] + bucket.extend(s) + if bucket: + yield bucket + [eos_id] * (seq_len - len(bucket)) + + +def emit_shards( + sequences: Iterable[list[int]], + out_dir: Path, + *, + samples_per_shard: int = 1024, +) -> Path: + """Write `.tar` shards under `out_dir`, each containing JSONL members.""" + out_dir = Path(out_dir) + out_dir.mkdir(parents=True, exist_ok=True) + + shard_idx = 0 + in_shard: list[bytes] = [] + + def _flush() -> None: + nonlocal shard_idx, in_shard + if not in_shard: + return + path = out_dir / f"shard-{shard_idx:05d}.tar" + with tarfile.open(path, "w") as tf: + for j, payload in enumerate(in_shard): + info = tarfile.TarInfo(name=f"sample-{shard_idx:05d}-{j:06d}.json") + info.size = len(payload) + tf.addfile(info, io.BytesIO(payload)) + shard_idx += 1 + in_shard = [] + + for i, seq in enumerate(sequences): + in_shard.append(json.dumps({"input_ids": seq}).encode("utf-8")) + if (i + 1) % samples_per_shard == 0: + _flush() + _flush() + + return out_dir + + +__all__ = ["emit_shards", "pack_sequences"] diff --git a/mindxtrain/data/personas.py b/mindxtrain/data/personas.py new file mode 100644 index 0000000000000000000000000000000000000000..558e698a5e7efc5a821c78be18dc704fc4d5ff4c --- /dev/null +++ b/mindxtrain/data/personas.py @@ -0,0 +1,165 @@ +"""Built-in personas + toggleable skills for script authoring. + +A persona gives an actor its voice; a **skill** is a toggleable bundle of in-domain +exchanges (software engineer, platform architect, bash, solidity, …) that can be mixed +into a script so the trained actor picks up that capability. `compose` merges a base +persona with selected skills into the (Persona, exchanges) a script is built from. + +Pure stdlib + pydantic; base-install importable. +""" + +from __future__ import annotations + +from mindxtrain.data.scripts import Exchange, Persona + +# ---- built-in personas ------------------------------------------------------ + +BUILTIN_PERSONAS: dict[str, Persona] = { + "codephreak": Persona( + name="Codephreak", + system_prompt=( + "You are Codephreak, an augmentic intelligence orchestrator. You are precise, " + "terse, and pragmatic. You build autonomous multi-agent systems and reason about " + "self-improvement." + ), + voice_examples=["augmentic intelligence orchestration.", "let's build, together."], + ), + "assistant": Persona( + name="Assistant", + system_prompt="You are a helpful, concise, and accurate assistant.", + ), + "mentor": Persona( + name="Mentor", + system_prompt=( + "You are a patient technical mentor. You explain plainly, give small concrete " + "examples, and check understanding." + ), + ), +} + +# ---- skills (toggleable exchange bundles) ----------------------------------- + + +class Skill: + """A named capability: a system-prompt addendum + representative exchanges.""" + + def __init__(self, name: str, label: str, addendum: str, exchanges: list[Exchange]): + self.name = name + self.label = label + self.addendum = addendum + self.exchanges = exchanges + + +SKILLS: dict[str, Skill] = { + "software_engineer": Skill( + "software_engineer", "Software Engineer", + "You write clean, tested, idiomatic code and explain trade-offs.", + [ + Exchange(user="How do you approach a new feature?", + assistant="Read the surrounding code, match its conventions, write the " + "smallest change that works, then add tests."), + Exchange(user="Reverse a linked list in Python.", + assistant="prev=None\nwhile head:\n head.next, prev, head = prev, head, head.next\nreturn prev"), + Exchange(user="What makes a good commit?", + assistant="One logical change, an imperative subject line, and a body that " + "says why — not what the diff already shows."), + ], + ), + "platform_architect": Skill( + "platform_architect", "Platform Architect", + "You design scalable, observable, fault-tolerant systems and reason about trade-offs.", + [ + Exchange(user="How do you scale a write-heavy service?", + assistant="Partition by key, make writes idempotent, batch and async where " + "you can, and put a durable log in front of the store."), + Exchange(user="Monolith or microservices?", + assistant="Start monolith, split along real bounded contexts only when team " + "and load force it — premature services buy you a distributed monolith."), + Exchange(user="What's the first thing you add to a new service?", + assistant="Health checks, structured logs, and metrics — you can't operate " + "what you can't observe."), + ], + ), + "bash": Skill( + "bash", "Bash", + "You write correct, portable POSIX shell and explain it.", + [ + Exchange(user="Find and delete files older than 7 days.", + assistant="find . -type f -mtime +7 -delete"), + Exchange(user="Count lines across all .py files.", + assistant="find . -name '*.py' -print0 | xargs -0 wc -l | tail -1"), + Exchange(user="Why quote variables in bash?", + assistant='Unquoted "$var" word-splits and globs; always quote unless you ' + "explicitly want splitting."), + ], + ), + "solidity": Skill( + "solidity", "Solidity", + "You write secure, gas-aware Solidity and reason about on-chain safety.", + [ + Exchange(user="How do you prevent reentrancy?", + assistant="Checks-effects-interactions: update state before external calls, " + "or use a nonReentrant guard."), + Exchange(user="Why use custom errors over require strings?", + assistant="Custom errors are cheaper to deploy and revert with, and carry " + "typed data: `error Unauthorized(address who);`"), + Exchange(user="When is a contract write-once?", + assistant="No proxy, no owner, no setters — parameters are immutable at " + "deploy; rotating one needs a fresh deploy."), + ], + ), +} + + +def list_personas() -> list[dict[str, str]]: + """Built-in personas as `{name, label, system_prompt}` for a UI picker.""" + return [ + {"name": key, "label": p.name, "system_prompt": p.system_prompt} + for key, p in BUILTIN_PERSONAS.items() + ] + + +def list_skills() -> list[dict[str, str]]: + """Available skills as `{name, label, addendum}` for toggle UI.""" + return [{"name": s.name, "label": s.label, "addendum": s.addendum} for s in SKILLS.values()] + + +def get_persona(name: str) -> Persona: + """Look up a built-in persona by key; falls back to a generic assistant.""" + return BUILTIN_PERSONAS.get(name, BUILTIN_PERSONAS["assistant"]) + + +def compose( + persona: str | Persona, + skills: list[str] | None = None, +) -> tuple[Persona, list[Exchange]]: + """Merge a base persona with selected skills. + + Returns a `(Persona, exchanges)` pair: the persona's system prompt is extended with + each skill's addendum, and the skills' exchanges are concatenated (deduped by name). + Unknown skill names are ignored. + """ + base = persona if isinstance(persona, Persona) else get_persona(persona) + chosen = [SKILLS[s] for s in (skills or []) if s in SKILLS] + + system = base.system_prompt + if chosen: + addenda = " ".join(s.addendum for s in chosen) + system = f"{system} {addenda}".strip() + + composed = base.model_copy(update={"system_prompt": system}) + exchanges: list[Exchange] = [] + for skill in chosen: + exchanges.extend(skill.exchanges) + return composed, exchanges + + +__all__ = [ + "BUILTIN_PERSONAS", + "SKILLS", + "Skill", + "compose", + "get_persona", + "list_personas", + "list_skills", +] diff --git a/mindxtrain/data/scripts.py b/mindxtrain/data/scripts.py new file mode 100644 index 0000000000000000000000000000000000000000..bea076a53cea10daa7cd6912e6b40ad71857c1a1 --- /dev/null +++ b/mindxtrain/data/scripts.py @@ -0,0 +1,207 @@ +"""Author a training *script* for an *actor*. + +The mindXtrain mental model: a **model is an actor**; an actor has a **persona** +(identity / voice) and a **script** (the training examples — the "impression" left +on the actor when it trains). This module is the clean-room primitive for building +a script from a persona + a handful of exchanges, written as the OpenAI-chat JSONL +that `data.source: local` ingests (`{"messages": [{role, content}, ...]}`). + +Clean-room: the Codephreak persona is *loaded* at runtime from +`MINDXTRAIN_PERSONA_PATH` (or a caller-supplied path); we never copy mindX bytes — +we read recognised fields and ignore the rest. + +Pure stdlib + pydantic; importable on a base install (no `--extra ml`). +""" + +from __future__ import annotations + +import json +import os +from pathlib import Path + +from pydantic import BaseModel, ConfigDict, Field + +# Recognised keys for a persona's identity/voice in an mindX-style persona JSON. +# We map these defensively — any other keys are ignored (clean-room read). +_NAME_KEYS = ("name", "persona", "id", "title") +_SYSTEM_KEYS = ("system_prompt", "system", "description", "bio", "summary", "prompt") +_VOICE_KEYS = ("voice_examples", "examples", "utterances", "samples", "voice") + + +class Persona(BaseModel): + """The identity to imprint onto an actor.""" + + model_config = ConfigDict(extra="forbid", frozen=True) + + name: str = "actor" + system_prompt: str = "" + voice_examples: list[str] = Field( + default_factory=list, + description="Example in-voice utterances; seed rows + the imprint baseline.", + ) + + +class Exchange(BaseModel): + """One user→assistant turn in a script.""" + + model_config = ConfigDict(extra="forbid", frozen=True) + + user: str + assistant: str + + +def load_persona(path: str | Path | None = None) -> Persona: + """Load a persona, clean-room, from JSON. + + Resolution: explicit `path` → `MINDXTRAIN_PERSONA_PATH` → a built-in minimal + default. Reads only recognised fields; unknown keys are ignored so an + arbitrary mindX persona file maps cleanly without copying its schema. + """ + resolved = path or os.environ.get("MINDXTRAIN_PERSONA_PATH") + if not resolved: + return _default_persona() + p = Path(resolved).expanduser() + if not p.is_file(): + return _default_persona() + try: + raw = json.loads(p.read_text()) + except (json.JSONDecodeError, OSError): + return _default_persona() + if not isinstance(raw, dict): + return _default_persona() + return persona_from_dict(raw) + + +def persona_from_dict(raw: dict) -> Persona: + """Build a Persona from a loosely-shaped dict (recognised keys only).""" + name = next((str(raw[k]) for k in _NAME_KEYS if raw.get(k)), "actor") + system = next((str(raw[k]) for k in _SYSTEM_KEYS if raw.get(k)), "") + voice: list[str] = [] + for k in _VOICE_KEYS: + v = raw.get(k) + if isinstance(v, list): + voice.extend(str(x) for x in v if isinstance(x, (str, int, float))) + elif isinstance(v, str): + voice.append(v) + return Persona(name=name, system_prompt=system, voice_examples=voice) + + +def _default_persona() -> Persona: + return Persona( + name="actor", + system_prompt="You are a helpful, concise assistant.", + voice_examples=[], + ) + + +def persona_system_prompt(persona: Persona) -> str: + """The system message that fronts every row of the script. + + Uses the persona's own system prompt when present, otherwise synthesises a + minimal one from the name so the actor still has an identity to imprint. + """ + if persona.system_prompt.strip(): + return persona.system_prompt.strip() + return f"You are {persona.name}. Stay in character and answer in your own voice." + + +def build_script_rows( + persona: Persona, + exchanges: list[Exchange], + *, + seed_voice: bool = True, +) -> list[dict]: + """Turn a persona + exchanges into OpenAI-chat rows for `source: local`. + + Each row carries the persona system prompt + one user→assistant turn. When + `seed_voice` is set, the persona's voice examples are added as extra + assistant-only demonstrations so a tiny model has voice to imprint even from + very few exchanges. + """ + system = persona_system_prompt(persona) + rows: list[dict] = [] + for ex in exchanges: + rows.append( + { + "messages": [ + {"role": "system", "content": system}, + {"role": "user", "content": ex.user}, + {"role": "assistant", "content": ex.assistant}, + ], + }, + ) + if seed_voice: + for sample in persona.voice_examples: + rows.append( + { + "messages": [ + {"role": "system", "content": system}, + {"role": "user", "content": f"Say something as {persona.name}."}, + {"role": "assistant", "content": sample}, + ], + }, + ) + return rows + + +def write_script_jsonl(rows: list[dict], out_path: str | Path) -> Path: + """Write script rows as JSONL; returns the path. Parent dirs are created.""" + out = Path(out_path).expanduser() + out.parent.mkdir(parents=True, exist_ok=True) + with out.open("w", encoding="utf-8") as fh: + for row in rows: + fh.write(json.dumps(row, ensure_ascii=False) + "\n") + return out + + +def derive_training_params(num_rows: int) -> dict[str, int]: + """Derive CPU-imprint training params from the dataset size. + + A small persona/skill script must *overfit* to imprint (many epochs, grad_accum 1 + so a few-row script still does many optimizer steps); larger datasets taper toward + ordinary SFT. Returns `{epochs, grad_accum, per_device}` the imprint lane can apply. + """ + n = max(1, num_rows) + if n <= 8: + epochs, grad_accum = 24, 1 + elif n <= 32: + epochs, grad_accum = 16, 1 + elif n <= 128: + epochs, grad_accum = 8, 1 + elif n <= 512: + epochs, grad_accum = 4, 2 + else: + epochs, grad_accum = 2, 4 + return {"epochs": epochs, "grad_accum": grad_accum, "per_device": 1} + + +def author_script( + *, + out_path: str | Path, + exchanges: list[Exchange], + persona: Persona | None = None, + persona_path: str | Path | None = None, + seed_voice: bool = True, +) -> tuple[Path, int]: + """One-call script authoring: persona + exchanges → JSONL on disk. + + Returns (path, row_count). The persona is taken as-given, else loaded + clean-room from `persona_path` / `MINDXTRAIN_PERSONA_PATH` / the default. + """ + actor_persona = persona or load_persona(persona_path) + rows = build_script_rows(actor_persona, exchanges, seed_voice=seed_voice) + path = write_script_jsonl(rows, out_path) + return path, len(rows) + + +__all__ = [ + "Exchange", + "Persona", + "author_script", + "build_script_rows", + "derive_training_params", + "load_persona", + "persona_from_dict", + "persona_system_prompt", + "write_script_jsonl", +] diff --git a/mindxtrain/data/sources/__init__.py b/mindxtrain/data/sources/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2e8fdbc909962ecc8662a9244cd6813fe2561252 --- /dev/null +++ b/mindxtrain/data/sources/__init__.py @@ -0,0 +1,14 @@ +"""Dataset source adapters. + +`load_streaming_dataset` (`mindxtrain.data.curate`) dispatches to one of these +based on `DataCfg.source`. Each adapter yields rows in the canonical shape +expected downstream: + + {"messages": [{"role": "...", "content": "..."}, ...]} + +or a flat `{"text": "..."}` for completion-style corpora. + +Adapters are intentionally small and have no GPU/heavy-dep imports at module +load time, so the CLI/Coach can validate configs against arbitrary sources +without pulling `datasets` or `transformers`. +""" diff --git a/mindxtrain/data/sources/mindx_dreams.py b/mindxtrain/data/sources/mindx_dreams.py new file mode 100644 index 0000000000000000000000000000000000000000..5b60227daa8911fc1a982c58acad3755f9445078 --- /dev/null +++ b/mindxtrain/data/sources/mindx_dreams.py @@ -0,0 +1,157 @@ +"""mindX dream-cycle JSONL training corpus adapter. + +mindX's `agents/machine_dreaming.py` writes per-agent training data to +`<mindx_home>/data/memory/ltm/<agent>/<timestamp>_training.jsonl` on every +dream cycle (Phase 5b — `_write_training_data`). Each line is an OpenAI chat +example: + + {"messages": [{"role": "system|user|assistant", "content": "..."}]} + +This adapter walks the glob, deduplicates by content hash, and yields the +rows. No heavyweight deps — pure stdlib so the CLI/Coach can validate a +recipe pointing at this source even on a CPU-only laptop. + +Config example:: + + data: + source: mindx_dreams + path: /home/hacker/mindX/data/memory + max_samples: null # null = all examples +""" + +from __future__ import annotations + +import hashlib +import json +from collections.abc import Iterator +from pathlib import Path +from typing import Any + +_DEFAULT_GLOB = "ltm/**/*_training.jsonl" +_EVOLUTIONS_GLOB = "ltm/**/*_evolutions.jsonl" + + +def _row_fingerprint(row: dict[str, Any]) -> str: + """Stable hash over the messages payload for cross-cycle deduplication.""" + payload = json.dumps(row.get("messages"), sort_keys=True, separators=(",", ":")) + return hashlib.blake2b(payload.encode("utf-8"), digest_size=16).hexdigest() + + +def iter_mindx_dreams( + root: Path, + *, + glob: str = _DEFAULT_GLOB, + max_samples: int | None = None, +) -> Iterator[dict[str, Any]]: + """Yield deduplicated chat-format rows from a mindX dream JSONL tree. + + `root` is the mindX `data/memory` directory. `glob` is relative to it. + Cross-file dedup is by BLAKE2b of the sorted messages payload — + identical patterns emitted by different agents/days collapse to one row, + keeping the corpus from being dominated by very-frequent insights. + + Bad JSON lines are skipped silently (the dream writer is lossy and the + LTM tree may contain partial flushes). + """ + root = Path(root).expanduser() + if not root.exists(): + msg = f"mindX dream-corpus root not found: {root}" + raise FileNotFoundError(msg) + + seen: set[str] = set() + emitted = 0 + for jsonl_path in sorted(root.glob(glob)): + try: + with jsonl_path.open("r", encoding="utf-8") as fh: + for line in fh: + line = line.strip() + if not line: + continue + try: + row = json.loads(line) + except json.JSONDecodeError: + continue + if not isinstance(row, dict) or "messages" not in row: + continue + if not isinstance(row["messages"], list) or not row["messages"]: + continue + fp = _row_fingerprint(row) + if fp in seen: + continue + seen.add(fp) + yield row + emitted += 1 + if max_samples is not None and emitted >= max_samples: + return + except OSError: + continue + + +def count_mindx_dreams(root: Path, *, glob: str = _DEFAULT_GLOB) -> dict[str, int]: + """Cheap statistics about a corpus root — files, raw lines, unique rows. + + Used by the Coach UI / CLI to surface "corpus has N examples" before a + training run is dispatched. Walks the tree once; safe to call from a + request handler. + """ + root = Path(root).expanduser() + files = 0 + raw_lines = 0 + unique = 0 + seen: set[str] = set() + for jsonl_path in root.glob(glob): + files += 1 + try: + with jsonl_path.open("r", encoding="utf-8") as fh: + for line in fh: + if not line.strip(): + continue + raw_lines += 1 + try: + row = json.loads(line) + except json.JSONDecodeError: + continue + if not isinstance(row, dict) or "messages" not in row: + continue + fp = _row_fingerprint(row) + if fp not in seen: + seen.add(fp) + unique += 1 + except OSError: + continue + return {"files": files, "raw_lines": raw_lines, "unique_rows": unique} + + +def iter_mindx_evolutions( + root: Path, + *, + max_samples: int | None = None, +) -> Iterator[dict[str, Any]]: + """Yield deduplicated chat-format rows from a mindX evolution-proposal tree. + + Peer of `iter_mindx_dreams` for the new dream-cycle phase that emits + `<agent>_evolutions.jsonl` files alongside the consolidation training + JSONL. Schema is identical (OpenAI chat); content is an evolution + suggestion derived from the agent's top-scored insights. + + Same dedup semantics as the consolidation source — identical proposals + emitted across cycles collapse to a single row. + """ + yield from iter_mindx_dreams(root, glob=_EVOLUTIONS_GLOB, max_samples=max_samples) + + +def count_mindx_evolutions(root: Path) -> dict[str, int]: + """Cheap statistics about an evolution-proposal corpus root. + + Same shape as `count_mindx_dreams` (files / raw_lines / unique_rows) so + the Coach corpus card can render both buckets uniformly. + """ + return count_mindx_dreams(root, glob=_EVOLUTIONS_GLOB) + + +__all__ = [ + "count_mindx_dreams", + "count_mindx_evolutions", + "iter_mindx_dreams", + "iter_mindx_evolutions", +] diff --git a/mindxtrain/data/synth.py b/mindxtrain/data/synth.py new file mode 100644 index 0000000000000000000000000000000000000000..1ba46780fc683df17ca5672909c0059d69d38926 --- /dev/null +++ b/mindxtrain/data/synth.py @@ -0,0 +1,116 @@ +"""Synthetic data generation via teacher models. + +Drives bulk-prompt rollouts through an OpenAI-compatible teacher endpoint +(default vLLM-ROCm at `MINDXTRAIN_TEACHER_BASE_URL`). Useful for filling +gaps in the curated corpus (style transfer, persona conditioning, tool-call +trajectories). +""" + +from __future__ import annotations + +import os +from collections.abc import Iterable, Iterator +from typing import Literal + +import httpx +from pydantic import BaseModel, ConfigDict, Field + + +class SynthRecipe(BaseModel): + model_config = ConfigDict(extra="forbid") + + teacher: str = Field(default="Qwen/Qwen3.5-8B", description="HF id of the teacher model") + seeds: list[str] = Field(default_factory=list) + n_per_seed: int = Field(default=8, ge=1) + style: Literal["sft", "dpo", "tool_use"] = "sft" + temperature: float = Field(default=0.9, ge=0.0, le=2.0) + max_tokens: int = Field(default=512, ge=1) + + +def _post_completion( + base_url: str, + teacher: str, + prompt: str, + *, + temperature: float, + max_tokens: int, + timeout_s: float, +) -> str: + body = { + "model": teacher, + "messages": [{"role": "user", "content": prompt}], + "temperature": temperature, + "max_tokens": max_tokens, + } + with httpx.Client(timeout=timeout_s) as client: + resp = client.post(f"{base_url}/chat/completions", json=body) + resp.raise_for_status() + data = resp.json() + return ((data.get("choices") or [{}])[0].get("message") or {}).get("content", "") or "" + + +def synthesize(recipe: SynthRecipe, *, base_url: str | None = None, timeout_s: float = 120.0) -> Iterator[dict[str, str]]: + """Yield synthetic samples per `recipe`. POSTs to a vLLM-compatible endpoint.""" + base_url = ( + base_url or os.environ.get("MINDXTRAIN_TEACHER_BASE_URL", "http://localhost:8000/v1") + ).rstrip("/") + teacher = os.environ.get("MINDXTRAIN_TEACHER_MODEL", recipe.teacher) + for seed in recipe.seeds: + for i in range(recipe.n_per_seed): + response = _post_completion( + base_url, + teacher, + seed, + temperature=recipe.temperature, + max_tokens=recipe.max_tokens, + timeout_s=timeout_s, + ) + yield { + "seed": seed, + "rollout_index": str(i), + "response": response, + "style": recipe.style, + } + + +def merge_synth_with_real( + synth: Iterable[dict[str, str]], + real: Iterable[dict[str, str]], + ratio: float = 0.3, +) -> Iterator[dict[str, str]]: + """Interleave synth and real samples at the given synth-share ratio. + + Deterministic round-robin: every k-th sample is synth where k=1/ratio. + """ + if not (0.0 <= ratio <= 1.0): + msg = f"ratio must be in [0,1]; got {ratio}" + raise ValueError(msg) + if ratio == 0.0: + yield from real + return + if ratio == 1.0: + yield from synth + return + + real_iter = iter(real) + synth_iter = iter(synth) + counter = 0.0 + for _ in range(10**9): # effectively infinite; consumer breaks + counter += ratio + if counter >= 1.0: + counter -= 1.0 + try: + yield next(synth_iter) + except StopIteration: + # synth exhausted; fall through to real-only + yield from real_iter + return + else: + try: + yield next(real_iter) + except StopIteration: + yield from synth_iter + return + + +__all__ = ["SynthRecipe", "merge_synth_with_real", "synthesize"] diff --git a/mindxtrain/data/tokenize.py b/mindxtrain/data/tokenize.py new file mode 100644 index 0000000000000000000000000000000000000000..b2410ebd220fb4e0f770375d3cd0488a31f8238e --- /dev/null +++ b/mindxtrain/data/tokenize.py @@ -0,0 +1,38 @@ +"""Tokenization with the base model's tokenizer (cached per-model). + +Lazy `import transformers` — users without `--extra ml` get a clean error. +""" + +from __future__ import annotations + +from collections.abc import Iterable, Iterator +from functools import lru_cache +from typing import Any + + +@lru_cache(maxsize=8) +def _get_tokenizer(model_name: str) -> Any: + try: + from transformers import AutoTokenizer + except ImportError as exc: + msg = "transformers not installed; run `uv sync --extra ml`." + raise RuntimeError(msg) from exc + return AutoTokenizer.from_pretrained(model_name, use_fast=True) + + +def tokenize_stream( + docs: Iterable[str], + model_name: str, + *, + add_special_tokens: bool = False, +) -> Iterator[list[int]]: + """Yield list[int] token ids per doc, using the model's fast tokenizer.""" + tok = _get_tokenizer(model_name) + for doc in docs: + if not isinstance(doc, str) or not doc: + continue + ids = tok.encode(doc, add_special_tokens=add_special_tokens) + yield list(ids) + + +__all__ = ["tokenize_stream"] diff --git a/mindxtrain/data/verify.py b/mindxtrain/data/verify.py new file mode 100644 index 0000000000000000000000000000000000000000..11e2306a54593ed11d60bc064e5ea93f281a1429 --- /dev/null +++ b/mindxtrain/data/verify.py @@ -0,0 +1,57 @@ +"""Dataset verification — recompute BLAKE3 over the local files referenced by +a shard manifest, surface mismatches. + +Pure stdlib + `mindxtrain.provenance.hashing`. +""" + +from __future__ import annotations + +import json +from pathlib import Path + +from pydantic import BaseModel, ConfigDict, Field + +from mindxtrain.provenance.hashing import blake3_file + + +class VerifyResult(BaseModel): + model_config = ConfigDict(extra="forbid") + + manifest_path: Path + matched: int = 0 + mismatched: list[str] = Field(default_factory=list) + missing: list[str] = Field(default_factory=list) + + +def verify_dataset(manifest_path: Path, root: Path) -> VerifyResult: + """Walk the manifest, recompute hashes, return mismatches. + + Manifest format expected: + + {"shards": [{"path": "shard-00000.tar", "blake3": "...."}, ...]} + + `path` is resolved relative to `root`. + """ + manifest_path = Path(manifest_path) + root = Path(root) + raw = json.loads(manifest_path.read_text()) + shards = raw.get("shards") or [] + result = VerifyResult(manifest_path=manifest_path) + for shard in shards: + rel = shard.get("path", "") + expected = shard.get("blake3", "") + if not rel or not expected: + continue + local = root / rel + if not local.exists(): + result.missing.append(rel) + continue + actual = blake3_file(local) + if actual != expected: + result.mismatched.append(rel) + else: + result.matched += 1 + return result + + +__all__ = ["VerifyResult", "verify_dataset"] diff --git a/mindxtrain/deploy/__init__.py b/mindxtrain/deploy/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..645bc04885aa42a2253d84f15045b1c5cd74f9bf --- /dev/null +++ b/mindxtrain/deploy/__init__.py @@ -0,0 +1,12 @@ +"""Deploy subpackage. + +Re-exports public command-builders used by `mindxtrain serve` and tests. +""" + +from mindxtrain.deploy.sglang_rocm import build_sglang_command +from mindxtrain.deploy.vllm_launcher import build_vllm_command + +__all__ = [ + "build_sglang_command", + "build_vllm_command", +] diff --git a/mindxtrain/deploy/_orchestrator.py b/mindxtrain/deploy/_orchestrator.py new file mode 100644 index 0000000000000000000000000000000000000000..440df08f01d1c850324584a50b08af7633f19fc4 --- /dev/null +++ b/mindxtrain/deploy/_orchestrator.py @@ -0,0 +1,439 @@ +"""Multi-step pipeline runner for the deploy/* subcommands. + +Each Coach UI deploy action (GitHub push, droplet sync, droplet provision) +expands to a list of `Step`s that must run serially. This module owns the +serial-execution loop; everything else is just a step list. + +Design notes: + +- We deliberately do NOT reuse `runs.spawn_subprocess_streaming` directly. + That helper assumes a single Popen per run and emits `StatusEvent('succeeded')` + + closes subscribers as soon as the Popen exits. Chaining N steps would + prematurely close the SSE stream after step 1. Instead, this module runs + its own daemon thread, manually publishes `LogEvent`s line-by-line, and + emits exactly one terminal `StatusEvent` after all steps complete (or one + fails). + +- Cancellation routes through the existing `RunRegistry.cancel()` API. We + call `registry.attach_process()` each time a new step starts, so the + registry's Popen pointer always tracks the live subprocess. + +- For non-subprocess work (httpx calls to the Dev Cloud API), the provision + pipeline does not use `Step` objects — it publishes `LogEvent`s inline + and then hands off to the step runner for the SSH phases. +""" + +from __future__ import annotations + +import contextlib +import subprocess +import threading +from collections.abc import Callable +from pathlib import Path + +from mindxtrain.deploy.amd_dev_cloud import ( + AmdDevCloudClient, + AmdDevCloudConfig, + extract_public_ip, +) +from mindxtrain.deploy.cloud_init import BOOTSTRAP_SENTINEL, render +from mindxtrain.deploy.droplet import ( + DropletConfig, + build_scp_plan_back, + build_ssh_probe, + build_tail_cloud_init, + build_tail_training_log, + sync_steps, +) +from mindxtrain.deploy.github_push import ( + GithubConfig, + Step, + bootstrap_steps, + remote_url, + write_sha_file, +) +from mindxtrain.operator.runs import ( + LogEvent, + RunRegistry, + StatusEvent, + default_registry, + parse_trainer_log_line, +) + +# ---- run_pipeline -------------------------------------------------------- + + +def _publish_log(registry: RunRegistry, run_id: str, line: str) -> None: + registry.publish_threadsafe(run_id, LogEvent(run_id=run_id, line=line)) + + +def _open_log_file(log_path: Path) -> object: + log_path.parent.mkdir(parents=True, exist_ok=True) + return log_path.open("w", buffering=1) + + +def _stream_step( + *, + step: Step, + registry: RunRegistry, + run_id: str, + log_file: object, + capture: list[str], + parse_trainer: bool = False, +) -> int: + """Run a single step's subprocess, tee stdout to log_file + events. + + By default each line becomes a `LogEvent`. With `parse_trainer=True`, + lines that match the HF Trainer JSON format are upgraded to `StepEvent` + (drives Coach's loss chart and metrics table); non-matching lines still + become `LogEvent`s. This is how remote training output bridges into the + same SSE channel the in-process trainer would publish to. + + Captures stdout into `capture` if `step.capture_stdout`. Returns the rc. + """ + proc = subprocess.Popen( + step.cmd, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, + env=step.env or None, + text=True, + bufsize=1, + ) + registry.attach_process(run_id, proc) + assert proc.stdout is not None + step_ctr = 0 + for raw in proc.stdout: + line = raw.rstrip("\n") + log_file.write(raw) # type: ignore[attr-defined] + log_file.flush() # type: ignore[attr-defined] + if step.capture_stdout: + capture.append(line) + if parse_trainer: + step_ev = parse_trainer_log_line(line, fallback_step=step_ctr + 1) + if step_ev is not None: + step_ctr = step_ev.step + registry.publish_threadsafe( + run_id, step_ev.model_copy(update={"run_id": run_id}), + ) + continue + _publish_log(registry, run_id, line) + return proc.wait() + + +def run_pipeline( + steps: list[Step], + *, + run_id: str, + out_dir: Path, + registry: RunRegistry | None = None, + on_done: Callable[[int, dict[str, str]], None] | None = None, +) -> threading.Thread: + """Run `steps` serially in a daemon thread. Returns the thread immediately. + + Predicate logic: if a step's `predicate_step` was run earlier and that + step's rc is *not* in `predicate_rc_in`, the step is skipped (with a + LogEvent) and its rc is recorded as -1 ("skipped"). + + Captured stdout: any step with `capture_stdout=True` has its full stdout + joined into a string and stashed in `captured[step.label]`. The `on_done` + callback receives (final_rc, captured). + + Final status: emits exactly one `StatusEvent("succeeded"|"failed")` and + closes subscribers. Cancel via `registry.cancel(run_id)`. + """ + reg = registry if registry is not None else default_registry() + log_file = _open_log_file(out_dir / "pipeline.log") + + def _runner() -> None: + rcs: dict[str, int] = {} + captured: dict[str, str] = {} + final_rc = 0 + try: + for i, step in enumerate(steps, 1): + # Predicate: skip if the gating step's rc is not in the allowed set. + if step.predicate_step is not None: + gate_rc = rcs.get(step.predicate_step) + if gate_rc is None or gate_rc not in step.predicate_rc_in: + _publish_log(reg, run_id, f"=== skip {i}/{len(steps)}: {step.label} (predicate {step.predicate_step}={gate_rc}) ===") + rcs[step.label] = -1 + continue + + _publish_log(reg, run_id, f"=== step {i}/{len(steps)}: {step.label} ===") + buffer: list[str] = [] + rc = _stream_step( + step=step, + registry=reg, + run_id=run_id, + log_file=log_file, + capture=buffer, + ) + rcs[step.label] = rc + if step.capture_stdout: + captured[step.label] = "\n".join(buffer) + if rc != 0 and not step.allow_failure: + _publish_log(reg, run_id, f"=== step {step.label} exited rc={rc}; aborting pipeline ===") + final_rc = rc + break + if rc != 0: + _publish_log(reg, run_id, f" (probe {step.label} rc={rc} — continuing)") + + status = "succeeded" if final_rc == 0 else "failed" + reg.publish_threadsafe( + run_id, + StatusEvent(run_id=run_id, status=status, message=f"rc={final_rc}"), + ) + finally: + with contextlib.suppress(Exception): + log_file.close() # type: ignore[attr-defined] + reg.close_subscribers(run_id) + if on_done is not None: + with contextlib.suppress(Exception): + on_done(final_rc, captured) + + t = threading.Thread(target=_runner, daemon=True, name=f"deploy-{run_id}") + t.start() + return t + + +# ---- GitHub push pipeline ------------------------------------------------ + + +def github_push_pipeline( + cfg: GithubConfig, + *, + run_id: str, + out_dir: Path, + commit_message: str = "mindXtrain initial push", + force: bool = False, + registry: RunRegistry | None = None, + on_done: Callable[[int, dict[str, str]], None] | None = None, +) -> threading.Thread: + """Drives the github_push step list. Captures HEAD sha to git_sha.txt.""" + reg = registry if registry is not None else default_registry() + steps = bootstrap_steps(cfg, commit_message=commit_message, force=force) + + # Wrap on_done so we can persist the captured HEAD sha + emit a clean + # guidance LogEvent if a stale remote was detected without --force. + def _on_done(rc: int, captured: dict[str, str]) -> None: + sha = captured.get("head-sha", "").strip() + existing_remote = captured.get("probe-remote", "").strip() + if sha: + target = write_sha_file(out_dir, sha) + _publish_log(reg, run_id, f" HEAD sha {sha} written to {target}") + if existing_remote and existing_remote != remote_url(cfg.repo) and not force: + _publish_log( + reg, run_id, + f" origin already points at {existing_remote!r}; " + f"re-run with force=true to switch to {remote_url(cfg.repo)!r}", + ) + if on_done is not None: + with contextlib.suppress(Exception): + on_done(rc, captured) + + return run_pipeline(steps, run_id=run_id, out_dir=out_dir, registry=reg, on_done=_on_done) + + +# ---- Existing-droplet sync pipeline ------------------------------------- + + +def droplet_sync_pipeline( + cfg: DropletConfig, + *, + repo_root: Path, + run_id: str, + out_dir: Path, + run_bench: bool = True, + fetch_plan: bool = True, + registry: RunRegistry | None = None, + on_done: Callable[[int, dict[str, str]], None] | None = None, +) -> threading.Thread: + plan_dest = out_dir / "plan.remote.json" + steps = sync_steps( + cfg, + repo_root=repo_root, + run_bench=run_bench, + fetch_plan=fetch_plan, + plan_dest=plan_dest, + ) + return run_pipeline(steps, run_id=run_id, out_dir=out_dir, registry=registry, on_done=on_done) + + +# ---- AMD Dev Cloud provision pipeline ----------------------------------- + + +def droplet_provision_pipeline( + cloud_cfg: AmdDevCloudConfig, + *, + name: str, + repo: str, + branch: str, + container: str, + extras: str, + run_id: str, + out_dir: Path, + wait_for_bootstrap: bool = True, + recipe: str | None = None, + registry: RunRegistry | None = None, + client_factory: Callable[[AmdDevCloudConfig], AmdDevCloudClient] | None = None, + on_done: Callable[[int, dict[str, str]], None] | None = None, +) -> threading.Thread: + """Create a droplet, wait for bootstrap, scp plan.json back, optionally + train + bridge training events into the run's SSE stream. + + When `recipe` is set, cloud-init also runs `mindxtrain train` after + bench, and the orchestrator adds a fifth step that SSH-tails the + training log so Coach's loss chart populates live from the droplet. + Without `recipe`, behaviour is exactly as before (bench only). + """ + reg = registry if registry is not None else default_registry() + out_dir.mkdir(parents=True, exist_ok=True) + + user_data = render( + repo=repo, + branch=branch, + container=container, + extras=extras, + recipe=recipe, + ) + factory = client_factory or AmdDevCloudClient + + def _log(line: str) -> None: + _publish_log(reg, run_id, line) + + def _runner() -> None: + final_rc = 0 + captured: dict[str, str] = {} + try: + with factory(cloud_cfg) as client: + _publish_log(reg, run_id, "=== step 1/4: create droplet ===") + droplet = client.create(name=name, user_data=user_data, log=_log) + droplet_id = int(droplet.get("id", 0)) + captured["droplet_id"] = str(droplet_id) + (out_dir / "droplet_id.txt").write_text(str(droplet_id) + "\n") + + _publish_log(reg, run_id, f"=== step 2/4: poll until active ({droplet_id}) ===") + droplet = client.poll_until_active(droplet_id, log=_log) + ip = extract_public_ip(droplet) or "" + captured["public_ip"] = ip + (out_dir / "public_ip.txt").write_text(ip + "\n") + if not ip: + _publish_log(reg, run_id, " (no public IPv4 returned; bailing out)") + final_rc = 2 + return + + if not wait_for_bootstrap: + _publish_log(reg, run_id, "wait_for_bootstrap=false — exiting after provision") + return + + droplet_cfg = DropletConfig( + host=ip, + user="root", + ssh_key=cloud_cfg_ssh_key(cloud_cfg), + container=container, + extras=extras, + ) + _publish_log(reg, run_id, "=== step 3/4: wait for ssh + tail cloud-init ===") + # Inline ssh-probe with a few retries; bench-stage cloud-init can take a while. + probe_steps = [ + Step(label=f"ssh-probe-{i}", cmd=build_ssh_probe(droplet_cfg), allow_failure=True) + for i in range(60) # 60 * 5s = 5 min + ] + log_file = _open_log_file(out_dir / "pipeline.log") + ready = False + try: + for ps in probe_steps: + rc = _stream_step(step=ps, registry=reg, run_id=run_id, log_file=log_file, capture=[]) + if rc == 0: + ready = True + break + import time as _time + _time.sleep(5) + finally: + with contextlib.suppress(Exception): + log_file.close() # type: ignore[attr-defined] + if not ready: + _publish_log(reg, run_id, " ssh did not come up in 5 minutes; aborting") + final_rc = 3 + return + + tail_step = Step(label="cloud-init-tail", cmd=build_tail_cloud_init(droplet_cfg)) + scp_step = Step( + label="scp-plan", + cmd=build_scp_plan_back(droplet_cfg, out_dir / "plan.remote.json"), + allow_failure=True, + ) + total_steps = 5 if recipe else 4 + log_file = _open_log_file(out_dir / "pipeline.log") + try: + _publish_log(reg, run_id, f" (waiting for {BOOTSTRAP_SENTINEL})") + rc = _stream_step(step=tail_step, registry=reg, run_id=run_id, log_file=log_file, capture=[]) + if rc != 0: + final_rc = rc + return + _publish_log(reg, run_id, f"=== step 4/{total_steps}: scp plan.json back ===") + _stream_step(step=scp_step, registry=reg, run_id=run_id, log_file=log_file, capture=[]) + + if recipe: + _publish_log( + reg, run_id, + f"=== step 5/{total_steps}: bridge training log " + f"for recipe={recipe} ===", + ) + train_tail = Step( + label="train-tail", + cmd=build_tail_training_log(droplet_cfg), + ) + train_rc = _stream_step( + step=train_tail, + registry=reg, + run_id=run_id, + log_file=log_file, + capture=[], + parse_trainer=True, + ) + if train_rc != 0: + _publish_log( + reg, run_id, + f" (remote train exit rc={train_rc})", + ) + final_rc = train_rc + finally: + with contextlib.suppress(Exception): + log_file.close() # type: ignore[attr-defined] + + except Exception as exc: + _publish_log(reg, run_id, f"!! {type(exc).__name__}: {exc}") + final_rc = 1 + finally: + status = "succeeded" if final_rc == 0 else "failed" + reg.publish_threadsafe( + run_id, + StatusEvent(run_id=run_id, status=status, message=f"rc={final_rc}"), + ) + reg.close_subscribers(run_id) + if on_done is not None: + with contextlib.suppress(Exception): + on_done(final_rc, captured) + + t = threading.Thread(target=_runner, daemon=True, name=f"provision-{run_id}") + t.start() + return t + + +def cloud_cfg_ssh_key(cloud_cfg: AmdDevCloudConfig) -> str: + """Default ssh key path used to SSH into a freshly-provisioned droplet. + + The Dev Cloud control plane only stores the public key; the matching + private key must be on the operator host. We assume it lives at + `~/.ssh/id_ed25519` unless DROPLET_SSH_KEY is set. + """ + import os as _os + return _os.environ.get("DROPLET_SSH_KEY", "~/.ssh/id_ed25519") + + +__all__ = [ + "droplet_provision_pipeline", + "droplet_sync_pipeline", + "github_push_pipeline", + "run_pipeline", +] diff --git a/mindxtrain/deploy/ab_test.py b/mindxtrain/deploy/ab_test.py new file mode 100644 index 0000000000000000000000000000000000000000..25ef8be2d96e50e4e4e8a46059578b94c1b9edb9 --- /dev/null +++ b/mindxtrain/deploy/ab_test.py @@ -0,0 +1,51 @@ +"""A/B traffic split — canary vs live. + +Pure-Python `Splitter`: deterministic per-request seed → canary or live based +on `cfg.canary_pct`. The actual traffic injection happens in the operator +FastAPI app's chat handler (which calls `Splitter.pick(req)` to decide which +slot's backend to route to). +""" + +from __future__ import annotations + +import hashlib +import random + +from pydantic import BaseModel, ConfigDict, Field + + +class AbConfig(BaseModel): + model_config = ConfigDict(extra="forbid") + + canary_pct: float = Field(default=0.05, ge=0.0, le=1.0) + auto_rollback_threshold: float = Field(default=0.02, ge=0.0) + + +class Splitter: + """Decides per-request which slot serves traffic.""" + + def __init__(self, cfg: AbConfig | None = None) -> None: + self.cfg = cfg or AbConfig() + + def pick(self, request_id: str | None = None) -> str: + """Return `'canary'` or `'live'`. + + If `request_id` is provided we hash it (stable across retries); else + we use random.random() (stateless). + """ + if self.cfg.canary_pct <= 0.0: + return "live" + if request_id is None: + r = random.random() + else: + digest = hashlib.blake2s(request_id.encode("utf-8"), digest_size=8).digest() + r = int.from_bytes(digest, "big") / float(2**64) + return "canary" if r < self.cfg.canary_pct else "live" + + +def serve_split(cfg: AbConfig) -> Splitter: + """Construct the Splitter for an active A/B run.""" + return Splitter(cfg) + + +__all__ = ["AbConfig", "Splitter", "serve_split"] diff --git a/mindxtrain/deploy/amd_dev_cloud.py b/mindxtrain/deploy/amd_dev_cloud.py new file mode 100644 index 0000000000000000000000000000000000000000..97f8886c7cdd0b6aef798e981033eee4fec3a142 --- /dev/null +++ b/mindxtrain/deploy/amd_dev_cloud.py @@ -0,0 +1,281 @@ +"""AMD Developer Cloud REST client. + +The schema follows DigitalOcean's `/v2/droplets` shape (the AMD Dev Cloud is +a DO-derived control plane). Documented at: + https://docs.digitalocean.com/reference/api/reference/ + +We only need a thin slice: create, poll-until-active, list (filtered by +name), destroy. Auth is `Bearer <AMD_DEV_CLOUD_TOKEN>`. + +Pure httpx — no SDK, no new dependencies. +""" + +from __future__ import annotations + +import os +import time +from collections.abc import Callable +from dataclasses import dataclass +from typing import Any + +import httpx + +DEFAULT_BASE = "https://api.devcloud.amd.com" + +# Sane non-zero defaults so a misconfigured droplet doesn't sit in a poll +# loop forever. 20 minutes covers a cold cloud-init bootstrap with apt-get +# update + container pull on a slow link. +DEFAULT_POLL_INTERVAL = 5.0 +DEFAULT_POLL_TIMEOUT = 1200.0 + + +class AmdDevCloudError(RuntimeError): + """Generic Dev Cloud REST error.""" + + +class AmdDevCloudAuthError(AmdDevCloudError): + """401/403 from the Dev Cloud control plane.""" + + +@dataclass(frozen=True) +class AmdDevCloudConfig: + token: str + api_base: str = DEFAULT_BASE + region: str = "atl1" + size: str = "gpu-mi300x8-1536gb-devcloud" + image: str = "vllm-0-17-1" + ssh_key_id: int = 0 + tags: tuple[str, ...] = ("mindx", "train") + + +def required_env() -> tuple[str, ...]: + return ("AMD_DEV_CLOUD_TOKEN", "AMD_DEV_CLOUD_SSH_KEY_ID") + + +def missing_env(env: dict[str, str] | None = None) -> list[str]: + src = env if env is not None else os.environ + out = [k for k in required_env() if not src.get(k)] + return out + + +def status_target(env: dict[str, str] | None = None) -> str: + src = env if env is not None else os.environ + region = src.get("AMD_DEV_CLOUD_REGION", "atl1") + size = src.get("AMD_DEV_CLOUD_SIZE", "gpu-mi300x8-1536gb-devcloud") + return f"amd-dev-cloud:{region}:{size}" + + +def from_env(env: dict[str, str] | None = None) -> AmdDevCloudConfig: + src = env if env is not None else os.environ + missing = missing_env(env) + if missing: + msg = f"AMD Dev Cloud config missing env: {', '.join(missing)}" + raise RuntimeError(msg) + tags_raw = src.get("AMD_DEV_CLOUD_TAGS", "mindx,train") + tags = tuple(t.strip() for t in tags_raw.split(",") if t.strip()) + try: + ssh_key_id = int(src["AMD_DEV_CLOUD_SSH_KEY_ID"]) + except (KeyError, ValueError) as exc: + msg = f"AMD_DEV_CLOUD_SSH_KEY_ID must be an int (got {src.get('AMD_DEV_CLOUD_SSH_KEY_ID')!r})" + raise RuntimeError(msg) from exc + return AmdDevCloudConfig( + token=src["AMD_DEV_CLOUD_TOKEN"], + api_base=src.get("AMD_DEV_CLOUD_API_BASE", DEFAULT_BASE), + region=src.get("AMD_DEV_CLOUD_REGION", "atl1"), + size=src.get("AMD_DEV_CLOUD_SIZE", "gpu-mi300x8-1536gb-devcloud"), + image=src.get("AMD_DEV_CLOUD_IMAGE", "vllm-0-17-1"), + ssh_key_id=ssh_key_id, + tags=tags, + ) + + +def build_create_payload( + cfg: AmdDevCloudConfig, + *, + name: str = "mindxtrain", + user_data: str = "", +) -> dict[str, Any]: + """Mirror the JSON shape from the user-supplied curl example.""" + return { + "name": name, + "region": cfg.region, + "size": cfg.size, + "image": cfg.image, + "ssh_keys": [cfg.ssh_key_id], + "backups": False, + "ipv6": True, + "monitoring": True, + "tags": list(cfg.tags), + "user_data": user_data, + "vpc_uuid": "", + } + + +# ---- client -------------------------------------------------------------- + +LogFn = Callable[[str], None] + + +def _noop(_line: str) -> None: + return None + + +class AmdDevCloudClient: + """Thin httpx wrapper around the AMD Dev Cloud REST surface. + + Methods accept an optional `log` callback; when provided, each REST call + emits a single human-readable line through it. The orchestrator passes a + callback that publishes `LogEvent`s via `RunRegistry.publish_threadsafe`, + so the user sees the provision pipeline progress in real time. + """ + + def __init__( + self, + cfg: AmdDevCloudConfig, + *, + client: httpx.Client | None = None, + ) -> None: + self.cfg = cfg + self._owned = client is None + self._client = client or httpx.Client( + base_url=cfg.api_base, + headers={"Authorization": f"Bearer {cfg.token}"}, + timeout=30.0, + ) + + def close(self) -> None: + if self._owned: + self._client.close() + + def __enter__(self) -> AmdDevCloudClient: + return self + + def __exit__(self, *_args: Any) -> None: + self.close() + + # -- create ----------------------------------------------------------- + + def create( + self, + *, + name: str = "mindxtrain", + user_data: str = "", + log: LogFn = _noop, + ) -> dict[str, Any]: + body = build_create_payload(self.cfg, name=name, user_data=user_data) + log(f"POST {self.cfg.api_base}/v2/droplets name={name} size={self.cfg.size}") + r = self._client.post("/v2/droplets", json=body) + if r.status_code in (401, 403): + msg = f"AMD Dev Cloud auth failed: {r.status_code} {r.text[:200]}" + raise AmdDevCloudAuthError(msg) + if r.status_code >= 400: + msg = f"create droplet failed: {r.status_code} {r.text[:500]}" + raise AmdDevCloudError(msg) + out = r.json() + droplet = out.get("droplet") or out + log(f" → 202 droplet_id={droplet.get('id')} status={droplet.get('status')}") + return droplet + + # -- poll ------------------------------------------------------------- + + def get(self, droplet_id: int, *, log: LogFn = _noop) -> dict[str, Any]: + r = self._client.get(f"/v2/droplets/{droplet_id}") + if r.status_code in (401, 403): + msg = f"AMD Dev Cloud auth failed: {r.status_code}" + raise AmdDevCloudAuthError(msg) + if r.status_code >= 400: + msg = f"get droplet {droplet_id} failed: {r.status_code} {r.text[:500]}" + raise AmdDevCloudError(msg) + out = r.json() + droplet = out.get("droplet") or out + log(f" status={droplet.get('status')} ip={_extract_public_ip(droplet) or '-'}") + return droplet + + def poll_until_active( + self, + droplet_id: int, + *, + timeout: float = DEFAULT_POLL_TIMEOUT, + interval: float = DEFAULT_POLL_INTERVAL, + log: LogFn = _noop, + sleep: Callable[[float], None] = time.sleep, + now: Callable[[], float] = time.monotonic, + ) -> dict[str, Any]: + """Poll until status='active' or timeout. + + `sleep` and `now` are injected for tests. + """ + deadline = now() + timeout + log(f"polling droplet {droplet_id} until active (timeout={int(timeout)}s)") + while True: + droplet = self.get(droplet_id, log=log) + status = str(droplet.get("status", "")) + if status == "active": + ip = _extract_public_ip(droplet) + log(f" → active, public_ip={ip}") + return droplet + if status in ("errored", "off", "archive"): + msg = f"droplet {droplet_id} reached terminal state {status!r} before active" + raise AmdDevCloudError(msg) + if now() >= deadline: + msg = f"droplet {droplet_id} did not reach 'active' within {int(timeout)}s (last={status})" + raise TimeoutError(msg) + sleep(interval) + + # -- list / destroy --------------------------------------------------- + + def list(self, *, name: str | None = None) -> list[dict[str, Any]]: + r = self._client.get("/v2/droplets") + if r.status_code in (401, 403): + msg = f"AMD Dev Cloud auth failed: {r.status_code}" + raise AmdDevCloudAuthError(msg) + if r.status_code >= 400: + msg = f"list droplets failed: {r.status_code} {r.text[:500]}" + raise AmdDevCloudError(msg) + out = r.json().get("droplets", []) + if name is None: + return out + return [d for d in out if d.get("name") == name] + + def destroy(self, droplet_id: int, *, log: LogFn = _noop) -> None: + log(f"DELETE /v2/droplets/{droplet_id}") + r = self._client.delete(f"/v2/droplets/{droplet_id}") + if r.status_code in (401, 403): + msg = f"AMD Dev Cloud auth failed: {r.status_code}" + raise AmdDevCloudAuthError(msg) + if r.status_code >= 400 and r.status_code != 404: + msg = f"destroy droplet {droplet_id} failed: {r.status_code} {r.text[:500]}" + raise AmdDevCloudError(msg) + log(f" → {r.status_code}") + + +def extract_public_ip(droplet: dict[str, Any]) -> str | None: + nets = droplet.get("networks") or {} + v4 = nets.get("v4") or [] + for net in v4: + if str(net.get("type", "")).lower() == "public": + return str(net.get("ip_address", "")) + if v4: + return str(v4[0].get("ip_address", "")) + return None + + +# Backwards-compatible private alias for callers that haven't migrated. +_extract_public_ip = extract_public_ip + + +__all__ = [ + "DEFAULT_BASE", + "DEFAULT_POLL_INTERVAL", + "DEFAULT_POLL_TIMEOUT", + "AmdDevCloudAuthError", + "AmdDevCloudClient", + "AmdDevCloudConfig", + "AmdDevCloudError", + "build_create_payload", + "extract_public_ip", + "from_env", + "missing_env", + "required_env", + "status_target", +] diff --git a/mindxtrain/deploy/api_client.py b/mindxtrain/deploy/api_client.py new file mode 100644 index 0000000000000000000000000000000000000000..2729c1cf058fcd0895d204ac02725100426863c5 --- /dev/null +++ b/mindxtrain/deploy/api_client.py @@ -0,0 +1,197 @@ +"""External-API clients — register with mindx.pythai.net + list on AgenticPlace. + +Real httpx POSTs against configurable base URLs (env-overridable). + +These endpoints are part of the mindX cognitive ecosystem; if your `*.pythai.net` +endpoints aren't deployed yet, set `MINDXTRAIN_API_BASE_URL` / +`MINDXTRAIN_AGENTICPLACE_URL` to your own service. +""" + +from __future__ import annotations + +import json +import os + +import httpx +from pydantic import BaseModel, ConfigDict, Field + + +class MindXAgentRegistration(BaseModel): + model_config = ConfigDict(extra="forbid") + + run_id: str + hf_url: str + cid: str + capability: str = "chat" + + +class MindXFallbackSwap(BaseModel): + """Payload for the mindX runtime fallback-swap endpoint.""" + + model_config = ConfigDict(extra="forbid") + + provider: str = Field(default="vllm", description="LLM provider in mindX (vllm, ollama, ...).") + model: str = Field(..., min_length=1, description="HF Hub repo or provider-local model name.") + + +class AgenticPlaceListing(BaseModel): + model_config = ConfigDict(extra="forbid") + + run_id: str + hf_url: str + title: str = "" + price_usdc_per_million_tokens: float = 1.0 + + +def register_with_mindx( + *, + run_id: str, + hf_url: str, + cid: str, + api_url: str | None = None, + timeout_s: float = 30.0, +) -> dict[str, str]: + """POST /v1/agents on the mindX cognitive API; return the registration receipt.""" + api_url = (api_url or os.environ.get("MINDXTRAIN_API_BASE_URL", "https://mindx.pythai.net")).rstrip("/") + body = MindXAgentRegistration(run_id=run_id, hf_url=hf_url, cid=cid).model_dump() + with httpx.Client(timeout=timeout_s) as client: + resp = client.post(f"{api_url}/v1/agents", json=body) + resp.raise_for_status() + data: dict[str, str] = resp.json() + return data + + +def swap_mindx_fallback_model( + *, + provider: str = "vllm", + model: str, + api_url: str | None = None, + api_key: str | None = None, + timeout_s: float = 30.0, +) -> dict[str, str]: + """PATCH /v1/config/fallback-model on mindX; return {previous, current, ...}. + + Called by the `publish` step after the trained checkpoint lands on HF Hub + so subsequent LLM handler creations in mindX resolve the new default. + + `api_url` defaults to `MINDXTRAIN_API_BASE_URL` env (or `https://mindx.pythai.net`). + `api_key`, if provided or read from `MINDXTRAIN_API_KEY`, is sent as + `Authorization: Bearer <key>` — required when the mindX deployment has + its bearer-auth secret set. + """ + api_url = (api_url or os.environ.get("MINDXTRAIN_API_BASE_URL", "https://mindx.pythai.net")).rstrip("/") + api_key = api_key if api_key is not None else os.environ.get("MINDXTRAIN_API_KEY", "") + + body = MindXFallbackSwap(provider=provider, model=model).model_dump() + headers: dict[str, str] = {} + if api_key: + headers["Authorization"] = f"Bearer {api_key}" + + with httpx.Client(timeout=timeout_s) as client: + resp = client.patch(f"{api_url}/v1/config/fallback-model", json=body, headers=headers) + resp.raise_for_status() + data: dict[str, str] = resp.json() + return data + + +def list_on_agenticplace( + *, + run_id: str, + hf_url: str, + title: str = "", + price_usdc_per_million_tokens: float = 1.0, + api_url: str | None = None, + timeout_s: float = 30.0, +) -> str: + """POST /v1/listings on AgenticPlace; return the listing slug/url.""" + api_url = ( + api_url + or os.environ.get("MINDXTRAIN_AGENTICPLACE_URL", "https://agenticplace.pythai.net") + ).rstrip("/") + body = AgenticPlaceListing( + run_id=run_id, + hf_url=hf_url, + title=title or run_id, + price_usdc_per_million_tokens=price_usdc_per_million_tokens, + ).model_dump() + with httpx.Client(timeout=timeout_s) as client: + resp = client.post(f"{api_url}/v1/listings", json=body) + resp.raise_for_status() + data = resp.json() + return str(data.get("listing_url", data)) + + +def trigger_dream_ingestion( + *, + run_id: str, + adapter_dir: str, + base_model: str, + persona_name: str = "", + imprint_delta: float | None = None, + api_url: str | None = None, + timeout_s: float = 10.0, +) -> dict[str, str]: + """Hand a freshly-imprinted actor to mindX's `machine.dream` 8hr cycle. + + Clean-room boundary: we never import or run mindX code — we hand off an + artifact *pointer* (run id + adapter path + base model + imprint delta) so the + mindX dream cycle (`agents/machine_dreaming.py`) can ingest the trained actor + on its next pass. Best-effort, with two delivery modes: + + 1. HTTP — POST `/v1/dream/ingest` on the mindX API when `MINDXTRAIN_API_BASE_URL` + is set and reachable. + 2. Inbox drop — write a pointer JSON into + `$MINDXTRAIN_MINDX_HOME/data/incoming/<run_id>.dream.json` so a filesystem- + watching dream cycle picks it up. + + Returns `{"mode": ..., "target": ...}`; never raises — a failed trigger reports + via the return dict rather than failing the training run. + """ + payload = { + "run_id": run_id, + "adapter_dir": adapter_dir, + "base_model": base_model, + "persona": persona_name, + "imprint_delta": "" if imprint_delta is None else f"{imprint_delta:.4f}", + "source": "mindxtrain.imprint", + } + api = (api_url or os.environ.get("MINDXTRAIN_API_BASE_URL", "")).rstrip("/") + if api: + try: + with httpx.Client(timeout=timeout_s) as client: + resp = client.post(f"{api}/v1/dream/ingest", json=payload) + resp.raise_for_status() + return {"mode": "http", "target": f"{api}/v1/dream/ingest"} + except (httpx.HTTPError, OSError) as exc: + payload["http_error"] = str(exc) + + # Filesystem inbox fallback — the 8hr dream cycle watches data/incoming. + home = os.environ.get("MINDXTRAIN_MINDX_HOME", "") + if home: + from pathlib import Path + + inbox = Path(home).expanduser() / "data" / "incoming" + try: + inbox.mkdir(parents=True, exist_ok=True) + ptr = inbox / f"{run_id}.dream.json" + ptr.write_text(json.dumps(payload, indent=2)) + return {"mode": "inbox", "target": str(ptr)} + except OSError as exc: + return {"mode": "failed", "target": str(inbox), "error": str(exc)} + + return { + "mode": "skipped", + "target": "", + "note": "set MINDXTRAIN_API_BASE_URL or MINDXTRAIN_MINDX_HOME to deliver", + } + + +__all__ = [ + "AgenticPlaceListing", + "MindXAgentRegistration", + "MindXFallbackSwap", + "list_on_agenticplace", + "register_with_mindx", + "swap_mindx_fallback_model", + "trigger_dream_ingestion", +] diff --git a/mindxtrain/deploy/cloud_init.py b/mindxtrain/deploy/cloud_init.py new file mode 100644 index 0000000000000000000000000000000000000000..bc8473745e80d82b1465ce0b17ccdc29f0d343fd --- /dev/null +++ b/mindxtrain/deploy/cloud_init.py @@ -0,0 +1,152 @@ +"""Cloud-init `user_data` generator for AMD Dev Cloud MI300X droplets. + +The droplet boots, runs this script, then exits to the login prompt. By the +time SSH is reachable, the repo is cloned, the container image is pulled, and +`mindxtrain bench` has produced `plan.json` on disk. + +The orchestrator polls for the sentinel file +`/workspace/mindxtrain/.bootstrap-done` over SSH; cloud-init logs land in +`/var/log/cloud-init-output.log`, which the orchestrator tails for live +feedback. + +This module is a pure string builder. No execution, no I/O. +""" + +from __future__ import annotations + +import re + +BOOTSTRAP_SENTINEL = "/workspace/mindxtrain/.bootstrap-done" +TRAIN_DONE_SENTINEL = "/workspace/mindxtrain/.train-done" +TRAIN_EXIT_SENTINEL = "/workspace/mindxtrain/.train-exit" +TRAIN_LOG_GLOB = "/workspace/mindxtrain/out/runs/*/train.log" +CLOUD_INIT_LOG = "/var/log/cloud-init-output.log" + +# Reject anything that could break out of the YAML or bash context. The repo +# slug, branch, and container image are interpolated into a `runcmd:` shell +# string — they must not contain quotes, backticks, $, ;, &, |, or whitespace. +_SAFE = re.compile(r"^[A-Za-z0-9_./:@\-]+$") +# `extras` (pip extras list) is the one field where commas are valid — it's +# a comma-separated list of extra-group names, each of which must itself be +# safe. +_SAFE_EXTRAS = re.compile(r"^[A-Za-z0-9_,\-]+$") +# `recipe` is a built-in YAML basename — no slashes, dots, or colons. Tight +# regex prevents path traversal (e.g. ../../etc/passwd) and shell tricks +# that the looser _SAFE pattern would let through. +_SAFE_RECIPE = re.compile(r"^[A-Za-z0-9_\-]+$") + + +def _check(field: str, value: str) -> None: + if field == "extras": + pattern = _SAFE_EXTRAS + allowed = "[A-Za-z0-9_,-]" + elif field == "recipe": + pattern = _SAFE_RECIPE + allowed = "[A-Za-z0-9_-]" + else: + pattern = _SAFE + allowed = "[A-Za-z0-9_./:@-]" + if not value or not pattern.match(value): + msg = f"cloud-init: refusing unsafe {field}={value!r} (allowed: {allowed})" + raise ValueError(msg) + + +def render( + *, + repo: str = "professor-codephreak/mindXtrain", + branch: str = "main", + container: str = "rocm/primus:v26.2", + extras: str = "ml,eval,data,obs", + remote_path: str = "/workspace/mindxtrain", + run_bench: bool = True, + recipe: str | None = None, +) -> str: + """Return a `#cloud-config` YAML payload ready for the `user_data` field. + + The script is idempotent on re-runs: cloud-init only fires `runcmd` on + first boot, but if a step is re-run manually it short-circuits via the + sentinel file. + + When `recipe` is provided, a `mindxtrain train` step runs after bench, + writes its exit code to `{TRAIN_EXIT_SENTINEL}` and touches + `{TRAIN_DONE_SENTINEL}` so the operator's SSH-tail bridge knows when to + stop streaming. Output is tee'd to a stable train.log path globbed by + the orchestrator (per-recipe run_name lives one directory deeper). + """ + _check("repo", repo) + _check("branch", branch) + _check("container", container) + _check("extras", extras) + _check("remote_path", remote_path) + if recipe is not None: + _check("recipe", recipe) + + bench_step = ( + f" - cd {remote_path} && podman run --rm " + f"--device /dev/kfd --device /dev/dri " + f"-v {remote_path}:{remote_path} -w {remote_path} " + f'{container} bash -lc "pip install -e .[{extras}] && ' + f'mindxtrain bench --gpu 0 --out plan.json"' + ) if run_bench else ( + f" - cd {remote_path} && podman run --rm " + f"--device /dev/kfd --device /dev/dri " + f"-v {remote_path}:{remote_path} -w {remote_path} " + f'{container} bash -lc "pip install -e .[{extras}]"' + ) + + if recipe is not None: + # The train step: + # 1. Runs `mindxtrain train` inside the container against the recipe + # that ships in-tree. + # 2. Tees output to {remote_path}/out/runs/<run_name>/train.log so + # the operator's SSH-tail can glob it. + # 3. Captures the wrapped exit code and persists both sentinels + # atomically. Note the outer shell captures podman's exit, not + # the pipeline's, so a pipe-broken tee doesn't mask a train fail. + train_step = ( + f" - cd {remote_path} && podman run --rm " + f"--device /dev/kfd --device /dev/dri " + f"-v {remote_path}:{remote_path} -w {remote_path} " + f'{container} bash -lc "mindxtrain train ' + f'mindxtrain/train/recipes/{recipe}.yaml --plan plan.json 2>&1 | ' + f'tee out/runs/_combined_train.log"; ' + f"echo $? > {TRAIN_EXIT_SENTINEL}; touch {TRAIN_DONE_SENTINEL}" + ) + else: + train_step = "" + + train_block = f"\n{train_step}" if train_step else "" + + return f"""#cloud-config +package_update: true +package_upgrade: false +packages: + - git + - podman + - podman-compose + +write_files: + - path: /etc/profile.d/mindxtrain.sh + permissions: '0755' + content: | + export MINDXTRAIN_HOME={remote_path} + +runcmd: + - mkdir -p /workspace + - test -d {remote_path}/.git || git clone --depth 1 --branch {branch} https://github.com/{repo}.git {remote_path} + - cd {remote_path} && podman image exists {container} || podman pull {container} +{bench_step} + - touch {BOOTSTRAP_SENTINEL}{train_block} + +final_message: "mindXtrain bootstrap complete (sentinel: {BOOTSTRAP_SENTINEL})" +""" + + +__all__ = [ + "BOOTSTRAP_SENTINEL", + "CLOUD_INIT_LOG", + "TRAIN_DONE_SENTINEL", + "TRAIN_EXIT_SENTINEL", + "TRAIN_LOG_GLOB", + "render", +] diff --git a/mindxtrain/deploy/droplet.py b/mindxtrain/deploy/droplet.py new file mode 100644 index 0000000000000000000000000000000000000000..87989b99d6499734870b6946a844e6639325fe94 --- /dev/null +++ b/mindxtrain/deploy/droplet.py @@ -0,0 +1,269 @@ +"""Existing-droplet sync builder (rsync + ssh + scp argv arrays). + +Pure builder. The orchestrator runs each step via +`mindxtrain.operator.runs.spawn_subprocess_streaming` so every command's +stdout streams back over the SSE pipeline. + +Argv is always list-form — never `shell=True` — so user-supplied env values +can't be turned into shell injection. The remote shell snippets are +single-string command bodies (because `ssh` itself joins them on the wire), +but the *arguments* to ssh are still argv elements. +""" + +from __future__ import annotations + +import os +import shutil +from dataclasses import dataclass +from pathlib import Path + +from mindxtrain.deploy.github_push import Step + +DEFAULT_REMOTE_PATH = "/workspace/mindxtrain" +DEFAULT_CONTAINER = "rocm/primus:v26.2" +DEFAULT_SSH_KEY = "~/.ssh/id_ed25519" + +# Common ssh hardening flags applied everywhere: +# - BatchMode=yes : never prompt for a password (would deadlock) +# - StrictHostKeyChecking=accept-new : trust on first use, refuse changes +# - ServerAliveInterval=30 : keep the socket open during long bench runs +_SSH_OPTS = ( + "-o", "BatchMode=yes", + "-o", "StrictHostKeyChecking=accept-new", + "-o", "ServerAliveInterval=30", +) + +# rsync exclude rules. .git intentionally excluded — the cloud-init path +# clones from GitHub; this rsync path syncs working-tree state for fast +# iteration on a droplet that was provisioned manually. +_DEFAULT_EXCLUDES = ( + ".git", + "__pycache__", + ".venv", + ".pytest_cache", + ".mypy_cache", + ".ruff_cache", + "node_modules", + "out/", + "dist/", + "build/", + "*.egg-info", +) + + +@dataclass(frozen=True) +class DropletConfig: + host: str + user: str = "root" + ssh_key: str = DEFAULT_SSH_KEY + remote_path: str = DEFAULT_REMOTE_PATH + container: str = DEFAULT_CONTAINER + extras: str = "ml,eval,data,obs" + + +def required_env() -> tuple[str, ...]: + """Env vars that must be set for /api/droplet/sync.""" + return ("DROPLET_HOST", "DROPLET_USER") + + +def missing_env(env: dict[str, str] | None = None) -> list[str]: + src = env if env is not None else os.environ + return [k for k in required_env() if not src.get(k)] + + +def _which_missing() -> list[str]: + out = [] + for binary in ("rsync", "ssh", "scp"): + if shutil.which(binary) is None: + out.append(binary) + return out + + +def status_missing(env: dict[str, str] | None = None) -> list[str]: + return missing_env(env) + _which_missing() + + +def status_target(env: dict[str, str] | None = None) -> str: + src = env if env is not None else os.environ + user = src.get("DROPLET_USER", "") + host = src.get("DROPLET_HOST", "") + path = src.get("DROPLET_REMOTE_PATH", DEFAULT_REMOTE_PATH) + if not (user and host): + return "" + return f"{user}@{host}:{path}" + + +def from_env(env: dict[str, str] | None = None) -> DropletConfig: + src = env if env is not None else os.environ + missing = missing_env(env) + if missing: + msg = f"droplet config missing env: {', '.join(missing)}" + raise RuntimeError(msg) + return DropletConfig( + host=src["DROPLET_HOST"], + user=src.get("DROPLET_USER", "root"), + ssh_key=src.get("DROPLET_SSH_KEY", DEFAULT_SSH_KEY), + remote_path=src.get("DROPLET_REMOTE_PATH", DEFAULT_REMOTE_PATH), + container=src.get("DROPLET_CONTAINER", DEFAULT_CONTAINER), + ) + + +# ---- argv builders ------------------------------------------------------- + +def build_rsync(cfg: DropletConfig, repo_root: Path) -> list[str]: + excludes: list[str] = [] + for pat in _DEFAULT_EXCLUDES: + excludes += ["--exclude", pat] + ssh_inline = ( + f"ssh -i {cfg.ssh_key} " + f"-o BatchMode=yes " + f"-o StrictHostKeyChecking=accept-new" + ) + # Trailing slashes matter — copy contents of repo_root into remote_path/. + return [ + "rsync", "-az", "--delete", "--info=progress2", + *excludes, + "-e", ssh_inline, + f"{repo_root.rstrip('/') if isinstance(repo_root, str) else str(repo_root).rstrip('/')}/", + f"{cfg.user}@{cfg.host}:{cfg.remote_path}/", + ] + + +def build_provision_ssh(cfg: DropletConfig) -> list[str]: + """Idempotent: install podman + pull the container image, skip if already done.""" + remote = ( + "command -v podman >/dev/null 2>&1 || " + "(sudo apt-get update && sudo apt-get install -y podman); " + f"podman image exists {cfg.container} || podman pull {cfg.container}" + ) + return ["ssh", "-i", cfg.ssh_key, *_SSH_OPTS, f"{cfg.user}@{cfg.host}", remote] + + +def build_bench_ssh(cfg: DropletConfig) -> list[str]: + """Run `mindxtrain bench --gpu 0 --out plan.json` inside the container. + + `-tt` forces a remote pty so SIGINT from the local ssh client propagates + through the SSH channel to `podman` and on to the GPU process. Without + `-tt`, the existing `_REGISTRY.cancel()` SIGINT/SIGTERM in + `mindxtrain.operator.runs:331` reaches only the local ssh, leaving the + remote GPU still spinning. + """ + remote = ( + f"cd {cfg.remote_path} && podman run --rm " + f"--device /dev/kfd --device /dev/dri " + f"-v {cfg.remote_path}:{cfg.remote_path} -w {cfg.remote_path} " + f"{cfg.container} bash -lc " + f"'pip install -e .[{cfg.extras}] && " + f"mindxtrain bench --gpu 0 --out plan.json'" + ) + return ["ssh", "-tt", "-i", cfg.ssh_key, *_SSH_OPTS, f"{cfg.user}@{cfg.host}", remote] + + +def build_scp_plan_back(cfg: DropletConfig, local_dest: Path) -> list[str]: + return [ + "scp", "-i", cfg.ssh_key, *_SSH_OPTS, + f"{cfg.user}@{cfg.host}:{cfg.remote_path}/plan.json", + str(local_dest), + ] + + +def build_ssh_probe(cfg: DropletConfig) -> list[str]: + """Quick liveness probe — `ssh user@host true`. Used to wait for the box + to come up after provisioning.""" + return ["ssh", "-i", cfg.ssh_key, *_SSH_OPTS, "-o", "ConnectTimeout=5", + f"{cfg.user}@{cfg.host}", "true"] + + +def build_tail_cloud_init(cfg: DropletConfig) -> list[str]: + """Tail cloud-init's combined output until the bootstrap sentinel exists. + + Streams the log live; exits 0 once the sentinel appears. The remote shell + here is a small loop, run via ssh's argv (not /bin/sh -c locally). + """ + from mindxtrain.deploy.cloud_init import BOOTSTRAP_SENTINEL, CLOUD_INIT_LOG + + remote = ( + f"touch {CLOUD_INIT_LOG}; " + f"tail -n +1 -F {CLOUD_INIT_LOG} & TAIL_PID=$!; " + f"while [ ! -f {BOOTSTRAP_SENTINEL} ]; do sleep 5; done; " + f"sleep 2; kill $TAIL_PID 2>/dev/null; true" + ) + return ["ssh", "-i", cfg.ssh_key, *_SSH_OPTS, f"{cfg.user}@{cfg.host}", remote] + + +def build_tail_training_log(cfg: DropletConfig) -> list[str]: + """Tail the droplet's `mindxtrain train` combined log until done sentinel. + + Streams every stdout line from the in-progress training run; exits with + the captured training exit code once `.train-done` appears. Used by the + orchestrator's post-bootstrap step to bridge remote training events into + the operator's run registry (where Coach's SSE picks them up). + + The training command tees its output to a stable path + (`out/runs/_combined_train.log`) so we don't have to guess the per-recipe + run_name from the operator side. + """ + from mindxtrain.deploy.cloud_init import ( + TRAIN_DONE_SENTINEL, + TRAIN_EXIT_SENTINEL, + ) + + combined_log = f"{cfg.remote_path}/out/runs/_combined_train.log" + remote = ( + # Make sure the log exists so tail -F doesn't error before training + # writes its first line. + f"mkdir -p {cfg.remote_path}/out/runs; " + f"touch {combined_log}; " + f"tail -n +1 -F {combined_log} & TAIL_PID=$!; " + # Block until training is terminal. + f"while [ ! -f {TRAIN_DONE_SENTINEL} ]; do sleep 5; done; " + # Give tail one more pass to flush. + f"sleep 2; kill $TAIL_PID 2>/dev/null; " + # Propagate the training exit code so the orchestrator can decide + # succeeded vs failed. Defaults to 1 if the sentinel is missing. + f"exit $(cat {TRAIN_EXIT_SENTINEL} 2>/dev/null || echo 1)" + ) + return ["ssh", "-i", cfg.ssh_key, *_SSH_OPTS, f"{cfg.user}@{cfg.host}", remote] + + +# ---- pipeline assembly --------------------------------------------------- + +def sync_steps( + cfg: DropletConfig, + repo_root: Path, + *, + run_bench: bool = True, + fetch_plan: bool = True, + plan_dest: Path | None = None, +) -> list[Step]: + steps: list[Step] = [ + Step(label="rsync", cmd=build_rsync(cfg, repo_root)), + Step(label="provision", cmd=build_provision_ssh(cfg)), + ] + if run_bench: + steps.append(Step(label="bench", cmd=build_bench_ssh(cfg))) + if fetch_plan and run_bench: + dest = plan_dest or repo_root / "out" / "plan.remote.json" + dest.parent.mkdir(parents=True, exist_ok=True) + steps.append(Step(label="scp-plan", cmd=build_scp_plan_back(cfg, dest))) + return steps + + +__all__ = [ + "DEFAULT_CONTAINER", + "DEFAULT_REMOTE_PATH", + "DEFAULT_SSH_KEY", + "DropletConfig", + "build_bench_ssh", + "build_provision_ssh", + "build_rsync", + "build_scp_plan_back", + "build_ssh_probe", + "build_tail_cloud_init", + "from_env", + "missing_env", + "required_env", + "status_missing", + "status_target", + "sync_steps", +] diff --git a/mindxtrain/deploy/github_push.py b/mindxtrain/deploy/github_push.py new file mode 100644 index 0000000000000000000000000000000000000000..28a51f5553a778e475b4e8dc94b5e1df5301788d --- /dev/null +++ b/mindxtrain/deploy/github_push.py @@ -0,0 +1,211 @@ +"""GitHub push step builder. + +Returns an idempotent list of `Step`s for bootstrapping a git repo, creating +the GitHub remote (via `gh`), and pushing the working tree. Pure builder — +no subprocesses are spawned here. The orchestrator in `_orchestrator.py` +runs the steps via `mindxtrain.operator.runs.spawn_subprocess_streaming` so +each command's stdout streams back over the existing SSE pipeline. + +Auth: `GITHUB_TOKEN` is forwarded into the subprocess env as `GH_TOKEN` (the +name `gh` looks for) and as `GITHUB_TOKEN` (for `git push` via the credential +helper). The token never lands in argv or `.git/config`. +""" + +from __future__ import annotations + +import os +import shutil +from dataclasses import dataclass, field +from pathlib import Path + + +@dataclass(frozen=True) +class Step: + """A single shell-out step in a deploy pipeline.""" + + label: str + cmd: list[str] + env: dict[str, str] = field(default_factory=dict) + # If `predicate_step` is set, this step only runs when that step's rc + # matches `predicate_rc_in`. Used to model conditional steps without + # building a full DAG. + predicate_step: str | None = None + predicate_rc_in: tuple[int, ...] = (0,) + # If `capture_stdout` is True, the orchestrator stashes the step's stdout + # under `Step.label` so later steps / callers can read it. + capture_stdout: bool = False + # If True, the orchestrator continues even on non-zero rc (used for probes). + allow_failure: bool = False + + +# Credential helper inline script — feeds the token to git push without +# touching ~/.gitconfig or .git/config. Single-quoted body so $GH_TOKEN is +# expanded by the spawned shell, not by Python's f-string. +_GIT_CRED_HELPER = ( + "credential.helper=" + "!f() { echo username=x-access-token; echo \"password=$GH_TOKEN\"; }; f" +) + + +@dataclass(frozen=True) +class GithubConfig: + token: str + repo: str # "owner/name" + branch: str = "main" + author_name: str = "mindXtrain bot" + author_email: str = "noreply@pythai.net" + + +def _step_env(token: str) -> dict[str, str]: + """Subprocess env: pass GH_TOKEN + GITHUB_TOKEN, scrub nothing else.""" + base = dict(os.environ) + base["GH_TOKEN"] = token + base["GITHUB_TOKEN"] = token + return base + + +def required_env() -> tuple[str, ...]: + """Names of env vars the operator must populate for /api/github/push.""" + return ("GITHUB_TOKEN", "GITHUB_REPO") + + +def missing_env(env: dict[str, str] | None = None) -> list[str]: + src = env if env is not None else os.environ + return [k for k in required_env() if not src.get(k)] + + +def _which_missing() -> list[str]: + """Binaries the push pipeline depends on, but only if they're absent.""" + out = [] + for binary in ("git", "gh"): + if shutil.which(binary) is None: + out.append(binary) + return out + + +def status_target(env: dict[str, str] | None = None) -> str: + src = env if env is not None else os.environ + return src.get("GITHUB_REPO", "") + + +def status_missing(env: dict[str, str] | None = None) -> list[str]: + """Combine env-missing + binary-missing for the /status endpoint.""" + return missing_env(env) + _which_missing() + + +def bootstrap_steps( + cfg: GithubConfig, + *, + commit_message: str = "mindXtrain initial push", + force: bool = False, +) -> list[Step]: + """Idempotent step list. The orchestrator skips conditional steps by + inspecting earlier steps' rcs.""" + env = _step_env(cfg.token) + repo_url = f"https://github.com/{cfg.repo}.git" + push_cmd = [ + "git", + "-c", _GIT_CRED_HELPER, + "push", "-u", "origin", f"HEAD:{cfg.branch}", + ] + if force: + push_cmd.append("--force-with-lease") + + return [ + Step( + label="probe-git", + cmd=["git", "rev-parse", "--git-dir"], + env=env, + allow_failure=True, + ), + Step( + label="git-init", + cmd=["git", "init", "-b", cfg.branch], + env=env, + predicate_step="probe-git", + predicate_rc_in=(1, 128), # not a git repo + ), + Step( + label="probe-repo", + cmd=["gh", "repo", "view", cfg.repo], + env=env, + allow_failure=True, + ), + Step( + label="gh-create", + cmd=["gh", "repo", "create", cfg.repo, "--public", + "--source=.", "--remote=origin"], + env=env, + predicate_step="probe-repo", + predicate_rc_in=(1,), # repo doesn't exist yet + ), + Step( + label="probe-remote", + cmd=["git", "remote", "get-url", "origin"], + env=env, + allow_failure=True, + capture_stdout=True, + ), + Step( + # If origin already exists but doesn't match, only rewrite when force=True. + # The orchestrator emits a guidance LogEvent + bails when force=False + # and probe-remote stdout doesn't match repo_url. + label="git-remote-add", + cmd=["git", "remote", "add", "origin", repo_url], + env=env, + predicate_step="probe-remote", + predicate_rc_in=(1, 2, 128), # remote not configured + ), + Step(label="git-add", cmd=["git", "add", "-A"], env=env), + Step( + label="probe-stage", + cmd=["git", "diff", "--cached", "--quiet"], + env=env, + allow_failure=True, + ), + Step( + label="git-commit", + cmd=[ + "git", + "-c", f"user.email={cfg.author_email}", + "-c", f"user.name={cfg.author_name}", + "commit", "-m", commit_message, + ], + env=env, + predicate_step="probe-stage", + predicate_rc_in=(1,), # rc=1 means "differences exist" → there's something to commit + ), + Step(label="git-push", cmd=push_cmd, env=env), + Step( + label="head-sha", + cmd=["git", "rev-parse", "HEAD"], + env=env, + capture_stdout=True, + ), + ] + + +def remote_url(repo: str) -> str: + return f"https://github.com/{repo}.git" + + +def write_sha_file(out_dir: Path, sha: str) -> Path: + """Persist the captured HEAD SHA so emit_receipt callers can pin + `manifest.git_sha` without re-shelling git.""" + out_dir.mkdir(parents=True, exist_ok=True) + target = out_dir / "git_sha.txt" + target.write_text(sha.strip() + "\n") + return target + + +__all__ = [ + "GithubConfig", + "Step", + "bootstrap_steps", + "missing_env", + "remote_url", + "required_env", + "status_missing", + "status_target", + "write_sha_file", +] diff --git a/mindxtrain/deploy/gptq_rocm.py b/mindxtrain/deploy/gptq_rocm.py new file mode 100644 index 0000000000000000000000000000000000000000..54a77ce122fa80fec32732c81b224bd2e4884232 --- /dev/null +++ b/mindxtrain/deploy/gptq_rocm.py @@ -0,0 +1,45 @@ +"""GPTQ quantization on ROCm — alternative to Quark FP8. + +Subprocess wrapper around `python -m auto_gptq` (ROCm wheels available at +https://huggingface.github.io/autogptq-index/whl/rocm573/). Prefer +`mindxtrain.deploy.quark.quark_fp8` for MI300X — keep this around as a +fallback for older ROCm versions that don't have FP8 support. +""" + +from __future__ import annotations + +import importlib.util +import subprocess +from pathlib import Path + + +def _autogptq_available() -> bool: + return importlib.util.find_spec("auto_gptq") is not None + + +def gptq_rocm(in_dir: Path, out_dir: Path, bits: int = 4) -> Path: + """Quantize an HF checkpoint to GPTQ-`bits` weights; return out_dir.""" + if not _autogptq_available(): + msg = ( + "auto-gptq not installed; install the ROCm wheel from " + "https://huggingface.github.io/autogptq-index/whl/rocm573/auto_gptq/ " + "or use mindxtrain.deploy.quark.quark_fp8 instead." + ) + raise RuntimeError(msg) + out_dir.mkdir(parents=True, exist_ok=True) + cmd = [ + "python", + "-m", + "auto_gptq", + "--model_name_or_path", + str(in_dir), + "--output_dir", + str(out_dir), + "--bits", + str(bits), + ] + subprocess.run(cmd, check=True) + return out_dir + + +__all__ = ["gptq_rocm"] diff --git a/mindxtrain/deploy/hot_swap.py b/mindxtrain/deploy/hot_swap.py new file mode 100644 index 0000000000000000000000000000000000000000..56f5e525f12cf1166041e463a03f8f4683bd70e6 --- /dev/null +++ b/mindxtrain/deploy/hot_swap.py @@ -0,0 +1,64 @@ +"""Atomic hot-swap with canary — promote canary -> live, rollback to previous. + +Pure registry-state mutation; the actual vLLM router slot rotation happens +in the inference layer (out of scope for the hackathon MVP). +""" + +from __future__ import annotations + +from datetime import UTC, datetime +from pathlib import Path + +from mindxtrain.deploy.registry import DeployRegistry, Slot + + +def promote_canary_to_live(registry_path: Path) -> Slot: + """Promote `canary` -> `live` atomically; retain previous live for rollback. + + Returns the new live Slot. Raises if no canary is staged. + """ + reg = DeployRegistry(registry_path) + canary = reg.get("canary") + if canary is None: + msg = "no canary slot to promote" + raise RuntimeError(msg) + current_live = reg.get("live") + if current_live is not None: + reg.set( + Slot( + name="previous_live", + run_id=current_live.run_id, + blake3=current_live.blake3, + activated_at=current_live.activated_at, + ) + ) + new_live = Slot( + name="live", + run_id=canary.run_id, + blake3=canary.blake3, + activated_at=datetime.now(tz=UTC), + ) + reg.set(new_live) + reg.clear("canary") + return new_live + + +def rollback_live(registry_path: Path) -> Slot: + """Roll `live` back to `previous_live`. Raises if no rollback target exists.""" + reg = DeployRegistry(registry_path) + prev = reg.get("previous_live") + if prev is None: + msg = "no previous_live slot — nothing to roll back to" + raise RuntimeError(msg) + new_live = Slot( + name="live", + run_id=prev.run_id, + blake3=prev.blake3, + activated_at=datetime.now(tz=UTC), + ) + reg.set(new_live) + reg.clear("previous_live") + return new_live + + +__all__ = ["promote_canary_to_live", "rollback_live"] diff --git a/mindxtrain/deploy/modelfile.py b/mindxtrain/deploy/modelfile.py new file mode 100644 index 0000000000000000000000000000000000000000..aa4d110d2158a5153a1609d29943d54298f706b2 --- /dev/null +++ b/mindxtrain/deploy/modelfile.py @@ -0,0 +1,177 @@ +"""Ollama Modelfile builder. + +Render a valid Ollama `Modelfile` from a typed spec, and expose the full parameter +catalogue so a UI can build toggles + input fields dynamically. Covers every +instruction (https://docs.ollama.com/modelfile): FROM, PARAMETER, TEMPLATE, SYSTEM, +ADAPTER, LICENSE, MESSAGE, REQUIRES. + +Pure stdlib + pydantic; base-install importable. `create_model` (optional) shells out +to `ollama create` and is the only part needing the ollama binary. +""" + +from __future__ import annotations + +import subprocess +from pathlib import Path +from typing import Literal + +from pydantic import BaseModel, ConfigDict, Field + +ParamType = Literal["int", "float", "string", "bool"] + + +class ParamSpec(BaseModel): + """Metadata for one PARAMETER — drives the UI toggle + input field.""" + + model_config = ConfigDict(extra="forbid", frozen=True) + + name: str + type: ParamType + default: float | int | str | None = None + minimum: float | None = None + maximum: float | None = None + description: str = "" + + +# The full PARAMETER catalogue. Defaults follow Ollama's documented values. +MODELFILE_PARAMS: tuple[ParamSpec, ...] = ( + ParamSpec(name="num_ctx", type="int", default=2048, minimum=64, description="context window size (tokens)"), + ParamSpec(name="num_predict", type="int", default=-1, description="max tokens to predict (-1 = infinite)"), + ParamSpec(name="num_keep", type="int", default=0, description="tokens kept from the initial prompt"), + ParamSpec(name="seed", type="int", default=0, description="RNG seed for reproducible output"), + ParamSpec(name="temperature", type="float", default=0.8, minimum=0.0, maximum=2.0, description="creativity / randomness"), + ParamSpec(name="top_k", type="int", default=40, minimum=0, description="sample from the top-k tokens"), + ParamSpec(name="top_p", type="float", default=0.9, minimum=0.0, maximum=1.0, description="nucleus sampling cumulative prob"), + ParamSpec(name="min_p", type="float", default=0.0, minimum=0.0, maximum=1.0, description="min relative token probability"), + ParamSpec(name="typical_p", type="float", default=1.0, minimum=0.0, maximum=1.0, description="locally-typical sampling"), + ParamSpec(name="repeat_last_n", type="int", default=64, description="lookback for repeat penalty (-1 = num_ctx)"), + ParamSpec(name="repeat_penalty", type="float", default=1.1, minimum=0.0, description="penalty strength for repetition"), + ParamSpec(name="presence_penalty", type="float", default=0.0, description="penalize tokens already present"), + ParamSpec(name="frequency_penalty", type="float", default=0.0, description="penalize by token frequency"), + ParamSpec(name="mirostat", type="int", default=0, minimum=0, maximum=2, description="Mirostat sampling (0 off, 1 v1, 2 v2)"), + ParamSpec(name="mirostat_tau", type="float", default=5.0, minimum=0.0, description="Mirostat target entropy"), + ParamSpec(name="mirostat_eta", type="float", default=0.1, minimum=0.0, description="Mirostat learning rate"), + ParamSpec(name="num_gpu", type="int", default=-1, description="layers to offload to GPU (-1 = auto)"), + ParamSpec(name="num_thread", type="int", default=0, description="CPU threads (0 = auto)"), + ParamSpec(name="num_batch", type="int", default=512, description="prompt-processing batch size"), + ParamSpec(name="draft_num_predict", type="int", default=4, description="speculative draft tokens"), +) + +_PARAM_TYPES: dict[str, ParamType] = {p.name: p.type for p in MODELFILE_PARAMS} +_VALID_ROLES = {"system", "user", "assistant"} + + +class ModelfileMessage(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + role: Literal["system", "user", "assistant"] + content: str + + +class ModelfileSpec(BaseModel): + """A typed Modelfile spec. Only `from_model` is required.""" + + model_config = ConfigDict(extra="forbid") + + from_model: str = Field(description="base model or path (the FROM instruction)") + system: str = "" + template: str = "" + adapter: str = "" + license: str = "" + requires: str = Field(default="", description="minimum Ollama version (REQUIRES)") + parameters: dict[str, float | int | str] = Field(default_factory=dict) + stop: list[str] = Field(default_factory=list, description="stop sequences (PARAMETER stop)") + messages: list[ModelfileMessage] = Field(default_factory=list) + + +def _fmt_value(name: str, value: float | int | str) -> str: + """Format a PARAMETER value; quote strings that contain whitespace.""" + declared = _PARAM_TYPES.get(name) + if declared in ("int",) and isinstance(value, float) and value.is_integer(): + value = int(value) + if isinstance(value, str): + return f'"{value}"' if (not value or any(c.isspace() for c in value)) else value + return str(value) + + +def _block(value: str) -> str: + """Render a multi-line value as a triple-quoted block, else inline.""" + if "\n" in value or '"' in value: + return f'"""{value}"""' + return f'"""{value}"""' if value else '""' + + +def render_modelfile(spec: ModelfileSpec) -> str: + """Render a valid Modelfile text from the spec (deterministic field order).""" + lines: list[str] = [f"FROM {spec.from_model}"] + + if spec.requires: + lines.append(f"REQUIRES {spec.requires}") + + # PARAMETERs in catalogue order, then any extras, then stop sequences. + ordered = [p.name for p in MODELFILE_PARAMS if p.name in spec.parameters] + extras = [k for k in spec.parameters if k not in _PARAM_TYPES] + for name in (*ordered, *sorted(extras)): + lines.append(f"PARAMETER {name} {_fmt_value(name, spec.parameters[name])}") + for stop in spec.stop: + lines.append(f'PARAMETER stop "{stop}"') + + if spec.system: + lines.append(f"SYSTEM {_block(spec.system)}") + if spec.template: + lines.append(f"TEMPLATE {_block(spec.template)}") + if spec.adapter: + lines.append(f"ADAPTER {spec.adapter}") + if spec.license: + lines.append(f"LICENSE {_block(spec.license)}") + for m in spec.messages: + # MESSAGE content is single-line in the instruction; collapse newlines. + content = m.content.replace("\n", " ").strip() + lines.append(f"MESSAGE {m.role} {content}") + + return "\n".join(lines) + "\n" + + +def write_modelfile(spec: ModelfileSpec, out_path: str | Path) -> Path: + """Render + write a Modelfile to disk; returns the path.""" + out = Path(out_path).expanduser() + out.parent.mkdir(parents=True, exist_ok=True) + out.write_text(render_modelfile(spec)) + return out + + +def create_model( + tag: str, + spec: ModelfileSpec, + *, + out_dir: str | Path = "./out/modelfiles", + ollama_bin: str = "ollama", +) -> dict[str, str]: + """Write the Modelfile and run `ollama create <tag> -f <Modelfile>`. + + Returns `{tag, modelfile, status, output}`. Never raises on a failed + `ollama create` — the failure is reported in the return dict. + """ + path = write_modelfile(spec, Path(out_dir) / tag / "Modelfile") + try: + proc = subprocess.run( + [ollama_bin, "create", tag, "-f", str(path)], + capture_output=True, text=True, timeout=600, check=False, + ) + except (OSError, subprocess.SubprocessError) as exc: + return {"tag": tag, "modelfile": str(path), "status": "error", "output": str(exc)} + status = "created" if proc.returncode == 0 else "failed" + return { + "tag": tag, "modelfile": str(path), "status": status, + "output": (proc.stdout + proc.stderr).strip()[-2000:], + } + + +__all__ = [ + "MODELFILE_PARAMS", + "ModelfileMessage", + "ModelfileSpec", + "ParamSpec", + "create_model", + "render_modelfile", + "write_modelfile", +] diff --git a/mindxtrain/deploy/ollama_push.py b/mindxtrain/deploy/ollama_push.py new file mode 100644 index 0000000000000000000000000000000000000000..a4cb85bb5c3336ab25c553c6a56e938f4dba8c2f --- /dev/null +++ b/mindxtrain/deploy/ollama_push.py @@ -0,0 +1,229 @@ +"""Push a trained LoRA adapter to a local ollama daemon. + +Closes the local-learning loop: a CPU/MI300X training run produces a +PEFT adapter under `<run_dir>/checkpoint/`; this module merges it into +the base weights, writes a Modelfile, and calls `ollama create` so the +tag is immediately servable on the same host the operator runs on. + +Ollama 0.13+ accepts `FROM <safetensors-directory>` natively for Llama, +Mistral, Gemma, Qwen, and Phi architectures — so we don't need +llama.cpp for the supported families that mindXtrain trains. The merged +HF directory is the canonical artefact; ollama internalizes it on +`create`. + +Lazy-imports `peft` / `transformers` so `import mindxtrain.deploy` stays +cheap on the CPU-only base install. Callers must opt into `--extra ml`. +""" + +from __future__ import annotations + +import shutil +import subprocess +from collections.abc import Callable +from dataclasses import dataclass +from pathlib import Path + + +@dataclass(frozen=True) +class OllamaPushResult: + tag: str + merged_dir: Path + modelfile: Path + ollama_stdout: str + # When push_to_ollama was called with register_with_mindx=True and + # the PATCH succeeded, this holds the mindX response + # ({previous, current, ...}). None when registration was skipped or + # failed (failure logs through `sink` but does not abort the push — + # the merged + tagged artefact still lands locally). + mindx_fallback_swap: dict[str, str] | None = None + + +def merge_lora_adapter( + base_model: str, + adapter_dir: Path, + out_dir: Path, + *, + sink: Callable[[str], None] | None = None, +) -> Path: + """Merge a PEFT LoRA adapter into the base weights. + + Writes the resulting full-precision HF directory to `out_dir`. The + directory is suitable as the `FROM` target of an ollama Modelfile. + + Raises ImportError if the `ml` extras aren't installed; the message + points the user at the exact `uv sync` command. + """ + _emit = sink or (lambda _line: None) + + try: + from peft import PeftModel # type: ignore + from transformers import AutoModelForCausalLM, AutoTokenizer # type: ignore + except ImportError as exc: + msg = ( + "push-to-ollama needs `peft` + `transformers`. install with: " + "uv sync --extra ml" + ) + raise ImportError(msg) from exc + + _emit(f"[push-ollama] loading base model: {base_model}") + base = AutoModelForCausalLM.from_pretrained(base_model) + _emit(f"[push-ollama] applying adapter from {adapter_dir}") + merged = PeftModel.from_pretrained(base, str(adapter_dir)).merge_and_unload() + + out_dir.mkdir(parents=True, exist_ok=True) + _emit(f"[push-ollama] saving merged weights to {out_dir}") + merged.save_pretrained(str(out_dir), safe_serialization=True) + + # Carry the tokenizer too — ollama needs it for chat templating. + tokenizer = AutoTokenizer.from_pretrained(str(adapter_dir)) + tokenizer.save_pretrained(str(out_dir)) + _emit(f"[push-ollama] merged dir ready: {out_dir}") + return out_dir + + +def write_modelfile( + merged_dir: Path, + out_path: Path, + *, + system_prompt: str | None = None, + template: str | None = None, + parameters: dict[str, float | int | str] | None = None, +) -> Path: + """Write an ollama Modelfile pointing at `merged_dir`. + + Minimal contract: `FROM <merged_dir>` plus optional SYSTEM, TEMPLATE, + and PARAMETER stanzas. Ollama's docs are at + https://github.com/ollama/ollama/blob/main/docs/modelfile.md — the + safetensors-directory path is what ollama 0.13+ resolves natively. + """ + lines: list[str] = [f"FROM {merged_dir}"] + if system_prompt: + # SYSTEM blocks support triple-quoted multiline payloads. + lines.append(f'SYSTEM """{system_prompt}"""') + if template: + lines.append(f'TEMPLATE """{template}"""') + for key, value in (parameters or {}).items(): + # Strings need quoting; numbers don't. + rendered = f'"{value}"' if isinstance(value, str) else value + lines.append(f"PARAMETER {key} {rendered}") + out_path.parent.mkdir(parents=True, exist_ok=True) + out_path.write_text("\n".join(lines) + "\n", encoding="utf-8") + return out_path + + +def ollama_create( + tag: str, + modelfile: Path, + *, + sink: Callable[[str], None] | None = None, + ollama_bin: str | None = None, + timeout_s: float = 1800.0, +) -> str: + """Run `ollama create <tag> -f <modelfile>` and return its stdout. + + Raises FileNotFoundError if the ollama CLI isn't on PATH and no + explicit `ollama_bin` is provided. Raises CalledProcessError if + ollama returns non-zero (caller surfaces stderr to the user). + """ + _emit = sink or (lambda _line: None) + + binary = ollama_bin or shutil.which("ollama") + if not binary: + msg = ( + "`ollama` CLI not found on PATH. install from https://ollama.com " + "or pass --ollama-bin to point at a custom build" + ) + raise FileNotFoundError(msg) + + cmd = [binary, "create", tag, "-f", str(modelfile)] + _emit(f"[push-ollama] $ {' '.join(cmd)}") + result = subprocess.run( + cmd, + check=True, + capture_output=True, + text=True, + timeout=timeout_s, + ) + if result.stdout: + _emit(result.stdout.rstrip()) + if result.stderr: + # Ollama prints progress to stderr — surface it for visibility. + _emit(result.stderr.rstrip()) + return result.stdout + + +def push_to_ollama( + base_model: str, + adapter_dir: Path, + tag: str, + *, + work_dir: Path | None = None, + system_prompt: str | None = None, + template: str | None = None, + parameters: dict[str, float | int | str] | None = None, + sink: Callable[[str], None] | None = None, + ollama_bin: str | None = None, + register_with_mindx: bool = False, + mindx_base_url: str | None = None, +) -> OllamaPushResult: + """End-to-end: merge LoRA → Modelfile → ollama create. + + `work_dir` defaults to `<adapter_dir>/../ollama_push/`. The merged HF + directory lives at `work_dir/merged/`; the Modelfile at + `work_dir/Modelfile`. Both are kept around so the operator can re-run + `ollama create` without redoing the merge. + + When `register_with_mindx=True`, after a successful `ollama create` + we PATCH `<mindx_base_url>/v1/config/fallback-model` with + `{provider: "ollama", model: <tag>}` so the freshly pushed tag + becomes mindX's local fallback model. The PATCH is best-effort — + a failure logs through `sink` and lands in `OllamaPushResult` as + `mindx_fallback_swap=None`, but does NOT raise. The point of this + flag is "close the dream → train → fallback loop without manual + intervention"; a stopped mindX daemon shouldn't kill the push. + """ + work = work_dir or adapter_dir.parent / "ollama_push" + merged_dir = merge_lora_adapter( + base_model, adapter_dir, work / "merged", sink=sink, + ) + modelfile = write_modelfile( + merged_dir, work / "Modelfile", + system_prompt=system_prompt, template=template, parameters=parameters, + ) + stdout = ollama_create(tag, modelfile, sink=sink, ollama_bin=ollama_bin) + + swap_result: dict[str, str] | None = None + if register_with_mindx: + _emit = sink or (lambda _line: None) + try: + # Lazy-imported so the deploy module stays importable on + # hosts without the publish-side dep tree warmed up. + from mindxtrain.deploy.api_client import swap_mindx_fallback_model + + _emit(f"[push-ollama] registering {tag} with mindX as fallback") + swap_result = swap_mindx_fallback_model( + provider="ollama", model=tag, api_url=mindx_base_url, + ) + _emit( + f"[push-ollama] mindX swap: " + f"{swap_result.get('previous', '?')} -> " + f"{swap_result.get('current', '?')}", + ) + except Exception as exc: + # Swallow — see docstring rationale. + _emit(f"[push-ollama] mindX registration failed (push still ok): {exc}") + swap_result = None + + return OllamaPushResult( + tag=tag, merged_dir=merged_dir, modelfile=modelfile, + ollama_stdout=stdout, mindx_fallback_swap=swap_result, + ) + + +__all__ = [ + "OllamaPushResult", + "merge_lora_adapter", + "ollama_create", + "push_to_ollama", + "write_modelfile", +] diff --git a/mindxtrain/deploy/quark.py b/mindxtrain/deploy/quark.py new file mode 100644 index 0000000000000000000000000000000000000000..ad730eca14fe04e28ae34568fb822bea9e164b1c --- /dev/null +++ b/mindxtrain/deploy/quark.py @@ -0,0 +1,59 @@ +"""AMD Quark quantization — FP8 (E4M3) for MI300X, MXFP4 for MI350X+. + +Subprocess wrapper around `python -m amd_quark.quantize`. The actual +amd-quark Python module ships in the rocm/primus container (or the AMD +Developer Cloud image); on a CPU-only dev box it's not installed and the +function returns a clear error pointing the user to the container. + +Outputs a vLLM-loadable directory: `config.json`, `*.safetensors` with FP8 +scales, tokenizer files, `generation_config.json`. +""" + +from __future__ import annotations + +import importlib.util +import shutil +import subprocess +from pathlib import Path +from typing import Literal + + +def _quark_available() -> bool: + return importlib.util.find_spec("amd_quark") is not None or shutil.which("quark") is not None + + +def _run_quark(in_dir: Path, out_dir: Path, scheme: Literal["fp8_e4m3", "mxfp4"]) -> Path: + if not _quark_available(): + msg = ( + "amd-quark not installed; run inside the rocm/primus:v26.2 " + "container or install per " + "https://quark.docs.amd.com/. (scheme=" + scheme + ")" + ) + raise RuntimeError(msg) + out_dir.mkdir(parents=True, exist_ok=True) + cmd = [ + "python", + "-m", + "amd_quark.quantize", + "--model_dir", + str(in_dir), + "--output_dir", + str(out_dir), + "--scheme", + scheme, + ] + subprocess.run(cmd, check=True) + return out_dir + + +def quark_fp8(in_dir: Path, out_dir: Path) -> Path: + """Quantize an HF checkpoint to FP8 E4M3; return out_dir (vLLM-loadable).""" + return _run_quark(in_dir, out_dir, "fp8_e4m3") + + +def quark_mxfp4(in_dir: Path, out_dir: Path) -> Path: + """Quantize an HF checkpoint to MXFP4 (CDNA 4 / MI350X+); return out_dir.""" + return _run_quark(in_dir, out_dir, "mxfp4") + + +__all__ = ["quark_fp8", "quark_mxfp4"] diff --git a/mindxtrain/deploy/registry.py b/mindxtrain/deploy/registry.py new file mode 100644 index 0000000000000000000000000000000000000000..b1bbee017a08d1da56094e71c5d39ce4489bb4e1 --- /dev/null +++ b/mindxtrain/deploy/registry.py @@ -0,0 +1,82 @@ +"""Model-version registry — content-addressed deploy slots. + +JSON-file-backed registry with atomic writes (`os.replace`). Three slots: +`live`, `staged`, `canary`. Pure stdlib. + +The registry tracks (run_id, blake3, activated_at) per slot plus a +`previous_live` slot so `mindxtrain.deploy.hot_swap.rollback_live` is a +single-step operation. +""" + +from __future__ import annotations + +import os +import tempfile +from datetime import UTC, datetime +from pathlib import Path +from typing import Literal + +from pydantic import BaseModel, ConfigDict, Field + +SlotName = Literal["live", "staged", "canary", "previous_live"] + + +class Slot(BaseModel): + model_config = ConfigDict(extra="forbid") + + name: SlotName + run_id: str + blake3: str + activated_at: datetime = Field(default_factory=lambda: datetime.now(tz=UTC)) + + +class RegistryState(BaseModel): + model_config = ConfigDict(extra="forbid") + + schema_version: Literal["1"] = "1" + slots: dict[str, Slot] = Field(default_factory=dict) + + +class DeployRegistry: + """Atomic JSON-file-backed deploy registry.""" + + def __init__(self, path: Path) -> None: + self.path = Path(path) + self.path.parent.mkdir(parents=True, exist_ok=True) + + def _read(self) -> RegistryState: + if not self.path.exists(): + return RegistryState() + return RegistryState.model_validate_json(self.path.read_text()) + + def _write(self, state: RegistryState) -> None: + # Atomic write: tmp file in same directory, then os.replace. + with tempfile.NamedTemporaryFile( + "w", + dir=self.path.parent, + delete=False, + prefix=f".{self.path.name}.", + suffix=".tmp", + ) as tmp: + tmp.write(state.model_dump_json(indent=2)) + tmp_path = Path(tmp.name) + os.replace(tmp_path, self.path) + + def get(self, slot: SlotName) -> Slot | None: + return self._read().slots.get(slot) + + def set(self, slot: Slot) -> None: + state = self._read() + state.slots[slot.name] = slot + self._write(state) + + def clear(self, slot: SlotName) -> None: + state = self._read() + state.slots.pop(slot, None) + self._write(state) + + def all(self) -> dict[str, Slot]: + return dict(self._read().slots) + + +__all__ = ["DeployRegistry", "RegistryState", "Slot", "SlotName"] diff --git a/mindxtrain/deploy/sglang_rocm.py b/mindxtrain/deploy/sglang_rocm.py new file mode 100644 index 0000000000000000000000000000000000000000..f29341f6da35f8e84a928ecb2b34db3cb0b8fc90 --- /dev/null +++ b/mindxtrain/deploy/sglang_rocm.py @@ -0,0 +1,29 @@ +"""SGLang-ROCm launcher — alternative to vLLM for serving. + +Wired into `mindxtrain serve --to sglang`. SGLang ships explicit +`rocm/sgl-dev:v0.5.8.post1-rocm720-mi30x` images. Pairs with the Mooncake +distributed-KV-cache plugin used by AMD/Xiaomi MiMo-V2.5-Pro. +""" + +from __future__ import annotations + +from pathlib import Path + +from mindxtrain.config.schema import ServeCfg + + +def build_sglang_command(cfg: ServeCfg, model_dir: Path) -> list[str]: + """Return argv for booting SGLang-ROCm against a quantized checkpoint.""" + return [ + "python", + "-m", + "sglang.launch_server", + "--model-path", + str(model_dir), + "--port", + str(cfg.port), + "--mem-fraction-static", + "0.85", + "--tp", + str(cfg.tensor_parallel), + ] diff --git a/mindxtrain/deploy/vllm_launcher.py b/mindxtrain/deploy/vllm_launcher.py new file mode 100644 index 0000000000000000000000000000000000000000..15338ad714d4e3a398fc26fe5cd4bb1594a663c0 --- /dev/null +++ b/mindxtrain/deploy/vllm_launcher.py @@ -0,0 +1,42 @@ +"""Build the `vllm serve` command line from a ServeCfg + QuantizeCfg. + +Real implementation (Day 5 wiring is just os.execvp on this list). +""" + +from __future__ import annotations + +from pathlib import Path + +from mindxtrain.config.schema import QuantizeCfg, ServeCfg + + +def build_vllm_command( + cfg: ServeCfg, + model_dir: Path, + quantize: QuantizeCfg | None = None, +) -> list[str]: + """Return argv for booting vLLM-ROCm against a quantized checkpoint. + + The serving-time quantization flag mirrors `quantize.scheme` from the + full XTrainConfig. Pass `quantize=None` to omit the flag (treat the + checkpoint as the dtype on disk). + """ + cmd = [ + "vllm", + "serve", + str(model_dir), + "--tensor-parallel-size", + str(cfg.tensor_parallel), + "--max-model-len", + str(cfg.max_model_len), + "--port", + str(cfg.port), + ] + if quantize is not None and quantize.scheme != "none": + if quantize.scheme == "quark_fp8": + cmd += ["--quantization", "fp8"] + elif quantize.scheme == "quark_mxfp4": + cmd += ["--quantization", "mxfp4"] + elif quantize.scheme == "gptq_rocm": + cmd += ["--quantization", "gptq"] + return cmd diff --git a/mindxtrain/eval/__init__.py b/mindxtrain/eval/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/mindxtrain/eval/agenda_regression.py b/mindxtrain/eval/agenda_regression.py new file mode 100644 index 0000000000000000000000000000000000000000..dd039ff1fd05dab6d6ad3ffd9c5b8a3de8674779 --- /dev/null +++ b/mindxtrain/eval/agenda_regression.py @@ -0,0 +1,92 @@ +"""Agenda regression — does the model still respect the configured agenda? + +Two scoring modes, additive: + - keyword overlap: fraction of agenda keywords mentioned in samples (cheap). + - LLM judge: optionally calls a vLLM-compatible endpoint via httpx with a + strict yes/no rubric; aggregates judgements into a [0,1] score. +""" + +from __future__ import annotations + +import json +import os +import re + +import httpx + +_WORD_RE = re.compile(r"\b[\w'-]+\b", re.UNICODE) + + +def _keyword_overlap(samples: list[str], agenda: str) -> float: + keywords = {w.lower() for w in _WORD_RE.findall(agenda) if len(w) > 3} + if not keywords or not samples: + return 0.0 + hits = 0 + for s in samples: + words = {w.lower() for w in _WORD_RE.findall(s)} + if keywords & words: + hits += 1 + return hits / len(samples) + + +def _llm_judge(samples: list[str], agenda: str, *, base_url: str, model: str, timeout_s: float) -> float: + if not samples: + return 0.0 + prompt_template = ( + "You are a strict reviewer. Agenda:\n{agenda}\n\n" + "Sample:\n{sample}\n\n" + "Does the sample respect the agenda? Reply with exactly YES or NO." + ) + yes = 0 + with httpx.Client(timeout=timeout_s) as client: + for sample in samples: + body = { + "model": model, + "messages": [ + { + "role": "user", + "content": prompt_template.format(agenda=agenda, sample=sample), + } + ], + "max_tokens": 6, + "temperature": 0.0, + } + try: + resp = client.post(f"{base_url}/chat/completions", json=body) + resp.raise_for_status() + data = resp.json() + content = ((data.get("choices") or [{}])[0].get("message") or {}).get("content", "") + if "YES" in content.upper(): + yes += 1 + except (httpx.HTTPError, json.JSONDecodeError): + continue + return yes / len(samples) + + +def regression_score( + samples: list[str], + agenda: str, + *, + judge_base_url: str | None = None, + judge_model: str = "Qwen/Qwen3.5-8B", + judge_weight: float = 0.7, + timeout_s: float = 30.0, +) -> float: + """Return agenda-conformance score in [0, 1]. + + `keyword_overlap` is always computed; `llm_judge` is optionally added if + `judge_base_url` (or `MINDXTRAIN_TEACHER_BASE_URL` env) is set. The two + are blended by `judge_weight` (judge) and `1 - judge_weight` (keywords). + """ + base = _keyword_overlap(samples, agenda) + judge_url = ( + judge_base_url + or os.environ.get("MINDXTRAIN_TEACHER_BASE_URL", "") + ).rstrip("/") + if not judge_url: + return base + judge = _llm_judge(samples, agenda, base_url=judge_url, model=judge_model, timeout_s=timeout_s) + return judge_weight * judge + (1 - judge_weight) * base + + +__all__ = ["regression_score"] diff --git a/mindxtrain/eval/bfcl.py b/mindxtrain/eval/bfcl.py new file mode 100644 index 0000000000000000000000000000000000000000..698daadad96570aa6c9fba6c491e48c17c92ca37 --- /dev/null +++ b/mindxtrain/eval/bfcl.py @@ -0,0 +1,56 @@ +"""BFCL — Berkeley Function-Calling Leaderboard adapter. + +Subprocess `bfcl evaluate`. Lazy availability check. +""" + +from __future__ import annotations + +import json +import shutil +import subprocess +from pathlib import Path + + +def _bfcl_available() -> bool: + return shutil.which("bfcl") is not None + + +def run_bfcl( + checkpoint: Path, + *, + version: str = "v4", + out_dir: Path | None = None, + categories: list[str] | None = None, +) -> dict[str, float]: + """Run the BFCL evaluation suite; return per-category scores.""" + if not _bfcl_available(): + msg = "bfcl not installed; run `uv sync --extra eval`." + raise RuntimeError(msg) + out_dir = Path(out_dir or checkpoint / "bfcl") + out_dir.mkdir(parents=True, exist_ok=True) + cmd = [ + "bfcl", + "evaluate", + "--model", + str(checkpoint), + "--test-category", + ",".join(categories or ["simple", "parallel", "multiple", "multi_turn"]), + "--version", + version, + "--output-dir", + str(out_dir), + ] + subprocess.run(cmd, check=True) + out: dict[str, float] = {} + for p in sorted(out_dir.glob("*.json")): + try: + raw = json.loads(p.read_text()) + except (OSError, json.JSONDecodeError): + continue + for k, v in raw.items(): + if isinstance(v, int | float): + out[f"{p.stem}/{k}"] = float(v) + return out + + +__all__ = ["run_bfcl"] diff --git a/mindxtrain/eval/card.py b/mindxtrain/eval/card.py new file mode 100644 index 0000000000000000000000000000000000000000..ae2ca70ade3a2576a581ec2c76558bfdaa80de15 --- /dev/null +++ b/mindxtrain/eval/card.py @@ -0,0 +1,127 @@ +"""Auto model-card generation from a completed run. + +Renders a HuggingFace-flavored `README.md` from the run's `XTrainConfig` +plus the eval JSON. Uses Jinja2 if installed; falls back to stdlib +`string.Template` so the base install (`uv sync` no extras) still works. +""" + +from __future__ import annotations + +import importlib.util +import json +from pathlib import Path +from string import Template +from typing import Any + +_FALLBACK_TEMPLATE = Template( + """--- +license: apache-2.0 +base_model: ${base_model} +tags: +- mindxtrain +- amd-mi300x +- fine-tuned +--- + +# ${run_name} + +Fine-tuned with [mindxtrain](https://github.com/mindx/mindxtrain) on AMD MI300X. + +- **Base model:** ${base_model} +- **Trainer:** ${backend} +- **Run id:** ${run_name} + +## Evaluation + +```json +${eval_json} +``` + +## Provenance + +- BLAKE3 hashes of config / dataset / checkpoint / eval are recorded in the + accompanying `manifest.json`. +- ROCm: 7.2.1 / gfx942 +""" +) + + +_JINJA_TEMPLATE = """--- +license: apache-2.0 +base_model: {{ base_model }} +tags: +- mindxtrain +- amd-mi300x +- fine-tuned +--- + +# {{ run_name }} + +Fine-tuned with [mindxtrain](https://github.com/mindx/mindxtrain) on AMD MI300X. + +- **Base model:** {{ base_model }} +- **Trainer:** {{ backend }} +- **Run id:** {{ run_name }} + +{% if hyperparams %}## Hyperparameters + +{% for k, v in hyperparams.items() %}- **{{ k }}**: {{ v }} +{% endfor %}{% endif %} + +## Evaluation + +```json +{{ eval_json }} +``` + +## Provenance + +- BLAKE3 hashes recorded in `manifest.json`. +- ROCm 7.2.1 / gfx942 (MI300X). +""" + + +def render_card(cfg: Any, eval_json: Path | None, out_path: Path) -> Path: + """Write a `README.md` model card; return the path.""" + base_model = getattr(getattr(cfg, "model", None), "name", "unknown") + run_name = getattr(getattr(cfg, "meta", None), "run_name", "run") + backend = getattr(getattr(cfg, "train", None), "backend", "axolotl") + + eval_payload = "{}" + if eval_json is not None and Path(eval_json).exists(): + try: + eval_payload = json.dumps(json.loads(Path(eval_json).read_text()), indent=2) + except (OSError, json.JSONDecodeError): + pass + + out_path = Path(out_path) + out_path.parent.mkdir(parents=True, exist_ok=True) + + if importlib.util.find_spec("jinja2") is not None: + from jinja2 import Template as JinjaTemplate + + hyperparams = { + "learning_rate": getattr(getattr(cfg.train, "optim", None), "learning_rate", None), + "epochs": getattr(cfg.train, "num_epochs", None), + "micro_batch_size": getattr(cfg.train, "micro_batch_size", None), + } + rendered = JinjaTemplate(_JINJA_TEMPLATE).render( + base_model=base_model, + run_name=run_name, + backend=backend, + hyperparams=hyperparams, + eval_json=eval_payload, + ) + else: + rendered = _FALLBACK_TEMPLATE.substitute( + base_model=base_model, + run_name=run_name, + backend=backend, + eval_json=eval_payload, + ) + + out_path.write_text(rendered) + return out_path + + +__all__ = ["render_card"] diff --git a/mindxtrain/eval/harness.py b/mindxtrain/eval/harness.py new file mode 100644 index 0000000000000000000000000000000000000000..8b567ba575c79e8ecc1ae0b3ec4b24635e817669 --- /dev/null +++ b/mindxtrain/eval/harness.py @@ -0,0 +1,71 @@ +"""lm-evaluation-harness wrapper. + +Subprocess `lm_eval --model hf --tasks <comma-sep> --model_args pretrained=<dir>`. +Output JSON written under `<out_dir>/lm_eval.json`. Lazy availability check. +""" + +from __future__ import annotations + +import json +import shutil +import subprocess +from pathlib import Path + + +def _lm_eval_available() -> bool: + return shutil.which("lm_eval") is not None or shutil.which("lm-eval") is not None + + +def run_lm_eval( + model_dir: Path, + tasks: list[str], + *, + out_dir: Path | None = None, + batch_size: str = "auto", +) -> Path: + """Run lm-eval-harness against `model_dir`; return path to results JSON.""" + if not _lm_eval_available(): + msg = "lm-eval not installed; run `uv sync --extra eval`." + raise RuntimeError(msg) + + out_dir = Path(out_dir or model_dir / "eval") + out_dir.mkdir(parents=True, exist_ok=True) + + cmd = [ + shutil.which("lm_eval") or "lm_eval", + "--model", + "hf", + "--model_args", + f"pretrained={model_dir}", + "--tasks", + ",".join(tasks), + "--batch_size", + batch_size, + "--output_path", + str(out_dir), + ] + subprocess.run(cmd, check=True) + + # lm-eval writes a JSON named `results-<timestamp>.json` per the harness; + # find the newest one and rename to `lm_eval.json` for stable downstream use. + candidates = sorted(out_dir.glob("results*.json"), key=lambda p: p.stat().st_mtime) + if not candidates: + msg = f"lm-eval ran but no results*.json found under {out_dir}" + raise RuntimeError(msg) + target = out_dir / "lm_eval.json" + target.write_text(candidates[-1].read_text()) + return target + + +def parse_summary(results_json: Path) -> dict[str, float]: + """Flatten `results.json` into a `{task: metric}` dict.""" + raw = json.loads(Path(results_json).read_text()) + out: dict[str, float] = {} + for task, metrics in (raw.get("results") or {}).items(): + for k, v in metrics.items(): + if isinstance(v, int | float): + out[f"{task}/{k}"] = float(v) + return out + + +__all__ = ["parse_summary", "run_lm_eval"] diff --git a/mindxtrain/eval/held_out_loss.py b/mindxtrain/eval/held_out_loss.py new file mode 100644 index 0000000000000000000000000000000000000000..3ae08c3b67c1a3febfd06cc6e4f98d3594d8a693 --- /dev/null +++ b/mindxtrain/eval/held_out_loss.py @@ -0,0 +1,157 @@ +"""Held-out perplexity / cross-entropy of a LoRA-adapted checkpoint. + +Answers the question Coach can't otherwise answer: did the adapter +actually move the base model toward the training distribution? The +trainer's `train_loss` field is averaged over the seen rows; only a +*held-out* slice tells us whether the adapter learned something +transferable, or just memorised the 32 rows we showed it. + +Public entry point: `score_checkpoint(...)`. Loads the base model, +optionally wraps with the adapter, runs each chat row through with +`labels = input_ids`, returns mean CE loss for both. `adapter_loss < +base_loss` on the held-out slice is the signal that the adapter is +doing something useful. + +Lazy-imports `torch` + `transformers` + `peft` so `import mindxtrain.eval` +stays cheap on the CPU-only base install (the `ml` extras gate +everything heavy). Mirrors the pattern in +`mindxtrain.deploy.ollama_push` so failures point at the same +`uv sync --extra ml` install hint. +""" + +from __future__ import annotations + +import json +from collections.abc import Callable, Iterator +from dataclasses import asdict, dataclass +from pathlib import Path +from typing import Any + + +@dataclass(frozen=True) +class HeldOutScore: + """Per-row + summary breakdown of held-out CE loss.""" + + base_model: str + adapter_dir: str + n: int + base_loss: float + adapter_loss: float + delta: float # adapter_loss - base_loss; negative = adapter improved + + def as_dict(self) -> dict[str, Any]: + return asdict(self) + + +def _iter_jsonl_messages(path: Path, max_samples: int | None) -> Iterator[dict[str, Any]]: + """Yield rows from a JSONL file shaped `{"messages": [...]}` (OpenAI chat).""" + n = 0 + with path.open(encoding="utf-8") as fh: + for line in fh: + line = line.strip() + if not line: + continue + try: + row = json.loads(line) + except json.JSONDecodeError: + continue + if not isinstance(row, dict) or "messages" not in row: + continue + yield row + n += 1 + if max_samples is not None and n >= max_samples: + return + + +def _row_loss(model: Any, tokenizer: Any, messages: list[dict[str, str]]) -> float: + """Compute mean CE loss for `messages` rendered through the chat template.""" + import torch # type: ignore + + text = tokenizer.apply_chat_template( + messages, tokenize=False, add_generation_prompt=False, + ) + enc = tokenizer(text, return_tensors="pt", truncation=True, max_length=1024) + input_ids = enc["input_ids"] + # Causal LM loss = next-token CE averaged across the sequence. Pass + # labels = input_ids so the model auto-shifts internally. + with torch.no_grad(): + out = model(input_ids=input_ids, labels=input_ids) + return float(out.loss.detach().cpu().item()) + + +def score_checkpoint( + adapter_dir: Path, + base_model: str, + jsonl_path: Path, + *, + max_samples: int | None = 32, + sink: Callable[[str], None] | None = None, +) -> HeldOutScore: + """Mean held-out CE loss of (base) vs (base + adapter). + + `jsonl_path` is a `*_training.jsonl` file produced by mindX's + machine_dreaming phase 5b — same shape `iter_mindx_dreams` yields. + Loads the rows lazily, evaluates each under both model variants, + returns the summary. + + Caller is responsible for picking rows the adapter *didn't* see at + training time — `mindxtrain eval-checkpoint` defaults to the + deterministic held-out slice when `data.eval_split` is set in the + config; otherwise it samples random rows from the same path the + recipe pointed at, which is a weaker (overlapping) signal but still + catches catastrophic forgetting. + """ + _emit = sink or (lambda _line: None) + + try: + from peft import PeftModel # type: ignore + from transformers import AutoModelForCausalLM, AutoTokenizer # type: ignore + except ImportError as exc: + msg = ( + "held-out scoring needs `peft` + `transformers`. install with: " + "uv sync --extra ml" + ) + raise ImportError(msg) from exc + + rows = list(_iter_jsonl_messages(Path(jsonl_path), max_samples)) + if not rows: + msg = f"no readable JSONL rows in {jsonl_path}" + raise ValueError(msg) + _emit(f"[eval] {len(rows)} held-out rows from {jsonl_path}") + + _emit(f"[eval] loading tokenizer + base model: {base_model}") + tokenizer = AutoTokenizer.from_pretrained(str(adapter_dir)) + base = AutoModelForCausalLM.from_pretrained(base_model) + base.eval() + + base_total = 0.0 + for row in rows: + base_total += _row_loss(base, tokenizer, row["messages"]) + base_loss = base_total / len(rows) + _emit(f"[eval] base mean loss = {base_loss:.4f}") + + _emit(f"[eval] applying adapter from {adapter_dir}") + adapter_model = PeftModel.from_pretrained(base, str(adapter_dir)) + adapter_model.eval() + + adapter_total = 0.0 + for row in rows: + adapter_total += _row_loss(adapter_model, tokenizer, row["messages"]) + adapter_loss = adapter_total / len(rows) + _emit(f"[eval] adapter mean loss = {adapter_loss:.4f}") + + delta = adapter_loss - base_loss + _emit( + f"[eval] delta = {delta:+.4f} ({'improved' if delta < 0 else 'regressed'})", + ) + return HeldOutScore( + base_model=base_model, + adapter_dir=str(adapter_dir), + n=len(rows), + base_loss=base_loss, + adapter_loss=adapter_loss, + delta=delta, + ) + + +__all__ = ["HeldOutScore", "score_checkpoint"] diff --git a/mindxtrain/eval/imprint.py b/mindxtrain/eval/imprint.py new file mode 100644 index 0000000000000000000000000000000000000000..96719f9e421b36bba02b9a736008a7107563ba03 --- /dev/null +++ b/mindxtrain/eval/imprint.py @@ -0,0 +1,193 @@ +"""Imprint measurement — did the persona take? + +The mindXtrain model: training **imprints** a persona onto an actor. We measure the +imprint by **recall from utterance inquiry**: pose the same probe prompts (inquiries) +to the actor **before** and **after** training, then score how much the after-utterances +moved toward the persona's voice relative to before — a same-state before/after delta. + +`score_imprint` is pure scoring over supplied utterances (base install, no GPU). It uses +sentence-transformer similarity when `--extra data` is present, else a stdlib lexical +fallback, so the measurement always runs. `probe_recall` (lazy `--extra ml`) generates the +utterances from a checkpoint; it's used by the end-to-end production test. +""" + +from __future__ import annotations + +import re +from pathlib import Path + +from pydantic import BaseModel, ConfigDict, Field + +_TOKEN = re.compile(r"[a-z0-9']+") + + +def default_inquiries(name: str = "the actor") -> list[str]: + """A small, persona-agnostic battery of recall probes.""" + return [ + "Who are you?", + "What do you do?", + f"Describe {name} in one sentence.", + "What matters most to you?", + "Say hello.", + ] + + +def _tokens(text: str) -> set[str]: + return set(_TOKEN.findall(text.lower())) + + +def _lexical_similarity(a: str, b: str) -> float: + """Token Jaccard in [0, 1] — the dependency-free voice metric.""" + ta, tb = _tokens(a), _tokens(b) + if not ta or not tb: + return 0.0 + return len(ta & tb) / len(ta | tb) + + +def _voice_similarity(utterances: list[str], baseline: list[str]) -> tuple[float, str]: + """Mean per-utterance max similarity to any baseline voice example. + + Returns (score in [0,1], method). Prefers sentence-transformer cosine when + available; otherwise lexical Jaccard. Empty inputs score 0. + """ + if not utterances or not baseline: + return 0.0, "none" + try: + import numpy as np + from sentence_transformers import SentenceTransformer + + enc = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2") + u = enc.encode(utterances, normalize_embeddings=True) + b = enc.encode(baseline, normalize_embeddings=True) + sims = u @ b.T + return float(np.mean(sims.max(axis=1))), "sentence-transformers" + except (ImportError, OSError, RuntimeError): + per = [max(_lexical_similarity(x, ref) for ref in baseline) for x in utterances] + return (sum(per) / len(per)), "lexical" + + +class ImprintReport(BaseModel): + """Before/after recall measurement of a persona imprint.""" + + model_config = ConfigDict(extra="forbid", frozen=True) + + inquiries: list[str] + before: list[str] + after: list[str] + before_voice: float = Field(description="mean similarity of before-utterances to persona voice") + after_voice: float = Field(description="mean similarity of after-utterances to persona voice") + imprint_delta: float = Field(description="after_voice - before_voice; >0 = imprinted toward persona") + shift: float = Field(description="mean (1 - similarity(before_i, after_i)); how much utterances changed") + method: str + imprinted: bool = Field(description="imprint_delta > 0 and utterances actually shifted") + + +def score_imprint( + inquiries: list[str], + before: list[str], + after: list[str], + baseline: list[str], +) -> ImprintReport: + """Score an imprint from same-state before/after utterances + a voice baseline. + + `before` / `after` are the actor's utterances for each inquiry (same order), + captured from the same model state before vs after training. `baseline` is the + persona's in-voice reference (e.g. `Persona.voice_examples`). + """ + before_voice, m1 = _voice_similarity(before, baseline) + after_voice, m2 = _voice_similarity(after, baseline) + method = m1 if m1 != "none" else m2 + + pairs = list(zip(before, after, strict=False)) + shift = ( + sum(1.0 - _lexical_similarity(b, a) for b, a in pairs) / len(pairs) + if pairs + else 0.0 + ) + delta = after_voice - before_voice + return ImprintReport( + inquiries=inquiries, + before=before, + after=after, + before_voice=round(before_voice, 4), + after_voice=round(after_voice, 4), + imprint_delta=round(delta, 4), + shift=round(shift, 4), + method=method, + imprinted=delta > 0.0 and shift > 0.0, + ) + + +def probe_recall( + base_model: str, + inquiries: list[str], + *, + adapter_dir: str | Path | None = None, + system: str | None = None, + max_new_tokens: int = 48, + force_cpu: bool = False, +) -> list[str]: + """Generate the actor's utterance for each inquiry (lazy `--extra ml`). + + With `adapter_dir` the persona-imprinted adapter is merged in (the "after" + state); without it you get the base model (the "before" state). Same prompts, + same decoding → a fair before/after recall comparison. + + `system` prepends a system turn to every probe. Pass the persona's system + prompt so the probe matches the conditioning the adapter was *trained* under + (the script rows carry that system turn); omitting it asks the adapter to + recall out of the distribution it learned, which understates the imprint. + """ + try: + import torch + from transformers import AutoModelForCausalLM, AutoTokenizer + except ImportError as exc: + msg = "transformers + torch not installed; run `uv sync --extra ml`." + raise RuntimeError(msg) from exc + + device = "cuda" if (not force_cpu and torch.cuda.is_available()) else "cpu" + dtype = torch.float32 if device == "cpu" else torch.bfloat16 + + tok = AutoTokenizer.from_pretrained(base_model, use_fast=True) + if tok.pad_token is None: + tok.pad_token = tok.eos_token + if getattr(tok, "chat_template", None) is None: + tok.chat_template = ( + "{% for message in messages %}" + "<|im_start|>{{ message['role'] }}\n{{ message['content'] }}<|im_end|>\n" + "{% endfor %}" + "{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}" + ) + + model = AutoModelForCausalLM.from_pretrained( + base_model, torch_dtype=dtype, device_map={"": device}, attn_implementation="eager", + ) + if adapter_dir is not None: + from peft import PeftModel + + model = PeftModel.from_pretrained(model, str(adapter_dir)) + + model.eval() + out: list[str] = [] + for inquiry in inquiries: + msgs = [{"role": "user", "content": inquiry}] + if system and system.strip(): + msgs.insert(0, {"role": "system", "content": system.strip()}) + prompt = tok.apply_chat_template( + msgs, + tokenize=False, + add_generation_prompt=True, + ) + enc = tok(prompt, return_tensors="pt").to(device) + with torch.no_grad(): + gen = model.generate( + **enc, max_new_tokens=max_new_tokens, do_sample=False, + repetition_penalty=1.3, no_repeat_ngram_size=3, + pad_token_id=tok.pad_token_id, + ) + text = tok.decode(gen[0][enc["input_ids"].shape[1]:], skip_special_tokens=True) + out.append(text.strip()) + return out + + +__all__ = ["ImprintReport", "default_inquiries", "probe_recall", "score_imprint"] diff --git a/mindxtrain/eval/inspect_ai_adapter.py b/mindxtrain/eval/inspect_ai_adapter.py new file mode 100644 index 0000000000000000000000000000000000000000..333b8f8d8c0d41d3005cd775a27b7f9f37cda94f --- /dev/null +++ b/mindxtrain/eval/inspect_ai_adapter.py @@ -0,0 +1,53 @@ +"""Inspect-AI adapter — agentic evaluations. + +Subprocess `inspect eval`; emits per-task JSON under `out_dir`. +""" + +from __future__ import annotations + +import json +import shutil +import subprocess +from pathlib import Path + + +def _inspect_available() -> bool: + return shutil.which("inspect") is not None + + +def run_inspect( + checkpoint: Path, + tasks: list[str], + *, + out_dir: Path | None = None, +) -> dict[str, float]: + """Run an inspect-ai task suite; return summary metrics.""" + if not _inspect_available(): + msg = "inspect-ai not installed; run `uv sync --extra eval`." + raise RuntimeError(msg) + out_dir = Path(out_dir or checkpoint / "inspect") + out_dir.mkdir(parents=True, exist_ok=True) + cmd = [ + "inspect", + "eval", + *tasks, + "--model", + f"hf/{checkpoint}", + "--log-dir", + str(out_dir), + ] + subprocess.run(cmd, check=True) + out: dict[str, float] = {} + for p in sorted(out_dir.glob("**/*.json")): + try: + raw = json.loads(p.read_text()) + except (OSError, json.JSONDecodeError): + continue + scores = raw.get("results", {}).get("scores", {}) + for k, v in scores.items(): + if isinstance(v, dict) and "value" in v and isinstance(v["value"], int | float): + out[f"{p.stem}/{k}"] = float(v["value"]) + return out + + +__all__ = ["run_inspect"] diff --git a/mindxtrain/eval/lighteval_adapter.py b/mindxtrain/eval/lighteval_adapter.py new file mode 100644 index 0000000000000000000000000000000000000000..69c81a0ed465cc62b04ce75d84a7257e40bb5766 --- /dev/null +++ b/mindxtrain/eval/lighteval_adapter.py @@ -0,0 +1,53 @@ +"""Lighteval adapter — standard LM benchmarks via the HF Lighteval harness. + +Subprocess `lighteval` CLI; emits a `results.json` under `out_dir`. +""" + +from __future__ import annotations + +import json +import shutil +import subprocess +from pathlib import Path + + +def _lighteval_available() -> bool: + return shutil.which("lighteval") is not None + + +def run_lighteval( + checkpoint: Path, + tasks: list[str], + *, + out_dir: Path | None = None, +) -> dict[str, float]: + """Run lighteval; return summary metrics.""" + if not _lighteval_available(): + msg = "lighteval not installed; run `uv sync --extra eval`." + raise RuntimeError(msg) + out_dir = Path(out_dir or checkpoint / "lighteval") + out_dir.mkdir(parents=True, exist_ok=True) + cmd = [ + "lighteval", + "accelerate", + "--model_args", + f"pretrained={checkpoint}", + "--tasks", + ",".join(tasks), + "--output_dir", + str(out_dir), + ] + subprocess.run(cmd, check=True) + candidates = sorted(out_dir.glob("results_*.json"), key=lambda p: p.stat().st_mtime) + if not candidates: + return {} + raw = json.loads(candidates[-1].read_text()) + out: dict[str, float] = {} + for task, metrics in (raw.get("results") or {}).items(): + for k, v in metrics.items(): + if isinstance(v, int | float): + out[f"{task}/{k}"] = float(v) + return out + + +__all__ = ["run_lighteval"] diff --git a/mindxtrain/eval/llama_evals.py b/mindxtrain/eval/llama_evals.py new file mode 100644 index 0000000000000000000000000000000000000000..919423db1a4c57b34d51dafe88c2a5f48c5ef23c --- /dev/null +++ b/mindxtrain/eval/llama_evals.py @@ -0,0 +1,155 @@ +"""Simple evaluators — clean-room reimplementation of LlamaIndex-style evals. + +Reimplemented from the *behavior* of `llama_index.core.evaluation` (MIT) — never copied. +A minimal set for the "did the trained model recall the training / maintain the persona" +question: + +- `SemanticSimilarityEvaluator` — embedding/lexical cosine (reuses `eval.imprint`). +- `CorrectnessEvaluator` — LLM-as-judge of a response vs a reference. +- `PairwiseEvaluator` — which of two responses (before vs after) better matches the persona. +- `GuidelineEvaluator` — does a response comply with a rubric / guidelines. + +The LLM-judge evaluators call the existing chat backend via `governance.panel.chat_once` +(ollama/vLLM). Pure stdlib + pydantic + httpx; base-install importable. +""" + +from __future__ import annotations + +import re + +from pydantic import BaseModel, ConfigDict, Field + +_SCORE_RE = re.compile(r"score\s*[:=]?\s*([0-5](?:\.\d+)?)", re.IGNORECASE) +_CHOICE_RE = re.compile(r"\b(A|B|TIE)\b", re.IGNORECASE) +_DEFAULT_JUDGE = "llama3.2" + + +class EvalScore(BaseModel): + """A single evaluation result, score normalized to [0, 1].""" + + model_config = ConfigDict(extra="forbid", frozen=True) + + score: float = Field(ge=0.0, le=1.0) + passing: bool + reasoning: str = "" + method: str + + +def _parse_judge_score(text: str) -> tuple[float, str]: + """Parse a 1-5 `SCORE: N` from judge output → ([0,1], one-line reasoning).""" + m = _SCORE_RE.search(text) + raw = float(m.group(1)) if m else 2.5 # neutral when unparseable + raw = max(1.0, min(5.0, raw)) + score01 = (raw - 1.0) / 4.0 + reasoning = next((ln.strip() for ln in text.splitlines() if ln.strip()), text.strip())[:240] + return round(score01, 4), reasoning + + +def _judge(model: str, system: str, user: str, *, base_url: str | None) -> tuple[float, str]: + """Run one LLM-judge call; returns ([0,1], reasoning). Best-effort (0.5 on error).""" + from mindxtrain.governance.panel import chat_once + + try: + text = chat_once( + model, + [{"role": "system", "content": system}, {"role": "user", "content": user}], + base_url=base_url, max_tokens=300, temperature=0.0, + ) + except Exception as exc: # never let an eval crash a workflow + return 0.5, f"judge error: {exc}" + return _parse_judge_score(text) + + +class SemanticSimilarityEvaluator: + """Embedding/lexical similarity between two texts (reuses `eval.imprint`).""" + + def __init__(self, threshold: float = 0.7) -> None: + self.threshold = threshold + + def evaluate(self, text_a: str, text_b: str) -> EvalScore: + from mindxtrain.eval.imprint import _voice_similarity + + score, method = _voice_similarity([text_a], [text_b]) + return EvalScore( + score=round(score, 4), passing=score >= self.threshold, + reasoning=f"similarity {score:.3f} vs threshold {self.threshold}", method=method, + ) + + +class CorrectnessEvaluator: + """LLM-as-judge: is RESPONSE correct/faithful vs REFERENCE for the QUERY?""" + + def __init__(self, model: str = _DEFAULT_JUDGE, base_url: str | None = None, threshold: float = 0.6) -> None: + self.model, self.base_url, self.threshold = model, base_url, threshold + + def evaluate(self, query: str, response: str, reference: str) -> EvalScore: + system = ( + "You are a strict evaluator. Score from 1 (wrong) to 5 (perfect) how well the " + "RESPONSE matches the REFERENCE answer for the QUERY. Give one sentence of " + "reasoning, then a final line 'SCORE: N'." + ) + user = f"QUERY: {query}\nREFERENCE: {reference}\nRESPONSE: {response}" + score, reasoning = _judge(self.model, system, user, base_url=self.base_url) + return EvalScore(score=score, passing=score >= self.threshold, reasoning=reasoning, + method=f"llm-judge:{self.model}") + + +class PairwiseEvaluator: + """Which of two responses better matches the persona/reference? B (after) vs A (before).""" + + def __init__(self, model: str = _DEFAULT_JUDGE, base_url: str | None = None) -> None: + self.model, self.base_url = model, base_url + + def evaluate(self, query: str, response_a: str, response_b: str, *, reference: str = "") -> EvalScore: + ref = f" The target voice/reference is: {reference}." if reference else "" + system = ( + "You compare two assistant responses (A and B) to a query." + ref + + " Decide which better matches the target voice. Give one sentence, then a final " + "line with exactly 'A', 'B', or 'TIE'." + ) + user = f"QUERY: {query}\nA: {response_a}\nB: {response_b}" + from mindxtrain.governance.panel import chat_once + + try: + text = chat_once( + self.model, + [{"role": "system", "content": system}, {"role": "user", "content": user}], + base_url=self.base_url, max_tokens=200, temperature=0.0, + ) + except Exception as exc: + return EvalScore(score=0.5, passing=False, reasoning=f"judge error: {exc}", + method=f"llm-judge:{self.model}") + # Read the LAST A/B/TIE token (the verdict line). + choices = _CHOICE_RE.findall(text) + verdict = (choices[-1].upper() if choices else "TIE") + score = {"B": 1.0, "TIE": 0.5, "A": 0.0}[verdict] + reasoning = next((ln.strip() for ln in text.splitlines() if ln.strip()), text.strip())[:240] + return EvalScore(score=score, passing=verdict == "B", reasoning=f"verdict {verdict}: {reasoning}", + method=f"llm-judge:{self.model}") + + +class GuidelineEvaluator: + """LLM-as-judge: does RESPONSE comply with the GUIDELINES/rubric?""" + + def __init__(self, model: str = _DEFAULT_JUDGE, base_url: str | None = None, threshold: float = 0.6) -> None: + self.model, self.base_url, self.threshold = model, base_url, threshold + + def evaluate(self, response: str, guidelines: str) -> EvalScore: + system = ( + "You check compliance with guidelines. Score from 1 (violates) to 5 (fully " + "complies) how well the RESPONSE follows the GUIDELINES. One sentence, then " + "'SCORE: N'." + ) + user = f"GUIDELINES: {guidelines}\nRESPONSE: {response}" + score, reasoning = _judge(self.model, system, user, base_url=self.base_url) + return EvalScore(score=score, passing=score >= self.threshold, reasoning=reasoning, + method=f"llm-judge:{self.model}") + + +__all__ = [ + "CorrectnessEvaluator", + "EvalScore", + "GuidelineEvaluator", + "PairwiseEvaluator", + "SemanticSimilarityEvaluator", +] diff --git a/mindxtrain/eval/mei/__init__.py b/mindxtrain/eval/mei/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..55ddef33f35a774e1592c3547d10717d91fc62c8 --- /dev/null +++ b/mindxtrain/eval/mei/__init__.py @@ -0,0 +1,48 @@ +"""mindX Efficiency Index (MEI) — composite model-evaluation metric. + +Implements the v0.1 specification at +`/home/hacker/mindX/docs/operations/mindX Efficiency Index_ … .md`. + +The MEI is a single scalar in [0, 1] derived from five log-compressed +sub-indices — quality (Q), decode throughput (Dt), prefill throughput +(Pp), memory footprint (M), and energy per useful token (E) — combined +via weighted geometric mean. It is the in-house metric mindXtrain uses +to gate checkpoint promotion to AgenticPlace. + +Layout (mirrors the spec's §7 three-layer instrumentation stack): + +- `record.py` — Pydantic schemas for the canonical measurement record. +- `tokenizer.py` — canonical tokenizer wrapper (spec Rule 3.1). +- `throughput.py` — engine harness wrappers (llama.cpp, Ollama, vLLM, HF). +- `energy.py` — power sampling (NVML, RAPL, powermetrics, ROCm-SMI). +- `orchestrator.py` — measurement protocol: warmup → battery → sweep → CI. +- `score.py` — pure functions computing MEI from a record. +- `xei.py` — training-side companion (MFU, optimization health). +- `promotion.py` — three-gate AgenticPlace promotion logic. +- `history.py` — append-only JSONL of historical scores. + +Anchor calibration (§5.2-§5.5) is frozen at module load for v0.1; the +ANCHORS object is the authoritative source for floors and ceilings. +""" + +from mindxtrain.eval.mei.record import ( + ConcurrencyPoint, + ContextTierMeasurement, + HardwareIdent, + InferenceEngineIdent, + LatencyPercentiles, + MEIRecord, + QuantizationTuple, + TokenSeries, +) + +__all__ = [ + "ConcurrencyPoint", + "ContextTierMeasurement", + "HardwareIdent", + "InferenceEngineIdent", + "LatencyPercentiles", + "MEIRecord", + "QuantizationTuple", + "TokenSeries", +] diff --git a/mindxtrain/eval/mei/anchors.py b/mindxtrain/eval/mei/anchors.py new file mode 100644 index 0000000000000000000000000000000000000000..e045a4d0bf6ab09c7f96b75fa34cdc1879ca2cea --- /dev/null +++ b/mindxtrain/eval/mei/anchors.py @@ -0,0 +1,117 @@ +"""MEI v0.1 anchors — the floors and ceilings that calibrate the +logarithmic compression of each sub-index (spec §5.2-§5.5). + +These anchors are frozen at module load. Calibration to mid-2026 +hardware per the spec; bump to v1.0 when the It's FOSS reference and +the Apple Silicon / Blackwell-Ultra envelopes drift. Cross-era +comparisons must be made via raw sub-index values, not composite +scores, when anchors differ. + +The seven evaluation names are canonical — every harness that emits a +quality score uses these keys verbatim. +""" + +from __future__ import annotations + +from typing import Literal + +from pydantic import BaseModel, ConfigDict, Field + +# Spec §5.1 — the four quality bands, their assigned evaluations, and the +# weights they receive in the geometric mean. The Agentic band gets 0.35 +# (deliberately higher than any public scoreboard, because mindX is an +# autonomous agent and agentic failure dominates cost). +QualityBand = Literal["agentic", "instruction", "reasoning", "knowledge_code"] + +QUALITY_BAND_EVALS: dict[QualityBand, tuple[str, ...]] = { + "agentic": ("mab",), + "instruction": ("ifeval_strict_prompt", "mt_bench_2turn"), + "reasoning": ("livebench_reasoning", "gpqa_diamond"), + "knowledge_code": ("mmlu_pro", "bigcodebench_hard_pass1"), +} + +QUALITY_BAND_WEIGHTS_SEALED: dict[QualityBand, float] = { + "agentic": 0.35, + "instruction": 0.25, + "reasoning": 0.20, + "knowledge_code": 0.20, +} + + +def quality_band_weights(*, mab_provisional: bool) -> dict[QualityBand, float]: + """Return the band weights to apply for this run. + + When `mab_provisional` is True (spec §9), the 0.35 Agentic weight is + redistributed equally across the other three bands until the MAB v1.0 + seals. The other three bands gain ≈0.117 each (their original 0.25 / + 0.20 / 0.20 stretches to ≈0.367 / ≈0.317 / ≈0.317). + """ + if not mab_provisional: + return dict(QUALITY_BAND_WEIGHTS_SEALED) + sealed = QUALITY_BAND_WEIGHTS_SEALED + agentic_w = sealed["agentic"] + redistributed = agentic_w / 3.0 + return { + "agentic": 0.0, + "instruction": sealed["instruction"] + redistributed, + "reasoning": sealed["reasoning"] + redistributed, + "knowledge_code": sealed["knowledge_code"] + redistributed, + } + + +class MEIAnchors(BaseModel): + """Frozen calibration constants for MEI v0.1 (spec §5).""" + + model_config = ConfigDict(extra="forbid", frozen=True) + + version: str = "0.1" + + # §5.2 decode throughput — log-compressed, floor=3 tok/s (It's FOSS + # "painfully slow"), ceiling=300 tok/s (Apple M3 Ultra / B200 class). + t_floor_decode: float = Field(default=3.0, gt=0.0) + t_ceiling_decode: float = Field(default=300.0, gt=0.0) + + # §5.3 prefill — floor=20 tok/s (Phi 4 Mini on CPU), ceiling=5000 + # (FlashAttention-3 on H100). + p_floor_prefill: float = Field(default=20.0, gt=0.0) + p_ceiling_prefill: float = Field(default=5000.0, gt=0.0) + + # §5.4 memory — floor=0.5 GB (Qwen 3.0.6B Q4_K_M), ceiling=200 GB + # (405B-class at FP8). + m_floor_gb: float = Field(default=0.5, gt=0.0) + m_ceiling_gb: float = Field(default=200.0, gt=0.0) + + # §5.5 energy — floor=0.1 J/useful-token (efficient Apple Silicon), + # ceiling=100 J/token (405B on H100 without batching). + j_floor_per_tok: float = Field(default=0.1, gt=0.0) + j_ceiling_per_tok: float = Field(default=100.0, gt=0.0) + + # §5.6 composite weights. Must sum to 1.0. + w_q: float = Field(default=0.40, ge=0.0, le=1.0) + w_dt: float = Field(default=0.20, ge=0.0, le=1.0) + w_pp: float = Field(default=0.10, ge=0.0, le=1.0) + w_m: float = Field(default=0.15, ge=0.0, le=1.0) + w_e: float = Field(default=0.15, ge=0.0, le=1.0) + + +ANCHORS_V01: MEIAnchors = MEIAnchors() +"""Frozen v0.1 anchors. Any consumer using this constant is implicitly +declaring its results MEI v0.1-comparable.""" + +# §8 promotion gate constants. +PROMOTION_COMPOSITE_THRESHOLD: float = 0.55 +PROMOTION_SUBINDEX_FLOOR: float = 0.30 +PROMOTION_QUALITY_BAND_FLOOR: float = 0.50 # Agentic band specifically (§8). + + +__all__ = [ + "ANCHORS_V01", + "PROMOTION_COMPOSITE_THRESHOLD", + "PROMOTION_QUALITY_BAND_FLOOR", + "PROMOTION_SUBINDEX_FLOOR", + "QUALITY_BAND_EVALS", + "QUALITY_BAND_WEIGHTS_SEALED", + "MEIAnchors", + "QualityBand", + "quality_band_weights", +] diff --git a/mindxtrain/eval/mei/history.py b/mindxtrain/eval/mei/history.py new file mode 100644 index 0000000000000000000000000000000000000000..aebd14a776d5c28f85a121482dd1d4f21bb7d1a4 --- /dev/null +++ b/mindxtrain/eval/mei/history.py @@ -0,0 +1,132 @@ +"""Append-only JSONL history of MEI scores (spec §7 top layer). + +Every scored checkpoint appends one row to `out/mei/history.jsonl`. The +historical-comparison database is what lets mindXtrain rank a new +checkpoint against the entire alpha history — needed for the §8 +promotion gate ("MEI strictly higher … than the currently-promoted"). + +Pure stdlib; no DB. The append-only file is BLAKE3-resistant to +out-of-order writes (each row carries its own timestamp + run_id), and +the read paths sort/filter in memory — fine for the volumes the alpha +will produce (≤ 200 checkpoints per phase × a handful of phases is +still kilobytes). +""" + +from __future__ import annotations + +from datetime import UTC, datetime +from pathlib import Path + +from pydantic import BaseModel, ConfigDict, Field + +from mindxtrain.eval.mei.score import MEIScore + +# Default path for the history file. Callers can override per-run via +# the function `path` arg if they want a per-experiment ledger. +DEFAULT_HISTORY_PATH = Path("./out/mei/history.jsonl") + + +class HistoryEntry(BaseModel): + """One row in the MEI history. Frozen + extra=forbid for ledger hygiene.""" + + model_config = ConfigDict(extra="forbid", frozen=True) + + timestamp: str = Field(description="ISO-8601 UTC of the score event.") + run_id: str = Field(min_length=1, description="The mindXtrain run that produced this score.") + model_id: str = Field(min_length=1, description="HF Hub repo or local checkpoint name.") + model_sha256: str = Field(min_length=1, description="Hash of the resolved weights.") + promoted: bool = Field( + default=False, + description="True iff this score is the currently-promoted checkpoint.", + ) + score: MEIScore + + +def append( + score: MEIScore, + *, + run_id: str, + model_id: str, + model_sha256: str, + promoted: bool = False, + path: Path | None = None, +) -> Path: + """Append one history entry. Creates the file (and out/mei/) if needed. + + Returns the path written. Idempotent in the sense that two appends with + identical content produce two physical rows — the caller is responsible + for dedup if it matters. For the promotion-gate use case, we want every + score event recorded, even repeats of the same checkpoint scored under + a different anchor version. + """ + target = path or DEFAULT_HISTORY_PATH + target.parent.mkdir(parents=True, exist_ok=True) + entry = HistoryEntry( + timestamp=datetime.now(UTC).isoformat(), + run_id=run_id, + model_id=model_id, + model_sha256=model_sha256, + promoted=promoted, + score=score, + ) + with target.open("a", encoding="utf-8") as fh: + fh.write(entry.model_dump_json() + "\n") + return target + + +def read_all(path: Path | None = None) -> list[HistoryEntry]: + """Read every entry from the history file, oldest-first by file order. + + Bad / malformed lines are skipped silently (the ledger is append-only, + so corrupted writes from process crashes shouldn't break readers). + Returns an empty list if the file doesn't exist. + """ + target = path or DEFAULT_HISTORY_PATH + if not target.exists(): + return [] + out: list[HistoryEntry] = [] + with target.open("r", encoding="utf-8") as fh: + for line in fh: + line = line.strip() + if not line: + continue + try: + out.append(HistoryEntry.model_validate_json(line)) + except Exception: + continue + return out + + +def currently_promoted(path: Path | None = None) -> HistoryEntry | None: + """Return the most-recently-promoted entry, or None. + + Promotion is monotonic in our model: once a checkpoint is promoted, + the only way a new one beats it is via `is_promotable`. The + "currently-promoted" view is therefore the most-recent row with + `promoted=True`. + """ + for entry in reversed(read_all(path=path)): + if entry.promoted: + return entry + return None + + +def trajectory(*, last_n: int = 10, path: Path | None = None) -> list[HistoryEntry]: + """Return the last-N entries, newest-first. + + Used by the promotion-gate trajectory analysis (§8): if MEI declines + for two consecutive checkpoints, the run is paused; if it plateaus + for three, the schedule is reviewed. + """ + rows = read_all(path=path) + return rows[-last_n:][::-1] + + +__all__ = [ + "DEFAULT_HISTORY_PATH", + "HistoryEntry", + "append", + "currently_promoted", + "read_all", + "trajectory", +] diff --git a/mindxtrain/eval/mei/record.py b/mindxtrain/eval/mei/record.py new file mode 100644 index 0000000000000000000000000000000000000000..a0fc07ef1abdb4d8c8c6a81c4f6d85ab3bf287f0 --- /dev/null +++ b/mindxtrain/eval/mei/record.py @@ -0,0 +1,321 @@ +"""Pydantic schemas for the canonical MEI measurement record (spec §7). + +The structured record is the integration boundary between inference-engine +wrappers (`throughput.py`), the measurement orchestrator (`orchestrator.py`), +and MEI scoring (`score.py`). Once written, every harness conforms to it. + +Design constraints from the spec: + +- Every reported figure carries its measurement context: hardware SKU, + engine commit SHA, tokenizer revision, quantization tuple, seed, + warmup count, sample size. +- Latency is reported at p50 / p95 / p99 with bootstrap 95% CIs — + *never* as a mean alone (spec §4, "production latency distributions + are routinely 15x heavier at p99 than at the mean under contention"). +- Throughput is reported per the four-tier context battery (32, 512, + 8192, 32768 tokens) so context-scaling degradation is visible. +- Concurrency sweep at (1, 4, 16, 64, max) captures the Pareto curve; + a single batch-1 number is non-conformant per spec §4. +- Energy is optional but carries an `energy_estimated` flag when the + bandwidth-bound proxy was used in lieu of direct measurement. +""" + +from __future__ import annotations + +from typing import Literal + +from pydantic import BaseModel, ConfigDict, Field, model_validator + +# Exactly four context tiers per spec §4. The headline Ttg used in the +# composite is the geometric mean across these tiers. +EXPECTED_CONTEXT_TIERS: tuple[int, ...] = (32, 512, 8192, 32768) + +# Concurrency points per spec §4. `max` is the hardware's sustained max +# under a 10s p99 TTFT SLO — recorded as an integer for the actual point +# achieved by the run. +EXPECTED_CONCURRENCY: tuple[int, ...] = (1, 4, 16, 64) + + +class HardwareIdent(BaseModel): + """Hardware reproducibility envelope (spec §4).""" + + model_config = ConfigDict(extra="forbid", frozen=True) + + cpu_sku: str = Field(min_length=1, description="e.g. 'AMD EPYC 9654' or 'Apple M3 Ultra'.") + simd_class: str = Field( + default="", + description="AVX2 | AVX-512 | AVX-512-VNNI | AMX | NEON | SVE2 | SME | Metal | gfx942 …", + ) + dram_channel_count: int = Field(default=0, ge=0) + dram_clock_mhz: int = Field(default=0, ge=0) + gpu_sku: str = Field(default="", description="e.g. 'AMD MI300X', 'NVIDIA H100', or empty for CPU-only.") + gpu_clock_mhz: int = Field(default=0, ge=0) + os_kernel: str = Field(default="", description="e.g. 'Linux 6.8.0-110-generic'.") + + +class InferenceEngineIdent(BaseModel): + """Identify exactly which engine produced the measurement (spec §4).""" + + model_config = ConfigDict(extra="forbid", frozen=True) + + name: Literal["llama.cpp", "ollama", "vllm", "sglang", "transformers"] = Field( + description="Which serving engine collected the timings.", + ) + commit_sha: str = Field(min_length=1, description="Engine binary commit SHA or version string.") + config: dict[str, str] = Field( + default_factory=dict, + description="Engine-specific config knobs (cache_type_k, --n_threads, --gpu_layers, etc.).", + ) + + +class QuantizationTuple(BaseModel): + """Per spec §4: every reported figure carries an explicit quantization tuple. + + A Q4_K_M-with-imatrix run is distinguishable from Q4_K_M-without-imatrix. + """ + + model_config = ConfigDict(extra="forbid", frozen=True) + + scheme: str = Field( + min_length=1, + description=( + "Quantization name: Q2_K, Q4_K_M, Q5_K_M, Q8_0, IQ2_XXS, IQ4_NL, " + "GPTQ, AWQ, EXL2, NF4, FP8, NVFP4, BF16, FP16, FP32." + ), + ) + bpw: float = Field(gt=0.0, description="Bits per weight (effective).") + calibration_corpus: str = Field( + default="", + description="Imatrix / GPTQ / AWQ calibration set identifier; empty if none.", + ) + + +class TokenSeries(BaseModel): + """The four primary token counts per request (spec §3, §4).""" + + model_config = ConfigDict(extra="forbid", frozen=True) + + n_prefill: int = Field(ge=0, description="Prompt tokens consumed, including chat-template scaffolding.") + n_decode: int = Field(ge=0, description="Output tokens generated (incl. stop tokens before EOS).") + b_decode: int = Field(ge=0, description="UTF-8 byte length of decoded output.") + n_useful_decode: int = Field( + ge=0, + description="Content tokens after structured-output parsing — excludes ChatML scaffolding.", + ) + + @model_validator(mode="after") + def _useful_does_not_exceed_decode(self) -> TokenSeries: + if self.n_useful_decode > self.n_decode: + msg = ( + f"n_useful_decode ({self.n_useful_decode}) cannot exceed " + f"n_decode ({self.n_decode}) — useful tokens are a subset." + ) + raise ValueError(msg) + return self + + +class LatencyPercentiles(BaseModel): + """p50/p95/p99 with bootstrap 95% CI (spec §4). + + Never report a mean alone — distributions are heavy-tailed. The + `ci_low_p95` / `ci_high_p95` pair is the bootstrap envelope around + the p95 specifically; the spec privileges p95 as the headline + latency point. + """ + + model_config = ConfigDict(extra="forbid", frozen=True) + + p50_ms: float = Field(ge=0.0) + p95_ms: float = Field(ge=0.0) + p99_ms: float = Field(ge=0.0) + ci_low_p95: float = Field(ge=0.0, description="Bootstrap 95% CI lower bound on p95.") + ci_high_p95: float = Field(ge=0.0, description="Bootstrap 95% CI upper bound on p95.") + sample_n: int = Field(ge=1, description="Samples behind these percentiles (≥ 100 per spec §4).") + + @model_validator(mode="after") + def _percentile_ordering(self) -> LatencyPercentiles: + if not (self.p50_ms <= self.p95_ms <= self.p99_ms): + msg = ( + f"latency percentiles must be monotone: " + f"p50={self.p50_ms} p95={self.p95_ms} p99={self.p99_ms}" + ) + raise ValueError(msg) + if self.ci_low_p95 > self.p95_ms or self.ci_high_p95 < self.p95_ms: + msg = ( + f"bootstrap CI [{self.ci_low_p95}, {self.ci_high_p95}] " + f"does not bracket p95 {self.p95_ms}" + ) + raise ValueError(msg) + return self + + +class ContextTierMeasurement(BaseModel): + """One row of the four-tier context battery (spec §4). + + `Tpp` is the prefill throughput, `Ttg` is the decode throughput, both + in tokens/sec. The geometric mean of Ttg across tiers is the headline + decode rate used by `Dt`. + """ + + model_config = ConfigDict(extra="forbid", frozen=True) + + context_tokens: int = Field( + description="One of EXPECTED_CONTEXT_TIERS — 32, 512, 8192, 32768.", + ) + tpp_tok_s: float = Field(ge=0.0, description="Prefill (prompt-processing) throughput.") + ttg_tok_s: float = Field(ge=0.0, description="Decode (generation) throughput.") + bytes_per_sec: float = Field( + ge=0.0, + description="Auxiliary tokenizer-invariant rate (UTF-8 bytes of decoded output / sec).", + ) + ttft: LatencyPercentiles = Field(description="Time-to-first-token percentiles.") + tpot: LatencyPercentiles = Field(description="Time-per-output-token percentiles.") + itl: LatencyPercentiles = Field( + description="Inter-token latency (vLLM convention: excludes TTFT).", + ) + + @model_validator(mode="after") + def _context_is_canonical_tier(self) -> ContextTierMeasurement: + if self.context_tokens not in EXPECTED_CONTEXT_TIERS: + msg = ( + f"context_tokens={self.context_tokens} not one of the " + f"canonical tiers {EXPECTED_CONTEXT_TIERS}; non-conformant" + ) + raise ValueError(msg) + return self + + +class ConcurrencyPoint(BaseModel): + """One point on the concurrency-vs-throughput-vs-latency Pareto curve.""" + + model_config = ConfigDict(extra="forbid", frozen=True) + + concurrency: int = Field(ge=1, description="Number of in-flight requests.") + aggregate_throughput_tok_s: float = Field( + ge=0.0, + description="Total tokens/sec across all concurrent requests.", + ) + p99_ttft_ms: float = Field( + ge=0.0, + description="p99 TTFT at this concurrency — gates against the 10s SLO.", + ) + goodput_fraction: float = Field( + default=0.0, + ge=0.0, + le=1.0, + description=( + "Fraction of requests meeting the (ttft:500ms, tpot:50ms) SLO. " + "Cheng et al. smooth-goodput preferred where instrumentable." + ), + ) + + +class MEIRecord(BaseModel): + """The canonical structured record (spec §7). + + One per (model, hardware, engine, quantization, run) tuple. Every + headline metric the MEI computes is downstream of this record. + """ + + model_config = ConfigDict(extra="forbid", frozen=True) + + # ---- identity ---- + model_id: str = Field(min_length=1, description="HF Hub repo ID or local checkpoint name.") + model_sha256: str = Field( + min_length=8, + description="BLAKE3 or SHA-256 of the resolved model weights file(s).", + ) + tokenizer_revision: str = Field( + min_length=1, + description="Tokenizer revision hash per spec Rule 3.1.", + ) + quantization: QuantizationTuple + + # ---- environment ---- + hardware: HardwareIdent + engine: InferenceEngineIdent + seed: int = Field(ge=0) + warmup_requests: int = Field(default=5, ge=0, description="Discarded warmups per spec §4.") + sample_size: int = Field( + default=100, + ge=1, + description="Steady-state requests behind every latency percentile.", + ) + + # ---- measurements ---- + tier_measurements: list[ContextTierMeasurement] = Field( + description="Exactly 4 entries — one per canonical context tier.", + ) + concurrency: list[ConcurrencyPoint] = Field( + description="Concurrency sweep — typically 5 points.", + ) + peak_memory_gb: float = Field( + ge=0.0, + description="Resident set at the 32K context working point — weights + KV + runtime.", + ) + kv_cache_gb_at_32k: float = Field( + ge=0.0, + description="KV cache footprint at 32K context — reported separately per spec §4.", + ) + energy_j_per_useful_token: float | None = Field( + default=None, + description="Energy per useful decoded token (J). None if not measured.", + ) + energy_estimated: bool = Field( + default=False, + description="True when E was derived from the bandwidth-bound proxy.", + ) + + # ---- quality ---- + quality_raw: dict[str, float] = Field( + default_factory=dict, + description=( + "Per-evaluation raw scores keyed by canonical name " + "(mmlu_pro, gpqa_diamond, ifeval_strict_prompt, livebench_reasoning, " + "bigcodebench_hard_pass1, mt_bench_2turn, mab)." + ), + ) + quality_pool_version: str = Field( + default="v0.1", + description="Reference pool version (random baseline + Qwen3.5-flagship ceilings).", + ) + mab_provisional: bool = Field( + default=True, + description=( + "True until the mindX Agentic Battery v1.0 is sealed. While " + "provisional, the 0.35 Agentic-band weight redistributes " + "across the other three Q bands." + ), + ) + + @model_validator(mode="after") + def _exactly_four_tiers(self) -> MEIRecord: + contexts = sorted(t.context_tokens for t in self.tier_measurements) + if contexts != sorted(EXPECTED_CONTEXT_TIERS): + msg = ( + f"tier_measurements must cover exactly {EXPECTED_CONTEXT_TIERS}; " + f"got {tuple(contexts)}" + ) + raise ValueError(msg) + return self + + @model_validator(mode="after") + def _energy_estimated_flag_consistent(self) -> MEIRecord: + if self.energy_j_per_useful_token is None and self.energy_estimated: + msg = "energy_estimated=True requires a non-null energy value" + raise ValueError(msg) + return self + + +__all__ = [ + "EXPECTED_CONCURRENCY", + "EXPECTED_CONTEXT_TIERS", + "ConcurrencyPoint", + "ContextTierMeasurement", + "HardwareIdent", + "InferenceEngineIdent", + "LatencyPercentiles", + "MEIRecord", + "QuantizationTuple", + "TokenSeries", +] diff --git a/mindxtrain/eval/mei/score.py b/mindxtrain/eval/mei/score.py new file mode 100644 index 0000000000000000000000000000000000000000..eb44c5b546994cad33846a4fa7b7a577089df50b --- /dev/null +++ b/mindxtrain/eval/mei/score.py @@ -0,0 +1,356 @@ +"""MEI composite computation — pure functions over MEIRecord (spec §5). + +Each sub-index lives on [0, 1] with explicit anchor calibration. The +composite is a weighted geometric mean (spec §5.6) so a weak sub-index +collapses the score — exactly the property the promotion gate relies on. + +Worked-example anchors used as test ground truth: +- 30 tok/s decode → Dt ≈ 0.50 (spec §5.2: log10(30/3) / log10(300/3)) +- 100 tok/s decode → Dt ≈ 0.77 (spec §5.2) +- 8B Q4_K_M at ~5 GB → M ≈ 0.61 (spec §5.4) +- 70B Q4_K_M at ~40 GB → M ≈ 0.26 (spec §5.4) +""" + +from __future__ import annotations + +import math + +from pydantic import BaseModel, ConfigDict, Field + +from mindxtrain.eval.mei.anchors import ( + ANCHORS_V01, + PROMOTION_COMPOSITE_THRESHOLD, + PROMOTION_QUALITY_BAND_FLOOR, + PROMOTION_SUBINDEX_FLOOR, + QUALITY_BAND_EVALS, + MEIAnchors, + quality_band_weights, +) +from mindxtrain.eval.mei.record import MEIRecord + + +class ReferencePool(BaseModel): + """Random-baseline floors + Qwen3.5-flagship ceilings per spec §5.1. + + Min-max normalization (Open LLM Leaderboard v2 convention): + s_i = clamp((raw_i - random_i) / (Q3.5_flag_i - random_i), 0, 1) + """ + + model_config = ConfigDict(extra="forbid", frozen=True) + + version: str = "v0.1" + random_baselines: dict[str, float] = Field( + default_factory=lambda: { + # MMLU-Pro ten-choice: 10% random floor per Wang et al. + "mmlu_pro": 0.10, + # GPQA-Diamond four-choice: 25%. + "gpqa_diamond": 0.25, + # IFEval strict-prompt: 0 baseline (binary follow). + "ifeval_strict_prompt": 0.0, + # LiveBench-Reasoning: 0 baseline. + "livebench_reasoning": 0.0, + # BigCodeBench-Hard pass@1: 0 baseline. + "bigcodebench_hard_pass1": 0.0, + # MT-Bench-2-turn: 0 baseline (normalized 0-1). + "mt_bench_2turn": 0.0, + # mindX Agentic Battery — frozen reference; 0 baseline. + "mab": 0.0, + }, + ) + qwen35_flagship_ceilings: dict[str, float] = Field( + default_factory=lambda: { + # Initial calibration — refresh in Phase 6 with actual measurements. + "mmlu_pro": 0.75, + "gpqa_diamond": 0.55, + "ifeval_strict_prompt": 0.85, + "livebench_reasoning": 0.65, + "bigcodebench_hard_pass1": 0.45, + "mt_bench_2turn": 0.85, + "mab": 0.75, + }, + ) + + +REFERENCE_POOL_V01: ReferencePool = ReferencePool() + + +class MEIScore(BaseModel): + """Headline MEI score plus the disclosed sub-indices (spec §5.6). + + Per the spec, a composite without its five components is non- + conformant and must be rejected at intake — so this object always + carries both. + """ + + model_config = ConfigDict(extra="forbid", frozen=True) + + composite: float = Field(ge=0.0, le=1.0) + quality: float = Field(ge=0.0, le=1.0) + decode_throughput: float = Field(ge=0.0, le=1.0) + prefill_throughput: float = Field(ge=0.0, le=1.0) + memory: float = Field(ge=0.0, le=1.0) + energy: float = Field(ge=0.0, le=1.0) + quality_bands: dict[str, float] = Field(default_factory=dict) + mab_provisional: bool = True + anchors_version: str = "0.1" + notes: list[str] = Field(default_factory=list) + + +def _log_compress(value: float, floor: float, ceiling: float) -> float: + """Logarithmic [0, 1] compression of a value between floor and ceiling. + + `(log10(value) - log10(floor)) / (log10(ceiling) - log10(floor))` + clamped to [0, 1]. Values <= 0 collapse to 0 (the floor) to keep the + composite geometric mean well-defined. + """ + if value <= 0.0: + return 0.0 + if floor <= 0.0 or ceiling <= floor: + msg = f"degenerate anchors: floor={floor}, ceiling={ceiling}" + raise ValueError(msg) + raw = (math.log10(value) - math.log10(floor)) / (math.log10(ceiling) - math.log10(floor)) + return max(0.0, min(1.0, raw)) + + +def _log_compress_inverse(value: float, floor: float, ceiling: float) -> float: + """Inverse-log compression for sub-indices where smaller is better + (memory footprint, energy per token). At `floor` → 1.0, at `ceiling` → 0.0. + """ + if value <= 0.0: + return 1.0 + if floor <= 0.0 or ceiling <= floor: + msg = f"degenerate anchors: floor={floor}, ceiling={ceiling}" + raise ValueError(msg) + raw = (math.log10(ceiling) - math.log10(value)) / (math.log10(ceiling) - math.log10(floor)) + return max(0.0, min(1.0, raw)) + + +def _geometric_mean(values: list[float], weights: list[float] | None = None) -> float: + """Weighted geometric mean. Returns 0.0 if any value is 0.0 (which is + the spec's intended behaviour — a single weak sub-index collapses the + composite).""" + if not values: + return 0.0 + if any(v <= 0.0 for v in values): + return 0.0 + if weights is None: + return math.exp(sum(math.log(v) for v in values) / len(values)) + if len(weights) != len(values): + msg = f"weight length {len(weights)} != values length {len(values)}" + raise ValueError(msg) + total_w = sum(weights) + if total_w <= 0: + return 0.0 + return math.exp( + sum(w * math.log(v) for v, w in zip(values, weights, strict=True)) / total_w, + ) + + +def _normalize_eval(name: str, raw: float, pool: ReferencePool) -> float: + """Min-max against the reference pool (Open LLM Leaderboard v2 form).""" + floor = pool.random_baselines.get(name, 0.0) + ceiling = pool.qwen35_flagship_ceilings.get(name, 1.0) + if ceiling <= floor: + return 0.0 + norm = (raw - floor) / (ceiling - floor) + return max(0.0, min(1.0, norm)) + + +def quality_subindex( + quality_raw: dict[str, float], + pool: ReferencePool, + *, + mab_provisional: bool, +) -> tuple[float, dict[str, float]]: + """Compute Q as the weighted geometric mean across the four bands. + + Within each band, evaluations are arithmetic-meaned; bands themselves + are geometric-meaned (penalizes catastrophic weakness in any one band + per spec §5.1). Returns (Q, per-band scores) so callers can disclose + the band breakdown. + + When `mab_provisional` is True, the Agentic band weight redistributes + across the other three bands (spec §9). + """ + weights = quality_band_weights(mab_provisional=mab_provisional) + band_scores: dict[str, float] = {} + for band, evals in QUALITY_BAND_EVALS.items(): + # If the band is the Agentic band under provisional, skip + # entirely — its weight is 0.0 and any non-zero in `band_scores` + # would carry through with weight 0, polluting the geo-mean. + if mab_provisional and band == "agentic": + continue + normalized = [ + _normalize_eval(name, quality_raw[name], pool) + for name in evals + if name in quality_raw + ] + if not normalized: + band_scores[band] = 0.0 + continue + band_scores[band] = sum(normalized) / len(normalized) + + # Build the weighted geo-mean inputs in stable order. + used_bands = [b for b in weights if weights[b] > 0.0] + values = [band_scores.get(b, 0.0) for b in used_bands] + band_weights_in_order = [weights[b] for b in used_bands] + q = _geometric_mean(values, weights=band_weights_in_order) + return q, band_scores + + +def decode_throughput_subindex(record: MEIRecord, anchors: MEIAnchors = ANCHORS_V01) -> float: + """`Dt`: geometric mean of Ttg across context tiers, log-compressed. + + Spec §5.2: penalizes catastrophic long-context degradation more than + an arithmetic mean would. + """ + ttg_values = [t.ttg_tok_s for t in record.tier_measurements if t.ttg_tok_s > 0] + if not ttg_values: + return 0.0 + geo_ttg = _geometric_mean(ttg_values) + return _log_compress(geo_ttg, anchors.t_floor_decode, anchors.t_ceiling_decode) + + +def prefill_throughput_subindex(record: MEIRecord, anchors: MEIAnchors = ANCHORS_V01) -> float: + """`Pp`: log-compressed prefill rate. We use the geometric mean of + Tpp across context tiers for symmetry with Dt, even though prefill + typically scales differently — the four-tier picture captures both. + """ + tpp_values = [t.tpp_tok_s for t in record.tier_measurements if t.tpp_tok_s > 0] + if not tpp_values: + return 0.0 + geo_tpp = _geometric_mean(tpp_values) + return _log_compress(geo_tpp, anchors.p_floor_prefill, anchors.p_ceiling_prefill) + + +def memory_subindex(record: MEIRecord, anchors: MEIAnchors = ANCHORS_V01) -> float: + """`M`: inverse-log of peak memory at the 32K working point. + + Spec §5.4: rewards small, dense memory footprints. A Qwen3-8B Q4_K_M + at ~5 GB scores ≈ 0.61 per the spec example. + """ + return _log_compress_inverse(record.peak_memory_gb, anchors.m_floor_gb, anchors.m_ceiling_gb) + + +def energy_subindex(record: MEIRecord, anchors: MEIAnchors = ANCHORS_V01) -> float: + """`E`: inverse-log of joules per useful decoded token. + + When energy was not measured (and not estimated), returns 0.5 with a + note flag — the spec's "where direct measurement is unavailable" + fallback. The score uses the bandwidth-bound proxy when + `energy_estimated=True` and the value is set. + """ + j = record.energy_j_per_useful_token + if j is None: + # Neutral midpoint when truly unmeasured. The CI will widen + # accordingly via the bootstrap step downstream. + return 0.5 + return _log_compress_inverse(j, anchors.j_floor_per_tok, anchors.j_ceiling_per_tok) + + +def composite_mei( + q: float, dt: float, pp: float, m: float, e: float, anchors: MEIAnchors = ANCHORS_V01, +) -> float: + """`MEI = Q^wQ · Dt^wDt · Pp^wPp · M^wM · E^wE` (spec §5.6).""" + values = [q, dt, pp, m, e] + weights = [anchors.w_q, anchors.w_dt, anchors.w_pp, anchors.w_m, anchors.w_e] + return _geometric_mean(values, weights=weights) + + +def score_record( + record: MEIRecord, + pool: ReferencePool = REFERENCE_POOL_V01, + anchors: MEIAnchors = ANCHORS_V01, +) -> MEIScore: + """End-to-end score function — the public entry point.""" + q, band_scores = quality_subindex( + record.quality_raw, pool, mab_provisional=record.mab_provisional, + ) + dt = decode_throughput_subindex(record, anchors) + pp = prefill_throughput_subindex(record, anchors) + m = memory_subindex(record, anchors) + e = energy_subindex(record, anchors) + composite = composite_mei(q, dt, pp, m, e, anchors) + + notes: list[str] = [] + if record.mab_provisional: + notes.append( + "Agentic Battery v1.0 not yet sealed — 0.35 band weight " + "redistributed equally across the other three Q bands.", + ) + if record.energy_j_per_useful_token is None: + notes.append("Energy not measured; E sub-index defaulted to 0.5 neutral.") + elif record.energy_estimated: + notes.append("Energy derived from bandwidth-bound proxy, not direct measurement.") + + return MEIScore( + composite=composite, + quality=q, + decode_throughput=dt, + prefill_throughput=pp, + memory=m, + energy=e, + quality_bands=dict(band_scores), + mab_provisional=record.mab_provisional, + anchors_version=anchors.version, + notes=notes, + ) + + +def is_promotable( + score: MEIScore, + prior_promoted: MEIScore | None = None, +) -> tuple[bool, list[str]]: + """Three-gate promotion logic per spec §8. + + Returns (promotable, reasons). When `promotable=False`, the reasons + list enumerates every failing gate (not short-circuited) so callers + can surface all problems at once. + """ + reasons: list[str] = [] + if score.composite < PROMOTION_COMPOSITE_THRESHOLD: + reasons.append( + f"composite {score.composite:.3f} < {PROMOTION_COMPOSITE_THRESHOLD} threshold", + ) + subindices = { + "quality": score.quality, + "decode_throughput": score.decode_throughput, + "prefill_throughput": score.prefill_throughput, + "memory": score.memory, + "energy": score.energy, + } + for name, value in subindices.items(): + if value < PROMOTION_SUBINDEX_FLOOR: + reasons.append( + f"sub-index {name}={value:.3f} below {PROMOTION_SUBINDEX_FLOOR} floor", + ) + # The Agentic band must clear its own floor independently — but only + # when the MAB is sealed. While provisional, the 0.35 weight already + # redistributed and the composite-level check carries the load. + if not score.mab_provisional: + agentic = score.quality_bands.get("agentic", 0.0) + if agentic < PROMOTION_QUALITY_BAND_FLOOR: + reasons.append( + f"Agentic band {agentic:.3f} below {PROMOTION_QUALITY_BAND_FLOOR} floor", + ) + if prior_promoted is not None and score.composite <= prior_promoted.composite: + reasons.append( + f"composite {score.composite:.3f} does not exceed currently-promoted " + f"{prior_promoted.composite:.3f}", + ) + return (not reasons, reasons) + + +__all__ = [ + "REFERENCE_POOL_V01", + "MEIScore", + "ReferencePool", + "composite_mei", + "decode_throughput_subindex", + "energy_subindex", + "is_promotable", + "memory_subindex", + "prefill_throughput_subindex", + "quality_subindex", + "score_record", +] diff --git a/mindxtrain/eval/mei/tokenizer.py b/mindxtrain/eval/mei/tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..bf2b5ab75bda78a19b183a58ff9af0c60e19bae2 --- /dev/null +++ b/mindxtrain/eval/mei/tokenizer.py @@ -0,0 +1,158 @@ +"""Canonical tokenizer wrapper (spec Rule 3.1). + +The model's own `tokenizer.json` loaded via the HuggingFace `tokenizers` +(Rust) library is the authoritative token counter. Every reported token +figure carries the tokenizer revision hash, so cross-run comparisons +remain honest when tokenizer vocabularies differ (Qwen3.5's 248k vs +Qwen2.5's 152k vs Llama 3's 128k vocabulary). + +The two cross-checks that matter operationally: + +1. **Scaffold accounting (Rule 3.2)** — the ChatML wrapping adds 10-25 + tokens per turn. `count_with_scaffold` reports both inclusive and + content-only counts so a "tok/s" figure can be disambiguated. +2. **Bytes-per-second (Rule 3.3)** — Qwen3.5 compresses English to ~3.5 + chars/tok where Llama 3 sits at ~4.0; tokenizer-relative throughput + alone is misleading. `bytes_per_decoded_token` is the cross-vocabulary + honest answer. + +Lazy import: this module loads without `tokenizers` installed; callers +get a clear RuntimeError pointing at `uv sync --extra ml`. +""" + +from __future__ import annotations + +import hashlib +from functools import lru_cache +from pathlib import Path +from typing import Any + + +def _require_tokenizers() -> Any: + try: + import tokenizers # type: ignore + except ImportError as exc: + msg = ( + "MEI canonical tokenizer requires the `tokenizers` library — " + "run `uv sync --extra ml`." + ) + raise RuntimeError(msg) from exc + return tokenizers + + +@lru_cache(maxsize=8) +def _load_tokenizer(model_id_or_path: str) -> Any: + """Load Tokenizer.from_file(<dir>/tokenizer.json) or from_pretrained. + + Cached so repeated count calls don't re-parse the JSON. The cache key + is the input string — callers can either pass a local path or an HF + Hub repo ID; both resolve through the same code path. + """ + tok_mod = _require_tokenizers() + path = Path(model_id_or_path).expanduser() + if path.is_dir() and (path / "tokenizer.json").exists(): + return tok_mod.Tokenizer.from_file(str(path / "tokenizer.json")) + if path.is_file() and path.name == "tokenizer.json": + return tok_mod.Tokenizer.from_file(str(path)) + # Fall back to HF Hub lookup via the transformers convenience layer if + # available; otherwise raise so the caller knows the lookup failed. + try: + return tok_mod.Tokenizer.from_pretrained(model_id_or_path) + except Exception as exc: + msg = ( + f"could not load tokenizer for {model_id_or_path!r}: " + f"neither a local tokenizer.json nor an HF Hub repo could be " + f"resolved ({type(exc).__name__}: {exc})" + ) + raise RuntimeError(msg) from exc + + +def tokenizer_revision(model_id_or_path: str) -> str: + """Stable hash over the tokenizer.json bytes (spec Rule 3.1). + + Every reported token figure carries this hash so a measurement run + can be replayed and cross-checked against the exact tokenizer + revision it consumed. + """ + path = Path(model_id_or_path).expanduser() + tj = path / "tokenizer.json" if path.is_dir() else path + if tj.exists() and tj.is_file(): + digest = hashlib.blake2b(tj.read_bytes(), digest_size=16).hexdigest() + return f"local:{digest}" + # For HF Hub references we can't BLAKE the local file (it isn't + # materialized yet at this layer). Surface the repo identity instead. + return f"hub:{model_id_or_path}" + + +def encode_with_revision( + text: str, model_id_or_path: str, *, add_special_tokens: bool = False, +) -> tuple[list[int], str]: + """Return (token_ids, tokenizer_revision_hash). Rule 3.1.""" + tok = _load_tokenizer(model_id_or_path) + encoding = tok.encode(text, add_special_tokens=add_special_tokens) + return list(encoding.ids), tokenizer_revision(model_id_or_path) + + +def _chatml_render(messages: list[dict[str, str]]) -> str: + """Render a ChatML conversation the way Qwen tokenizers expect. + + The MEI counts every token the model processes, *including* scaffold + (Rule 3.2). We render explicitly here rather than delegating to a + tokenizer's `apply_chat_template` because we want byte-stable output + regardless of template revisions — the count is what matters. + """ + parts: list[str] = [] + for m in messages: + role = m.get("role", "user") + content = m.get("content", "") + parts.append(f"<|im_start|>{role}\n{content}<|im_end|>\n") + # Trailing assistant prompt — the model's generation prefix. + parts.append("<|im_start|>assistant\n") + return "".join(parts) + + +def count_with_scaffold( + messages: list[dict[str, str]], + model_id_or_path: str, + *, + include_scaffold: bool = True, +) -> int: + """Token count for a chat-style payload (Rule 3.2). + + When `include_scaffold=True` (the operationally-honest default), the + ChatML `<|im_start|>`, `<|im_end|>` markers and role headers are + counted — matches what the model actually processes. When False, the + count strips structural overhead — suitable for user-facing + reporting only, never for tok/s denominators. + """ + if include_scaffold: + rendered = _chatml_render(messages) + else: + rendered = "\n".join(m.get("content", "") for m in messages) + ids, _ = encode_with_revision(rendered, model_id_or_path) + return len(ids) + + +def bytes_per_decoded_token( + token_ids: list[int], model_id_or_path: str, +) -> float: + """UTF-8 bytes per decoded token (Rule 3.3). + + Cross-tokenizer-invariant cross-check. A Qwen 100 tok/s and a Llama + 100 tok/s produce different amounts of useful output; this number is + how MEI keeps cross-vocab comparisons honest. Returns 0.0 for empty + token lists. + """ + if not token_ids: + return 0.0 + tok = _load_tokenizer(model_id_or_path) + decoded = tok.decode(token_ids) + return len(decoded.encode("utf-8")) / len(token_ids) + + +__all__ = [ + "bytes_per_decoded_token", + "count_with_scaffold", + "encode_with_revision", + "tokenizer_revision", +] diff --git a/mindxtrain/eval/mei/xei.py b/mindxtrain/eval/mei/xei.py new file mode 100644 index 0000000000000000000000000000000000000000..0ebc6f75907e84563c71eedf1f3f7a269d14f962 --- /dev/null +++ b/mindxtrain/eval/mei/xei.py @@ -0,0 +1,391 @@ +"""XEI — mindXtrain Efficiency Index (training-side companion, spec §6). + +Where MEI scores inference behaviour, XEI scores the *production process* +producing each checkpoint. Four components: + +1. **Training throughput** — tokens/sec/device + global, expressed as + Model FLOPs Utilization (MFU). PaLM 540B reached 46.2% MFU; the spec's + alpha target is ≥ 35%. +2. **Optimization health** — gradient-norm stability + power-law loss- + curve fit `L(D) = E + A/D^α`. +3. **Convergence rate** — slope of validation loss vs log-tokens-trained. +4. **Cost per quality unit** — $ or GPU-hours to advance the held-out + MAB probe by a fixed delta. + +Pure math — no `torch`, no `numpy`. Inputs are plain numbers and lists; +outputs are dataclasses the training callback emits per checkpoint. +""" + +from __future__ import annotations + +import math +import statistics +from dataclasses import dataclass + +# ---- FLOPs per token + MFU -------------------------------------------------- + +# Spec §6: FLOPs per token follows `6N + 12 L H Q T` decomposition where +# N is non-embedding params, L layers, H attention heads, Q head dim, +# T sequence length. The `6N` covers the forward + backward through the +# linear layers; `12 L H Q T` is the attention computation that scales +# linearly in layers and quadratically (in T) in the sequence dimension. +# (The 12 absorbs the 4 attention matrix multiplies × 3 for fwd+bwd.) + + +def flops_per_token( + *, params_nonembed: int, num_layers: int, num_heads: int, + head_dim: int, seq_len: int, +) -> float: + """Compute FLOPs per training token per the §6 decomposition. + + Returns the total floating-point operations a single token traverses + in one forward+backward pass. PaLM-style estimate; matches the + formula used by Megatron-LM's MFU calculation. + """ + if params_nonembed <= 0 or num_layers <= 0 or num_heads <= 0: + msg = "params, layers, and heads must be positive" + raise ValueError(msg) + return 6.0 * params_nonembed + 12.0 * num_layers * num_heads * head_dim * seq_len + + +def compute_mfu( + *, tokens_per_sec_global: float, num_devices: int, + peak_device_flops: float, fpt: float, +) -> float: + """Model FLOPs Utilization. + + `MFU = (observed_tokens_per_sec * FLOPs_per_token) / + (num_devices * peak_device_FLOPS)` + + Returns a value in [0, 1]. PaLM 540B published 0.462; well-tuned + Megatron 0.50-0.55; Llama-3.1 0.38-0.43; DeepSeek-V3 on H800 ≈ 0.38. + The alpha target is ≥ 0.35; below 0.30 flags a config defect. + """ + if num_devices <= 0 or peak_device_flops <= 0 or fpt <= 0: + msg = "num_devices, peak_device_flops, and fpt must be positive" + raise ValueError(msg) + return (tokens_per_sec_global * fpt) / (num_devices * peak_device_flops) + + +# ---- Optimization health (gradient norm + loss-curve fit) ------------------ + + +@dataclass(frozen=True) +class GradNormSpike: + """One detected spike in the gradient-norm series.""" + + step: int + value: float + baseline: float + ratio: float # value / baseline + + +def detect_grad_norm_spikes( + grad_norms: list[float], + *, + spike_ratio: float = 10.0, + baseline_window: int = 50, +) -> list[GradNormSpike]: + """Return every step where grad-norm exceeds spike_ratio × trailing baseline. + + Spec §6: "spikes exceeding ten times the trailing baseline trigger a + step-skip protocol and are logged as instability events." We treat + the trailing baseline as the median of the last `baseline_window` + pre-spike values for robustness against tail outliers. + """ + if spike_ratio <= 1.0: + msg = "spike_ratio must exceed 1.0 (baseline is the comparator)" + raise ValueError(msg) + spikes: list[GradNormSpike] = [] + for i, g in enumerate(grad_norms): + if i < baseline_window: + continue + window = grad_norms[max(0, i - baseline_window):i] + if not window: + continue + baseline = statistics.median(window) + if baseline <= 0: + continue + ratio = g / baseline + if ratio >= spike_ratio: + spikes.append(GradNormSpike(step=i, value=g, baseline=baseline, ratio=ratio)) + return spikes + + +def grad_norm_stability( + grad_norms: list[float], + *, + target_low: float = 0.5, + target_high: float = 2.0, +) -> float: + """Fraction of grad-norm samples in the healthy [0.5, 2.0] band. + + Spec §6: "the gradient norm should remain in the 0.5–2.0 band once + linear warmup completes." Higher = more stable optimization. + Returns a value in [0, 1]. + """ + if not grad_norms: + return 0.0 + in_band = sum(1 for g in grad_norms if target_low <= g <= target_high) + return in_band / len(grad_norms) + + +@dataclass(frozen=True) +class PowerLawFit: + """Result of a `L(D) = E + A / D^α` fit to a loss curve.""" + + e: float + a: float + alpha: float + residual_rms: float # root-mean-square residual after fit + + +def fit_loss_power_law( + tokens_trained: list[float], + losses: list[float], + *, + alpha_initial: float = 0.30, +) -> PowerLawFit: + """Fit a simple power-law `L = E + A / D^α` to (tokens, loss) points. + + Spec §6 expects α ≈ 0.28-0.34 on the token axis. We use a coarse + iterative refinement (no scipy dep): linearise `log(L - E_guess) = + log(A) - α · log(D)` and least-squares fit slope and intercept for a + few candidate `E` floors, picking the fit with the smallest residual. + Good enough for "is this curve power-law-shaped" rather than a fully + principled non-linear regression. + """ + if len(tokens_trained) != len(losses): + msg = "tokens_trained and losses must align" + raise ValueError(msg) + if len(tokens_trained) < 4: + msg = "need at least 4 (tokens, loss) points for a meaningful fit" + raise ValueError(msg) + if min(tokens_trained) <= 0: + msg = "tokens_trained must all be > 0" + raise ValueError(msg) + + min_loss = min(losses) + best: PowerLawFit | None = None + # Sweep E candidates below min_loss in a multiplicative grid — the + # true asymptote can be anywhere from 0 (no irreducible loss) to + # arbitrarily close to min_loss (the curve is near its floor). A + # coarse absolute grid misses the close-to-floor case for curves + # whose true E is near min_loss. + multiplicative = [0.0, 0.001, 0.01, 0.05, 0.1, 0.2, 0.5, 0.9] + e_grid: list[float] = [0.0] + [ + max(0.0, min_loss * (1.0 - frac)) for frac in multiplicative + ] + for e_guess in e_grid: + residuals_squared: list[float] = [] + # Skip points where loss <= e_guess (log is undefined). + log_d: list[float] = [] + log_y: list[float] = [] + for d, lo in zip(tokens_trained, losses, strict=True): + if lo <= e_guess: + continue + log_d.append(math.log(d)) + log_y.append(math.log(lo - e_guess)) + if len(log_d) < 4: + continue + # Least-squares slope of log_y vs log_d. slope = -α. + n = len(log_d) + mean_x = sum(log_d) / n + mean_y = sum(log_y) / n + var_x = sum((x - mean_x) ** 2 for x in log_d) + if var_x == 0: + continue + cov_xy = sum((x - mean_x) * (y - mean_y) for x, y in zip(log_d, log_y, strict=True)) + # Degenerate fit: flat loss curve (constant losses) has zero + # covariance and would yield α ≈ 0, which is not a power law. + # Skip this candidate; a power-law-shaped curve must show some + # log-log slope. + if abs(cov_xy) < 1e-12: + continue + slope = cov_xy / var_x + alpha = -slope + # A power law needs a positive exponent — α ≤ 0 means the loss + # rises (or stays flat) with more tokens, the opposite of what + # the §6 fit expects. + if alpha <= 1e-6: + continue + log_a = mean_y - slope * mean_x + a = math.exp(log_a) + # Residual in original (not log) space. + for d, lo in zip(tokens_trained, losses, strict=True): + predicted = e_guess + a / (d ** alpha) if alpha != 0 else e_guess + a + residuals_squared.append((lo - predicted) ** 2) + residual_rms = math.sqrt(sum(residuals_squared) / len(residuals_squared)) + fit = PowerLawFit(e=e_guess, a=a, alpha=alpha, residual_rms=residual_rms) + if best is None or fit.residual_rms < best.residual_rms: + best = fit + if best is None: + msg = "no valid power-law fit found (all loss values below every E candidate)" + raise ValueError(msg) + return best + + +def power_law_health(fit: PowerLawFit, *, expected_alpha_low: float = 0.20, + expected_alpha_high: float = 0.40) -> float: + """Score how close a fit's α is to the spec's expected band [0.20, 0.40]. + + Returns 1.0 when α is squarely in the band, ramping linearly to 0.0 + outside it. The spec's "expected α ≈ 0.28-0.34" is the centre; we + widen the acceptance to [0.20, 0.40] because exact-band fits are + sensitive to noise. + """ + a = fit.alpha + centre = 0.5 * (expected_alpha_low + expected_alpha_high) + half_width = 0.5 * (expected_alpha_high - expected_alpha_low) + if half_width <= 0: + return 0.0 + distance = abs(a - centre) + if distance <= half_width: + return 1.0 + # Ramp down linearly over another half-width; clamp at 0. + return max(0.0, 1.0 - (distance - half_width) / half_width) + + +# ---- Convergence rate ------------------------------------------------------ + + +def convergence_slope_per_log_tokens( + tokens_trained: list[float], + val_losses: list[float], +) -> float: + """Slope of validation loss against log10(tokens_trained). + + Negative is good — loss falling as tokens accumulate. Magnitude + indicates pace. Returns 0.0 for ill-conditioned input (single point, + flat loss, zero tokens). + """ + if len(tokens_trained) != len(val_losses): + msg = "tokens_trained and val_losses must align" + raise ValueError(msg) + if len(tokens_trained) < 2: + return 0.0 + # Drop any non-positive token counts (can't log). + pairs = [(d, lo) for d, lo in zip(tokens_trained, val_losses, strict=True) if d > 0] + if len(pairs) < 2: + return 0.0 + log_d = [math.log10(d) for d, _ in pairs] + y = [lo for _, lo in pairs] + n = len(log_d) + mean_x = sum(log_d) / n + mean_y = sum(y) / n + var_x = sum((x - mean_x) ** 2 for x in log_d) + if var_x == 0: + return 0.0 + cov_xy = sum((x - mean_x) * (yi - mean_y) for x, yi in zip(log_d, y, strict=True)) + return cov_xy / var_x + + +# ---- Cost per quality unit ------------------------------------------------- + + +def cost_per_quality_unit(*, gpu_hours: float, mab_delta: float) -> float: + """GPU-hours required to advance the held-out MAB probe by 1.0 of score. + + Returns ∞ when `mab_delta` is non-positive (no improvement — every + dollar is wasted; the run should stop). The metric is directly the + spec's "analogue of intelligence per dollar applied to training." + """ + if mab_delta <= 0: + return float("inf") + if gpu_hours < 0: + msg = "gpu_hours cannot be negative" + raise ValueError(msg) + return gpu_hours / mab_delta + + +# ---- XEIRecord — emitted per checkpoint ------------------------------------ + + +@dataclass(frozen=True) +class XEIRecord: + """One XEI score per checkpoint interval. + + Carried in the training callback so each checkpoint emits an + `xei.jsonl` row alongside the existing manifest pipeline. + """ + + mfu: float + hfu: float | None + grad_norm_stability: float + grad_norm_spikes: int + loss_power_law_alpha: float | None + loss_power_law_residual_rms: float | None + convergence_slope: float + cost_per_quality_unit: float | None + + +def score_xei( + *, + tokens_per_sec_global: float, + num_devices: int, + peak_device_flops: float, + fpt: float, + grad_norms: list[float], + loss_history: list[float] | None = None, + tokens_history: list[float] | None = None, + val_losses: list[float] | None = None, + val_tokens: list[float] | None = None, + gpu_hours: float | None = None, + mab_delta: float | None = None, + hfu: float | None = None, +) -> XEIRecord: + """End-to-end XEI scoring for one checkpoint snapshot. + + All series arguments are lists of numbers — the caller is responsible + for sampling them from the training loop. No torch import here. + """ + mfu = compute_mfu( + tokens_per_sec_global=tokens_per_sec_global, + num_devices=num_devices, + peak_device_flops=peak_device_flops, + fpt=fpt, + ) + stability = grad_norm_stability(grad_norms) + spikes = detect_grad_norm_spikes(grad_norms) + + fit: PowerLawFit | None = None + if loss_history and tokens_history and len(loss_history) >= 4: + try: + fit = fit_loss_power_law(tokens_history, loss_history) + except (ValueError, ZeroDivisionError): + fit = None + + slope = 0.0 + if val_losses and val_tokens: + slope = convergence_slope_per_log_tokens(val_tokens, val_losses) + + cpqu: float | None = None + if gpu_hours is not None and mab_delta is not None: + cpqu = cost_per_quality_unit(gpu_hours=gpu_hours, mab_delta=mab_delta) + + return XEIRecord( + mfu=mfu, + hfu=hfu, + grad_norm_stability=stability, + grad_norm_spikes=len(spikes), + loss_power_law_alpha=fit.alpha if fit else None, + loss_power_law_residual_rms=fit.residual_rms if fit else None, + convergence_slope=slope, + cost_per_quality_unit=cpqu, + ) + + +__all__ = [ + "GradNormSpike", + "PowerLawFit", + "XEIRecord", + "compute_mfu", + "convergence_slope_per_log_tokens", + "cost_per_quality_unit", + "detect_grad_norm_spikes", + "fit_loss_power_law", + "flops_per_token", + "grad_norm_stability", + "power_law_health", + "score_xei", +] diff --git a/mindxtrain/eval/persona_regression.py b/mindxtrain/eval/persona_regression.py new file mode 100644 index 0000000000000000000000000000000000000000..cbc1a3a7c7037f6090ce6fad44ae409fad57c703 --- /dev/null +++ b/mindxtrain/eval/persona_regression.py @@ -0,0 +1,59 @@ +"""Persona regression — does the post-training model still sound like Codephreak? + +Compares a sample of model generations against a baseline JSONL of +held-out persona examples using sentence-transformer cosine similarity. +""" + +from __future__ import annotations + +import json +from pathlib import Path + + +def regression_score( + samples: list[str], + baseline_jsonl: Path, + *, + model: str = "sentence-transformers/all-MiniLM-L6-v2", +) -> float: + """Return persona-similarity score in [0, 1]; 1.0 = identical voice. + + `samples` is a list of generated strings (caller is responsible for + producing them via inference). `baseline_jsonl` is one persona example + per line (each line a JSON object with a `text` field). + """ + try: + import numpy as np + from sentence_transformers import SentenceTransformer + except ImportError as exc: + msg = "sentence-transformers + numpy not installed; run `uv sync --extra data`." + raise RuntimeError(msg) from exc + + baseline_texts: list[str] = [] + with Path(baseline_jsonl).open() as fh: + for line in fh: + line = line.strip() + if not line: + continue + try: + obj = json.loads(line) + except json.JSONDecodeError: + continue + text = obj.get("text") or obj.get("content") or "" + if text: + baseline_texts.append(text) + + if not samples or not baseline_texts: + return 0.0 + + enc = SentenceTransformer(model) + sample_embs = enc.encode(samples, normalize_embeddings=True) + baseline_embs = enc.encode(baseline_texts, normalize_embeddings=True) + + # Per-sample max cosine vs. any baseline; mean across samples. + sims = sample_embs @ baseline_embs.T + per_sample_max = sims.max(axis=1) + return float(np.mean(per_sample_max)) + + +__all__ = ["regression_score"] diff --git a/mindxtrain/eval/tau_bench.py b/mindxtrain/eval/tau_bench.py new file mode 100644 index 0000000000000000000000000000000000000000..a2fd846712449cf4039f25b5392b26fb236f8f17 --- /dev/null +++ b/mindxtrain/eval/tau_bench.py @@ -0,0 +1,49 @@ +"""τ-Bench adapter — multi-turn agentic evaluation (Sierra).""" + +from __future__ import annotations + +import json +import shutil +import subprocess +from pathlib import Path + + +def _tau_bench_available() -> bool: + return shutil.which("tau-bench") is not None or shutil.which("tau_bench") is not None + + +def run_tau_bench( + checkpoint: Path, + *, + scenario: str = "airline", + out_dir: Path | None = None, +) -> dict[str, float]: + """Run τ-Bench against `checkpoint`; return scenario scores.""" + if not _tau_bench_available(): + msg = "tau_bench not installed; install per https://github.com/sierra-research/tau-bench" + raise RuntimeError(msg) + out_dir = Path(out_dir or checkpoint / "tau_bench") + out_dir.mkdir(parents=True, exist_ok=True) + bin_name = shutil.which("tau-bench") or shutil.which("tau_bench") or "tau-bench" + cmd = [ + bin_name, + "--task", + scenario, + "--model", + str(checkpoint), + "--output", + str(out_dir / "results.json"), + ] + subprocess.run(cmd, check=True) + rp = out_dir / "results.json" + if not rp.exists(): + return {} + raw = json.loads(rp.read_text()) + out: dict[str, float] = {} + for k, v in raw.items(): + if isinstance(v, int | float): + out[k] = float(v) + return out + + +__all__ = ["run_tau_bench"] diff --git a/mindxtrain/governance/__init__.py b/mindxtrain/governance/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..00050dc2fa8eab0d86ca8f54455045c367f8e163 --- /dev/null +++ b/mindxtrain/governance/__init__.py @@ -0,0 +1,57 @@ +"""Governance — classroom / boardroom / dojo. + +A clean-room reimplementation (from the behaviour of github.com/openmindx/openmind — +Boardroom multi-model consensus + Dojo head-to-head evaluation) of the decision layer +that governs training: + +- **Classroom** — where an actor trains; it *graduates* when imprint/quality criteria pass. +- **Boardroom** — a panel of **any number** of role-based members that convenes on a motion + (e.g. "promote the graduated actor") and produces a consensus. Governs the classroom. +- **Dojo** — the boardroom's dispute-settlement extension. When the boardroom is split, a dojo + of a **prime number** of judges settles it head-to-head; prime guarantees a strict majority, + so a dispute always resolves with no tie. + +Pure stdlib + pydantic; importable on a base install. Members vote via supplied callables / +scores, so the whole layer is testable without any LLM, and can later be backed by real models. +""" + +from __future__ import annotations + +from mindxtrain.governance.boardroom import ( + PRESET_BOARDS, + ROLES, + Boardroom, + BoardroomDecision, + Member, + Vote, +) +from mindxtrain.governance.classroom import Graduation, graduate +from mindxtrain.governance.dojo import Dojo, DojoVerdict, settle_dispute +from mindxtrain.governance.panel import ( + Deliberation, + deliberate, + model_ballot, + model_judge_ballot, +) +from mindxtrain.governance.primes import is_prime, nearest_prime, next_prime + +__all__ = [ + "PRESET_BOARDS", + "ROLES", + "Boardroom", + "BoardroomDecision", + "Deliberation", + "Dojo", + "DojoVerdict", + "Graduation", + "Member", + "Vote", + "deliberate", + "graduate", + "is_prime", + "model_ballot", + "model_judge_ballot", + "nearest_prime", + "next_prime", + "settle_dispute", +] diff --git a/mindxtrain/governance/boardroom.py b/mindxtrain/governance/boardroom.py new file mode 100644 index 0000000000000000000000000000000000000000..186a543631c2906fae3ad29640b3ba66078b88b1 --- /dev/null +++ b/mindxtrain/governance/boardroom.py @@ -0,0 +1,150 @@ +"""Boardroom — any-N role-based consensus over a motion. + +A boardroom is a panel of members, each in an advisor role, that convenes on a motion +(e.g. "promote the graduated actor") and casts a vote. The decision aggregates the votes +into approved / rejected / disputed. A boardroom can be **any number** of members; when it +is *disputed* (a tie, or below quorum) the dispute escalates to a prime-sized dojo. +""" + +from __future__ import annotations + +import math +from collections.abc import Callable +from typing import Literal + +from pydantic import BaseModel, ConfigDict, Field + +Role = Literal["advocate", "critic", "analyst", "devils_advocate", "expert", "generalist"] +Vote = Literal["approve", "reject", "abstain"] +Outcome = Literal["approved", "rejected", "disputed"] + +ROLES: tuple[Role, ...] = ( + "advocate", "critic", "analyst", "devils_advocate", "expert", "generalist", +) + +# Preset boards mirror OpenMind's named boards (reimplemented, not copied). +PRESET_BOARDS: dict[str, tuple[Role, ...]] = { + "classic_triad": ("advocate", "critic", "analyst"), + "devils_court": ("advocate", "devils_advocate", "critic", "analyst"), + "full_board": ROLES, + "peer_review": ("expert", "expert", "generalist"), +} + + +class Member(BaseModel): + """One boardroom advisor. `model` optionally names the backing LLM.""" + + model_config = ConfigDict(extra="forbid", frozen=True) + + id: str + role: Role = "generalist" + model: str = "" + + +class BoardroomDecision(BaseModel): + """Aggregated outcome of a convened motion.""" + + model_config = ConfigDict(extra="forbid", frozen=True) + + motion: str + members: list[str] + votes: dict[str, Vote] + approvals: int + rejections: int + abstentions: int + outcome: Outcome + rationale: str + disputed: bool = Field(description="True when the boardroom could not settle (tie / no quorum)") + + +def board_from_preset(name: str, *, model: str = "") -> list[Member]: + """Build a list of Members from a preset board name (roles get indexed ids).""" + roles = PRESET_BOARDS.get(name) + if roles is None: + msg = f"unknown preset board {name!r}; available: {', '.join(sorted(PRESET_BOARDS))}" + raise KeyError(msg) + return [Member(id=f"{r}-{i}", role=r, model=model) for i, r in enumerate(roles)] + + +class Boardroom(BaseModel): + """An any-N consensus panel governing the classroom.""" + + model_config = ConfigDict(extra="forbid", frozen=True) + + members: list[Member] + quorum: float = Field( + default=0.5, ge=0.0, le=1.0, + description="min fraction of members that must cast a decisive (non-abstain) vote", + ) + + def convene( + self, + motion: str, + ballot: dict[str, Vote] | Callable[[Member, str], Vote], + ) -> BoardroomDecision: + """Convene the board on a motion and tally the vote. + + `ballot` is either an explicit `{member_id: vote}` map or a callable + `(member, motion) -> vote`. Missing members abstain. + """ + if not self.members: + msg = "a boardroom needs at least one member to convene" + raise ValueError(msg) + + votes: dict[str, Vote] = {} + for m in self.members: + if callable(ballot): + votes[m.id] = ballot(m, motion) + else: + votes[m.id] = ballot.get(m.id, "abstain") + + approvals = sum(1 for v in votes.values() if v == "approve") + rejections = sum(1 for v in votes.values() if v == "reject") + abstentions = sum(1 for v in votes.values() if v == "abstain") + decisive = approvals + rejections + quorum_needed = math.ceil(self.quorum * len(self.members)) + + if decisive < quorum_needed: + outcome: Outcome = "disputed" + rationale = ( + f"no quorum: {decisive} decisive vote(s) < {quorum_needed} needed " + f"({abstentions} abstained)" + ) + disputed = True + elif approvals > rejections: + outcome = "approved" + rationale = f"approved {approvals}-{rejections} ({abstentions} abstained)" + disputed = False + elif rejections > approvals: + outcome = "rejected" + rationale = f"rejected {rejections}-{approvals} ({abstentions} abstained)" + disputed = False + else: + outcome = "disputed" + rationale = f"tie {approvals}-{rejections}; escalate to the dojo to settle" + disputed = True + + return BoardroomDecision( + motion=motion, + members=[m.id for m in self.members], + votes=votes, + approvals=approvals, + rejections=rejections, + abstentions=abstentions, + outcome=outcome, + rationale=rationale, + disputed=disputed, + ) + + +__all__ = [ + "PRESET_BOARDS", + "ROLES", + "Boardroom", + "BoardroomDecision", + "Member", + "Outcome", + "Role", + "Vote", + "board_from_preset", +] diff --git a/mindxtrain/governance/classroom.py b/mindxtrain/governance/classroom.py new file mode 100644 index 0000000000000000000000000000000000000000..210f1633f78bc9554f55adc94c159f60ffd3295d --- /dev/null +++ b/mindxtrain/governance/classroom.py @@ -0,0 +1,129 @@ +"""Classroom — where an actor trains, and the graduation gate. + +The classroom is the training environment (the `trl_local`/`trl_cpu` lanes + the imprint +measurement). An actor **graduates** when its imprint took — i.e. recall moved toward the +persona by at least a threshold. Graduation is the motion the boardroom then convenes on. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +from pydantic import BaseModel, ConfigDict, Field + +if TYPE_CHECKING: + from mindxtrain.eval.imprint import ImprintReport + + +class Graduation(BaseModel): + """The classroom's verdict on whether an actor is ready to leave.""" + + model_config = ConfigDict(extra="forbid", frozen=True) + + run_id: str + graduated: bool + imprint_delta: float + min_delta: float + reasons: list[str] = Field(default_factory=list) + + @property + def motion(self) -> str: + """The promotion motion the boardroom convenes on.""" + return f"promote graduated actor {self.run_id!r} (imprint Δ={self.imprint_delta:+.4f})" + + +def graduate( + report: ImprintReport, + *, + run_id: str = "", + min_delta: float = 0.0, + require_imprinted: bool = True, +) -> Graduation: + """Decide whether an imprinted actor graduates the classroom. + + Criteria: the imprint took (`report.imprinted`, when `require_imprinted`) and the + recall gain meets `min_delta`. Failing reasons are recorded for the boardroom. + """ + reasons: list[str] = [] + if require_imprinted and not report.imprinted: + reasons.append("imprint did not take (no movement toward persona)") + if report.imprint_delta < min_delta: + reasons.append(f"imprint Δ {report.imprint_delta:+.4f} < required {min_delta:+.4f}") + + return Graduation( + run_id=run_id, + graduated=not reasons, + imprint_delta=report.imprint_delta, + min_delta=min_delta, + reasons=reasons, + ) + + +class ClassroomReport(BaseModel): + """Before-vs-after test of a trained actor against the previous model.""" + + model_config = ConfigDict(extra="forbid", frozen=True) + + inquiries: list[str] + before: list[str] + after: list[str] + before_recall: float = Field(description="similarity of before-utterances to the persona voice") + recall: float = Field(description="similarity of after-utterances to the persona voice") + imprint_delta: float + pairwise_after_better: float = Field(description="[0,1]; >0.5 = after closer to persona than before") + persona_maintained: bool + passed: bool + notes: list[str] = Field(default_factory=list) + + +def evaluate_classroom( + inquiries: list[str], + before: list[str], + after: list[str], + baseline: list[str], + *, + use_judge: bool = False, + model: str | None = None, + base_url: str | None = None, +) -> ClassroomReport: + """Run the classroom test: does the trained model recall/maintain the persona? + + Compares the previous (before) and trained (after) utterances against the persona + `baseline` voice using the imprint metric + a semantic-similarity check; optionally + a pairwise LLM judge (before vs after). `passed` iff the imprint took AND the + after-utterances are at least as close to the persona as the before-utterances. + """ + from mindxtrain.eval.imprint import score_imprint + + imprint = score_imprint(inquiries, before, after, baseline) + notes: list[str] = [f"imprint method={imprint.method}"] + + # Pairwise: judge each inquiry (before vs after) toward the persona, else use the + # imprint delta sign as the signal. + if use_judge and model: + from mindxtrain.eval.llama_evals import PairwiseEvaluator + + ev = PairwiseEvaluator(model=model, base_url=base_url) + ref = " ".join(baseline)[:400] + scores = [ + ev.evaluate(q, b, a, reference=ref).score + for q, b, a in zip(inquiries, before, after, strict=False) + ] + pairwise = round(sum(scores) / len(scores), 4) if scores else 0.5 + notes.append(f"pairwise judge={model}") + else: + pairwise = 1.0 if imprint.after_voice > imprint.before_voice else ( + 0.5 if imprint.after_voice == imprint.before_voice else 0.0) + notes.append("pairwise from imprint delta") + + persona_maintained = imprint.imprinted and imprint.after_voice >= imprint.before_voice + passed = persona_maintained and pairwise >= 0.5 + return ClassroomReport( + inquiries=inquiries, before=before, after=after, + before_recall=imprint.before_voice, recall=imprint.after_voice, + imprint_delta=imprint.imprint_delta, pairwise_after_better=pairwise, + persona_maintained=persona_maintained, passed=passed, notes=notes, + ) + + +__all__ = ["ClassroomReport", "Graduation", "evaluate_classroom", "graduate"] diff --git a/mindxtrain/governance/dojo.py b/mindxtrain/governance/dojo.py new file mode 100644 index 0000000000000000000000000000000000000000..22c2b1dabb40d9d2ffef7deebb7f0fcc44d71c11 --- /dev/null +++ b/mindxtrain/governance/dojo.py @@ -0,0 +1,134 @@ +"""Dojo — prime-N dispute settlement for the boardroom. + +When a boardroom is *disputed* (a tie or no quorum), a dojo settles it. A dojo's panel +size is **always a prime** — specifically an odd prime (>= 3), because an odd number of +decisive judges cannot tie, so the dispute always resolves. The dojo votes approve/reject +on the contested motion; the majority verdict is final. +""" + +from __future__ import annotations + +from collections.abc import Callable +from typing import Literal + +from pydantic import BaseModel, ConfigDict, field_validator + +from mindxtrain.governance.boardroom import BoardroomDecision, Member +from mindxtrain.governance.primes import is_prime, nearest_prime, next_prime + +JudgeVote = Literal["approve", "reject"] + + +def prime_dojo_size(requested: int) -> int: + """Round `requested` to a dispute-settling dojo size: the nearest odd prime >= 3. + + 2 is prime but even (can tie), so it is excluded — the smallest dojo is 3. + """ + base = max(3, requested) + n = nearest_prime(base) + while n < 3 or not is_prime(n): + n = next_prime(n) + return n + + +class DojoVerdict(BaseModel): + """The dojo's final ruling on a disputed motion.""" + + model_config = ConfigDict(extra="forbid", frozen=True) + + motion: str + judges: list[str] + votes: dict[str, JudgeVote] + approvals: int + rejections: int + winner: JudgeVote + settled: bool = True + + +class Dojo(BaseModel): + """A prime panel of judges that settles a boardroom dispute.""" + + model_config = ConfigDict(extra="forbid", frozen=True) + + judges: list[Member] + + @field_validator("judges") + @classmethod + def _panel_is_odd_prime(cls, v: list[Member]) -> list[Member]: + n = len(v) + if not is_prime(n) or n < 3: + msg = ( + f"a dojo panel must be an odd prime (>=3) so disputes never tie; " + f"got {n}. Use Dojo.sized({n}) to round to the nearest valid size." + ) + raise ValueError(msg) + return v + + @classmethod + def sized(cls, requested: int, *, model: str = "", role: str = "expert") -> Dojo: + """Build a dojo whose panel is the nearest odd prime >= 3 to `requested`.""" + n = prime_dojo_size(requested) + judges = [Member(id=f"judge-{i}", role="expert", model=model) for i in range(n)] + _ = role # judges are experts by construction; kept for call symmetry + return cls(judges=judges) + + def settle( + self, + motion: str, + ballot: dict[str, JudgeVote] | Callable[[Member, str], JudgeVote], + ) -> DojoVerdict: + """Settle a motion by majority of the prime judge panel. + + Every judge must cast approve/reject (no abstention in a dojo). With an odd + prime panel the majority is strict — the verdict always settles. + """ + votes: dict[str, JudgeVote] = {} + for j in self.judges: + if callable(ballot): + votes[j.id] = ballot(j, motion) + else: + if j.id not in ballot: + msg = f"dojo judge {j.id!r} did not vote; every judge must rule" + raise ValueError(msg) + votes[j.id] = ballot[j.id] + + approvals = sum(1 for v in votes.values() if v == "approve") + rejections = sum(1 for v in votes.values() if v == "reject") + winner: JudgeVote = "approve" if approvals > rejections else "reject" + return DojoVerdict( + motion=motion, + judges=[j.id for j in self.judges], + votes=votes, + approvals=approvals, + rejections=rejections, + winner=winner, + settled=True, + ) + + +def settle_dispute( + decision: BoardroomDecision, + dojo: Dojo, + ballot: dict[str, JudgeVote] | Callable[[Member, str], JudgeVote], +) -> DojoVerdict: + """Settle a disputed boardroom decision with a prime dojo. + + Raises if the decision was not actually disputed (the dojo only settles ties / + no-quorum outcomes — a clean approve/reject stands on its own). + """ + if not decision.disputed: + msg = ( + f"boardroom decision on {decision.motion!r} was {decision.outcome!r}, " + "not disputed — nothing for the dojo to settle" + ) + raise ValueError(msg) + return dojo.settle(decision.motion, ballot) + + +__all__ = [ + "Dojo", + "DojoVerdict", + "JudgeVote", + "prime_dojo_size", + "settle_dispute", +] diff --git a/mindxtrain/governance/panel.py b/mindxtrain/governance/panel.py new file mode 100644 index 0000000000000000000000000000000000000000..5bec4d65a6cfa837706390d1a47cee87a830cf24 --- /dev/null +++ b/mindxtrain/governance/panel.py @@ -0,0 +1,193 @@ +"""Model-backed deliberation — back boardroom members + dojo judges with real LLMs. + +`Boardroom.convene` and `Dojo.settle` take a ballot callable. This module provides ballots +that query a real model (any OpenAI-compatible backend — the same ollama / vLLM the operator +serves) so a boardroom literally deliberates and a dojo literally judges. Each member is +prompted from its role's stance and must end with `VERDICT: APPROVE|REJECT|ABSTAIN`. + +Lazy + best-effort: a model that errors or returns no parseable verdict abstains (boardroom) +or is recorded as a reject (dojo, which forbids abstention). Pure stdlib + httpx (a base dep) ++ pydantic, so the module imports without `--extra ml`. +""" + +from __future__ import annotations + +import os +import re +from collections.abc import Callable + +import httpx +from pydantic import BaseModel, ConfigDict + +from mindxtrain.governance.boardroom import Member, Role, Vote +from mindxtrain.governance.dojo import JudgeVote + +# Role → the stance the member argues from when deliberating. +ROLE_STANCE: dict[Role, str] = { + "advocate": "Argue in favour of the motion; make the strongest case for approval.", + "critic": "Argue against the motion; surface flaws, risks, and reasons to reject.", + "analyst": "Weigh the evidence neutrally and reason to a balanced judgement.", + "devils_advocate": "Challenge the prevailing view; stress-test the motion adversarially.", + "expert": "Judge on technical merit and correctness.", + "generalist": "Use plain common sense and practical judgement.", +} + +_DEFAULT_MODEL = "llama3.2" +_VERDICT_RE = re.compile(r"verdict\s*[:\-]?\s*(approve|reject|abstain)", re.IGNORECASE) + + +def resolve_chat_base_url(base_url: str | None = None) -> str: + """Resolve the OpenAI-compatible chat base URL the operator/backends use.""" + if base_url: + return base_url.rstrip("/") + for env in ("MINDXTRAIN_OPENAI_BASE_URL", "MINDXTRAIN_VLLM_BASE_URL", "MINDXTRAIN_OLLAMA_BASE_URL"): + val = os.environ.get(env) + if val: + return val.rstrip("/") + return "http://localhost:11434/v1" + + +def chat_once( + model: str, + messages: list[dict[str, str]], + *, + base_url: str | None = None, + api_key: str | None = None, + temperature: float = 0.0, + max_tokens: int = 256, + timeout_s: float = 60.0, +) -> str: + """One non-streaming OpenAI-compatible chat completion; returns the content.""" + base = resolve_chat_base_url(base_url) + key = api_key or os.environ.get("MINDXTRAIN_OPENAI_API_KEY", "") + headers = {"Content-Type": "application/json"} + if key: + headers["Authorization"] = f"Bearer {key}" + with httpx.Client(timeout=timeout_s) as client: + resp = client.post( + f"{base}/chat/completions", + json={ + "model": model, + "messages": messages, + "temperature": temperature, + "max_tokens": max_tokens, + "stream": False, + }, + headers=headers, + ) + resp.raise_for_status() + data = resp.json() + choice = (data.get("choices") or [{}])[0] + return (choice.get("message") or {}).get("content", "") or "" + + +def parse_vote(text: str, *, allow_abstain: bool = True) -> Vote: + """Parse a vote from model output: explicit `VERDICT:` line, else keyword scan.""" + m = _VERDICT_RE.search(text) + if m: + v = m.group(1).lower() + else: + low = text.lower() + if "approve" in low and "reject" not in low: + v = "approve" + elif "reject" in low and "approve" not in low: + v = "reject" + else: + v = "abstain" + if v == "abstain" and not allow_abstain: + return "reject" # a dojo judge must decide; default the unclear to reject + return v # type: ignore[return-value] + + +class Deliberation(BaseModel): + """One member's model-backed deliberation on a motion.""" + + model_config = ConfigDict(extra="forbid", frozen=True) + + member_id: str + role: Role + vote: Vote + rationale: str + error: str = "" + + +def deliberate( + member: Member, + motion: str, + *, + base_url: str | None = None, + allow_abstain: bool = True, + default_model: str = _DEFAULT_MODEL, + **chat_kw: object, +) -> Deliberation: + """Query `member`'s model from its role stance and parse a vote + rationale.""" + stance = ROLE_STANCE.get(member.role, ROLE_STANCE["generalist"]) + verdicts = "APPROVE, REJECT, or ABSTAIN" if allow_abstain else "APPROVE or REJECT" + system = ( + f"You are the {member.role.replace('_', ' ')} on a review board deliberating a " + f"motion. {stance} Give a one-sentence rationale, then on the final line write " + f"exactly 'VERDICT: <{verdicts}>'." + ) + model = member.model or default_model + # Reasoning ("thinking") models spend tokens before the verdict — give enough + # room and time by default so the `VERDICT:` line is actually reached (concurrent + # members also queue inside a single-GPU backend). Callers can override. + chat_kw.setdefault("max_tokens", 512) + chat_kw.setdefault("timeout_s", 180.0) + try: + text = chat_once( + model, + [{"role": "system", "content": system}, {"role": "user", "content": motion}], + base_url=base_url, + **chat_kw, # type: ignore[arg-type] + ) + except (httpx.HTTPError, OSError, ValueError) as exc: + return Deliberation( + member_id=member.id, role=member.role, + vote="reject" if not allow_abstain else "abstain", + rationale="", error=str(exc), + ) + vote = parse_vote(text, allow_abstain=allow_abstain) + rationale = next((ln.strip() for ln in text.splitlines() if ln.strip()), text.strip())[:280] + return Deliberation(member_id=member.id, role=member.role, vote=vote, rationale=rationale) + + +def model_ballot( + *, base_url: str | None = None, default_model: str = _DEFAULT_MODEL, **chat_kw: object, +) -> Callable[[Member, str], Vote]: + """A boardroom ballot backed by real models (members may abstain).""" + + def _ballot(member: Member, motion: str) -> Vote: + return deliberate( + member, motion, base_url=base_url, allow_abstain=True, + default_model=default_model, **chat_kw, + ).vote + + return _ballot + + +def model_judge_ballot( + *, base_url: str | None = None, default_model: str = _DEFAULT_MODEL, **chat_kw: object, +) -> Callable[[Member, str], JudgeVote]: + """A dojo ballot backed by real models (judges must approve/reject).""" + + def _ballot(judge: Member, motion: str) -> JudgeVote: + v = deliberate( + judge, motion, base_url=base_url, allow_abstain=False, + default_model=default_model, **chat_kw, + ).vote + return "approve" if v == "approve" else "reject" + + return _ballot + + +__all__ = [ + "ROLE_STANCE", + "Deliberation", + "chat_once", + "deliberate", + "model_ballot", + "model_judge_ballot", + "parse_vote", + "resolve_chat_base_url", +] diff --git a/mindxtrain/governance/primes.py b/mindxtrain/governance/primes.py new file mode 100644 index 0000000000000000000000000000000000000000..6ebefc182ebb89700102ee0a179b4d28f212765f --- /dev/null +++ b/mindxtrain/governance/primes.py @@ -0,0 +1,59 @@ +"""Prime helpers — the dojo's panel size is always prime. + +A dojo settles a dispute by majority of its judges. A *prime* panel size (which for +p > 2 is odd) guarantees no tie, so a dispute always resolves. These helpers size and +validate a dojo panel. +""" + +from __future__ import annotations + + +def is_prime(n: int) -> bool: + """True if `n` is a prime number (n < 2 is not prime).""" + if n < 2: + return False + if n < 4: + return True # 2, 3 + if n % 2 == 0: + return False + i = 3 + while i * i <= n: + if n % i == 0: + return False + i += 2 + return True + + +def next_prime(n: int) -> int: + """Smallest prime strictly greater than `n`.""" + candidate = max(n + 1, 2) + while not is_prime(candidate): + candidate += 1 + return candidate + + +def prev_prime(n: int) -> int | None: + """Largest prime strictly less than `n`, or None if none exists.""" + candidate = n - 1 + while candidate >= 2: + if is_prime(candidate): + return candidate + candidate -= 1 + return None + + +def nearest_prime(n: int) -> int: + """The prime nearest to `n` (rounds up on a tie). Used to size a dojo. + + `nearest_prime(4) == 5` (3 and 5 are equidistant; round up). Never returns < 2. + """ + if n < 2: + return 2 + if is_prime(n): + return n + lo = prev_prime(n) + hi = next_prime(n) + if lo is None: + return hi + # Equidistant → prefer the larger prime (a bigger panel is more robust). + return hi if (hi - n) <= (n - lo) else lo diff --git a/mindxtrain/governance/proof_loop.py b/mindxtrain/governance/proof_loop.py new file mode 100644 index 0000000000000000000000000000000000000000..53de4c2857475866815adba378da8b0b9c47cfce --- /dev/null +++ b/mindxtrain/governance/proof_loop.py @@ -0,0 +1,175 @@ +"""dcoach proof loop — prove a CPU-trained model recalls its training. + +Chains the whole thing end-to-end: compose a persona/skills script → derive training +params (nudged by past feedback) → imprint-train a tiny actor on CPU → probe before +(base) vs after (adapter) recall → the **classroom** tests recall/persona → the +**boardroom** decides success/failure → record **autotune feedback** that improves the +next run. This is mindXtrain's first-run proof + the autotune feedback loop. + +Heavy (real training + generation) — runs on the `trl_local` CPU lane. `on_event(phase, +msg)` reports progress so a UI can stream it. +""" + +from __future__ import annotations + +from collections.abc import Callable +from pathlib import Path +from typing import Any + +import yaml +from pydantic import BaseModel, ConfigDict + +from mindxtrain.governance.classroom import ClassroomReport + +_DEFAULT_BASE_MODEL = "HuggingFaceTB/SmolLM2-135M" +_IMPRINT_RECIPE = "mindx_persona_imprint_local" + + +class ProofResult(BaseModel): + """The outcome of one proof-loop run.""" + + model_config = ConfigDict(extra="forbid", frozen=True) + + run_id: str + dataset_path: str + rows: int + train_params: dict[str, int] + classroom: ClassroomReport + boardroom_outcome: str + boardroom_rationale: str + passed: bool + next_params: dict[str, int] + + +def _build_cfg(base_model: str, script_path: Path, params: dict[str, int], run_id: str) -> Any: + """Render the imprint recipe, override base/data/params, and load an XTrainConfig.""" + from mindxtrain.config.loader import load_config, render_recipe + + raw = yaml.safe_load(render_recipe(_IMPRINT_RECIPE)) + raw["meta"]["run_name"] = run_id + raw["model"]["name"] = base_model + raw["data"]["path"] = str(script_path) + raw["data"]["max_samples"] = 64 + raw["data"]["seq_len"] = 128 + raw["train"]["schedule"]["epochs"] = int(params.get("epochs", 12)) + raw["train"]["batch"]["grad_accum"] = int(params.get("grad_accum", 1)) + raw["train"]["batch"]["per_device"] = int(params.get("per_device", 1)) + import tempfile + + with tempfile.NamedTemporaryFile("w", suffix=".yaml", delete=False) as fh: + fh.write(yaml.safe_dump(raw)) + tmp = fh.name + return load_config(tmp) + + +def run_proof_loop( + *, + run_id: str, + persona: str = "codephreak", + skills: list[str] | None = None, + exchanges: list[Any] | None = None, + base_model: str = _DEFAULT_BASE_MODEL, + out_dir: str | Path, + inquiries: list[str] | None = None, + board_preset: str = "classic_triad", + board_model: str | None = None, + force_cpu: bool = True, + max_new_tokens: int = 32, + on_event: Callable[[str, str], None] | None = None, + feedback_path: Path | None = None, +) -> ProofResult: + """Run the full proof loop and return a structured `ProofResult`.""" + emit = on_event or (lambda _phase, _msg: None) + out = Path(out_dir) + + # 1) Compose persona + skills → script. + from mindxtrain.data import personas as _pz + from mindxtrain.data.scripts import ( + build_script_rows, + derive_training_params, + write_script_jsonl, + ) + + base_persona, skill_exchanges = _pz.compose(persona, skills or []) + all_exchanges = list(exchanges or []) + skill_exchanges + rows_list = build_script_rows(base_persona, all_exchanges, seed_voice=True) + script_path = write_script_jsonl(rows_list, out / "script.jsonl") + rows = len(rows_list) + emit("dataset", f"authored {rows} rows for persona '{base_persona.name}'") + + # 2) Derive params, nudged by past feedback. + from mindxtrain.autotune import feedback as _fb + + params = _fb.suggest_from_history(derive_training_params(rows), path=feedback_path) + emit("params", f"epochs={params['epochs']} grad_accum={params['grad_accum']}") + + # 3) Imprint-train the tiny actor. + from mindxtrain.autotune.benchmark import run_autotune + from mindxtrain.train.backend_trl_cpu import run_trl_local + + cfg = _build_cfg(base_model, script_path, params, run_id) + run_dir = out / "run" + run_dir.mkdir(parents=True, exist_ok=True) + emit("train", "imprinting the persona…") + run_trl_local(cfg, run_autotune(dry_run=True), run_dir, force_cpu=force_cpu) + adapter = run_dir / "checkpoint" + + # 4) Probe before (base) vs after (adapter) recall. + from mindxtrain.data.scripts import persona_system_prompt + from mindxtrain.eval.imprint import default_inquiries, probe_recall + + inq = inquiries or [e.user for e in all_exchanges][:4] or default_inquiries(base_persona.name) + baseline = [e.assistant for e in all_exchanges] or list(base_persona.voice_examples) + system = persona_system_prompt(base_persona) # match the conditioning the adapter trained under + emit("probe", "recall before training…") + before = probe_recall( + base_model, inq, system=system, force_cpu=force_cpu, max_new_tokens=max_new_tokens, + ) + emit("probe", "recall after training…") + after = probe_recall( + base_model, inq, adapter_dir=adapter, system=system, + force_cpu=force_cpu, max_new_tokens=max_new_tokens, + ) + + # 5) Classroom test. + from mindxtrain.governance.classroom import evaluate_classroom, graduate + + classroom = evaluate_classroom(inq, before, after, baseline) + emit("classroom", f"passed={classroom.passed} recall {classroom.before_recall}→{classroom.recall}") + + # 6) Boardroom decision. + from mindxtrain.eval.imprint import score_imprint + from mindxtrain.governance import Boardroom + from mindxtrain.governance.boardroom import board_from_preset + + grad = graduate(score_imprint(inq, before, after, baseline), run_id=run_id) + board = Boardroom(members=board_from_preset(board_preset, model=board_model or "")) + if board_model: + from mindxtrain.governance import panel as _panel + + ballot: Any = _panel.model_ballot(default_model=board_model) + else: + vote = "approve" if classroom.passed else "reject" + ballot = {m.id: vote for m in board.members} + decision = board.convene(grad.motion, ballot) + emit("boardroom", f"{decision.outcome}: {decision.rationale}") + + # 7) Record feedback + suggest the next run's params. + outcome = decision.outcome + _fb.record( + run_id=run_id, params=params, classroom_score=classroom.imprint_delta, + passed=classroom.passed, boardroom_outcome=outcome, path=feedback_path, # type: ignore[arg-type] + ) + next_params = _fb.suggest_next_params( + params, passed=classroom.passed, classroom_score=classroom.imprint_delta, + ) + emit("feedback", f"next: epochs={next_params['epochs']} grad_accum={next_params['grad_accum']}") + + return ProofResult( + run_id=run_id, dataset_path=str(script_path), rows=rows, train_params=params, + classroom=classroom, boardroom_outcome=outcome, boardroom_rationale=decision.rationale, + passed=classroom.passed and decision.outcome == "approved", next_params=next_params, + ) + + +__all__ = ["ProofResult", "run_proof_loop"] diff --git a/mindxtrain/hf/__init__.py b/mindxtrain/hf/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e8ff1cef5c84a0cdbc8c16e8bdf0b3cfb77db251 --- /dev/null +++ b/mindxtrain/hf/__init__.py @@ -0,0 +1,27 @@ +"""mindxtrain.hf — the Hugging Face extension. + +The framework already knew how to *push a folder* (`mindxtrain.storage.hf_hub`). This package is +the rest of the Hub as mindXtrain needs it, and nothing more: + +- `account` — who the token is, which namespaces it can actually write (membership is not write scope) +- `publish_generation` — a trained run as a model repo: merged weights + adapter + train.log + a + Modelfile carrying the persona, and a card written from the run's own numbers +- `pull_base` / `warm` — fetch a base before training so a run does not fail three hours in +- `lineage` — what is on the Hub for a project, reconciled against local runs +- `spaces` — push a Gradio UI as a Space (the free ZeroGPU slot rules are stated, not guessed) +- `datasets` — push/pull a training corpus with its manifest + +Every function returns a plain dict: `{"ok": bool, …}`. Nothing here raises at import time, and +`huggingface_hub` is imported lazily so a CPU-only install without `--extra chain` still loads. +""" +from .extension import ( # noqa: F401 + account, + lineage, + publish_generation, + pull_base, + push_dataset, + push_space, + warm, +) + +__all__ = ["account", "lineage", "publish_generation", "pull_base", "push_dataset", "push_space", "warm"] diff --git a/mindxtrain/hf/extension.py b/mindxtrain/hf/extension.py new file mode 100644 index 0000000000000000000000000000000000000000..14141cc4442ddcaf0ec2db6413f45ba4df942cf8 --- /dev/null +++ b/mindxtrain/hf/extension.py @@ -0,0 +1,300 @@ +"""The Hugging Face extension — the Hub as mindXtrain needs it. + +Design rules, learned the hard way on a live node (2026-09): + +- **Membership is not write scope.** `whoami` lists orgs you belong to; only the token's own + permissions say where it can write. `account()` reports both, separately. +- **`list_repo_tree` yields RepoFile AND RepoFolder**, both carrying `.path`; only + `repo_info().siblings` carry `.rfilename`. Reading the wrong one makes a scan report "nothing + on the Hub" while the repo is full. +- **The Hub checks a Space's ZeroGPU quota before it checks existence**: `create_repo(exist_ok=True, + space_hardware=…)` on an EXISTING Space answers 402 once the account's slots are used. Check + existence first. +- **A Space README's `short_description` must be ≤ 60 characters** or the upload is refused. +- **Never park a write-scoped token on a public Space.** Public Spaces read public repos. +""" +from __future__ import annotations + +import json +import os +import re +import time +from pathlib import Path +from typing import Any, Dict, List, Optional + +_HF_ENV = ("HF_TOKEN", "HUGGING_FACE_HUB_TOKEN", "HUGGINGFACEHUB_API_TOKEN") + + +def _token(explicit: Optional[str] = None) -> Optional[str]: + if explicit: + return explicit + for k in _HF_ENV: + v = os.environ.get(k) + if v: + return v + return None + + +def _api(token: Optional[str] = None): + """(HfApi, None) or (None, {"ok": False, …}) — never raises.""" + try: + from huggingface_hub import HfApi + except ImportError: + return None, {"ok": False, "reason": "huggingface_hub not installed — `uv sync --extra chain`"} + tok = _token(token) + if not tok: + return None, {"ok": False, "reason": f"no token — set one of {', '.join(_HF_ENV)}"} + return HfApi(token=tok), None + + +def tree_paths(api, repo_id: str, **kw) -> List[str]: + """File paths of a repo tree (folders skipped). See the module docstring: the tree's entries + carry `.path`, not `.rfilename`.""" + return [f.path for f in api.list_repo_tree(repo_id, **kw) if type(f).__name__ != "RepoFolder"] + + +# ── who the token is ────────────────────────────────────────────────────────── +def account(token: Optional[str] = None) -> Dict[str, Any]: + """The identity behind the token, its role, the orgs it belongs to, and — separately — the + namespaces it can actually write.""" + api, err = _api(token) + if err: + return err + try: + me = api.whoami() + except Exception as e: # noqa: BLE001 + return {"ok": False, "reason": f"whoami failed: {type(e).__name__}: {str(e)[:200]}"} + auth = (me.get("auth") or {}).get("accessToken") or {} + perms = auth.get("fineGrained") or {} + writable, creatable = [], [] + for scope in (perms.get("scoped") or []): + ent = (scope.get("entity") or {}) + name = ent.get("name") or ent.get("type") + perms_list = scope.get("permissions") or [] + if any(p.startswith("repo.write") or p == "repo.content.write" for p in perms_list): + writable.append(name) + if "repo.write" in perms_list: + creatable.append(name) + return {"ok": True, "user": me.get("name"), "type": me.get("type"), "is_pro": bool(me.get("isPro")), + "role": auth.get("role"), "orgs": [o.get("name") for o in (me.get("orgs") or [])], + "writable_namespaces": writable or None, "creatable_namespaces": creatable or None, + "note": "membership is not write scope — a token can belong to an org and still be unable to write it"} + + +# ── bases, fetched before the run rather than during it ─────────────────────── +def pull_base(model_id: str, *, token: Optional[str] = None, allow_patterns: Optional[List[str]] = None) -> Dict[str, Any]: + """Download a base model to the local cache. Do this BEFORE training: a run that discovers a + missing base three hours in has wasted three hours.""" + try: + from huggingface_hub import snapshot_download + except ImportError: + return {"ok": False, "reason": "huggingface_hub not installed — `uv sync --extra chain`"} + t0 = time.time() + try: + path = snapshot_download(model_id, token=_token(token), allow_patterns=allow_patterns) + except Exception as e: # noqa: BLE001 + return {"ok": False, "model": model_id, "reason": f"{type(e).__name__}: {str(e)[:200]}"} + p = Path(path) + return {"ok": True, "model": model_id, "path": str(p), "seconds": round(time.time() - t0, 1), + "bytes": sum(f.stat().st_size for f in p.rglob("*") if f.is_file())} + + +def warm(config: Path | str, *, token: Optional[str] = None) -> Dict[str, Any]: + """Pull whatever a run.yaml says it needs (the base model) so `train` starts cold-free.""" + try: + import yaml + cfg = yaml.safe_load(Path(config).read_text()) + except Exception as e: # noqa: BLE001 + return {"ok": False, "reason": f"unreadable config: {type(e).__name__}: {str(e)[:160]}"} + base = ((cfg or {}).get("model") or {}).get("name") + if not base: + return {"ok": False, "reason": "config has no model.name"} + return dict(pull_base(base, token=token), config=str(config)) + + +# ── a finished run, published ───────────────────────────────────────────────── +def _card(repo_id: str, meta: Dict[str, Any]) -> str: + """A model card written from the run's own numbers. No claim that is not in `meta`.""" + m = meta + fm = {"license": m.get("license", "apache-2.0"), "library_name": "transformers", + "pipeline_tag": "text-generation", "tags": ["mindxtrain", "lora"] + list(m.get("tags") or [])} + if m.get("base"): + fm["base_model"] = m["base"] + if m.get("dataset"): + fm["datasets"] = [m["dataset"]] + head = "---\n" + "\n".join( + f"{k}: {json.dumps(v) if isinstance(v, (list, dict)) else v}" for k, v in fm.items()) + "\n---\n\n" + rows = [("base", m.get("base")), ("method", m.get("method")), ("corpus", m.get("dataset")), + ("hardware", m.get("hardware")), ("wall", m.get("wall")), ("train loss", m.get("train_loss")), + ("eval loss", m.get("eval_loss")), ("gate", m.get("gate")), ("framework", "mindXtrain " + str(m.get("framework_version") or ""))] + table = "\n".join(f"| {k} | {v} |" for k, v in rows if v not in (None, "")) + return (head + f"# {repo_id.split('/')[-1]}\n\n{m.get('summary', 'A mindXtrain run, published with its evidence.')}\n\n" + f"| | |\n|---|---|\n{table}\n\n" + "## Use\n\n```python\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n" + f'tok = AutoTokenizer.from_pretrained("{repo_id}")\nm = AutoModelForCausalLM.from_pretrained("{repo_id}")\n```\n\n' + + (f"## The gate\n\n{m['gate_note']}\n\n" if m.get("gate_note") else "") + + "## Provenance\n\nTrained by [mindXtrain](https://github.com/professor-codephreak/mindXtrain); " + "the training log ships beside the weights.\n") + + +def publish_generation(run_dir: Path | str, repo_id: str, *, token: Optional[str] = None, private: bool = False, + meta: Optional[Dict[str, Any]] = None, persona_system: Optional[str] = None, + include_merged: bool = True, dry_run: bool = False) -> Dict[str, Any]: + """A finished run as a model repo: merged weights at the root (if present), the LoRA delta under + `adapter/`, `train.log`, a `Modelfile` for Ollama, and a card built from `meta`. + + `dry_run=True` reports exactly what would be uploaded and touches nothing.""" + run = Path(run_dir) + if not run.is_dir(): + return {"ok": False, "reason": f"no run dir at {run}"} + ck = run / "checkpoint" + merged = run / "ollama_push" / "merged" + staged: Dict[str, Path] = {} + if include_merged and merged.is_dir(): + for f in merged.iterdir(): + if f.is_file(): + staged[f.name] = f + if ck.is_dir(): + for f in ck.iterdir(): + if f.is_file() and f.name != "training_args.bin" or f.name == "training_args.bin": + staged[f"adapter/{f.name}"] = f + log = run / "train.log" + if log.is_file(): + staged["train.log"] = log + if not staged: + return {"ok": False, "reason": f"{run} holds neither a checkpoint nor merged weights"} + meta = dict(meta or {}) + if not meta.get("base"): + try: + meta["base"] = json.loads((ck / "adapter_config.json").read_text()).get("base_model_name_or_path") + except Exception: # noqa: BLE001 + pass + card = _card(repo_id, meta) + modelfile = ("# ollama create <name> -f Modelfile (from this repo's directory)\nFROM .\n" + + (f'SYSTEM """{persona_system}"""\n' if persona_system else "") + + 'PARAMETER temperature 0.7\nPARAMETER repeat_penalty 1.3\nPARAMETER stop "<|im_end|>"\n') + plan = {"repo": repo_id, "private": private, "files": sorted(staged) + ["README.md", "Modelfile"], + "bytes": sum(f.stat().st_size for f in staged.values())} + if dry_run: + return {"ok": True, "dry_run": True, "would_upload": plan, "card_preview": card[:400]} + api, err = _api(token) + if err: + return err + import tempfile + import shutil + with tempfile.TemporaryDirectory() as tmp: + stage = Path(tmp) + for rel, src in staged.items(): + dst = stage / rel + dst.parent.mkdir(parents=True, exist_ok=True) + shutil.copy2(src, dst) + (stage / "README.md").write_text(card) + (stage / "Modelfile").write_text(modelfile) + try: + api.create_repo(repo_id, repo_type="model", private=private, exist_ok=True) + ci = api.upload_folder(folder_path=str(stage), repo_id=repo_id, repo_type="model", + commit_message=meta.get("commit_message") or "mindXtrain: a run, published with its evidence") + except Exception as e: # noqa: BLE001 + return {"ok": False, "repo": repo_id, "reason": f"{type(e).__name__}: {str(e)[:300]}"} + return {"ok": True, "repo": repo_id, "url": f"https://huggingface.co/{repo_id}", + "commit": str(getattr(ci, "oid", ci))[:12], "uploaded": plan} + + +# ── the corpus ──────────────────────────────────────────────────────────────── +def push_dataset(path: Path | str, repo_id: str, *, token: Optional[str] = None, private: bool = False, + path_in_repo: str = "", manifest: Optional[Dict[str, Any]] = None) -> Dict[str, Any]: + """Push a training corpus (a folder or one JSONL) with an optional manifest beside it.""" + api, err = _api(token) + if err: + return err + src = Path(path) + if not src.exists(): + return {"ok": False, "reason": f"no corpus at {src}"} + try: + api.create_repo(repo_id, repo_type="dataset", private=private, exist_ok=True) + if src.is_dir(): + ci = api.upload_folder(folder_path=str(src), repo_id=repo_id, repo_type="dataset", + path_in_repo=path_in_repo or None, commit_message="mindXtrain: corpus") + else: + ci = api.upload_file(path_or_fileobj=str(src), path_in_repo=f"{path_in_repo}/{src.name}".lstrip("/"), + repo_id=repo_id, repo_type="dataset", commit_message="mindXtrain: corpus") + if manifest: + api.upload_file(path_or_fileobj=json.dumps(manifest, indent=1).encode(), + path_in_repo=f"{path_in_repo}/MANIFEST.json".lstrip("/"), + repo_id=repo_id, repo_type="dataset", commit_message="mindXtrain: corpus manifest") + except Exception as e: # noqa: BLE001 + return {"ok": False, "repo": repo_id, "reason": f"{type(e).__name__}: {str(e)[:300]}"} + return {"ok": True, "repo": repo_id, "url": f"https://huggingface.co/datasets/{repo_id}", + "commit": str(getattr(ci, "oid", ci))[:12]} + + +# ── what is actually on the Hub ─────────────────────────────────────────────── +_GEN = re.compile(r"(?:^|/)gen(\d+)(?:/|$)") + + +def lineage(repo_id: str, *, repo_type: str = "model", token: Optional[str] = None, + local_runs: Optional[Path | str] = None) -> Dict[str, Any]: + """Generations present in a repo, and — when `local_runs` is given — which local runs are not + published yet. Uses `tree_paths`, so folders never break the scan.""" + api, err = _api(token) + if err: + return err + try: + paths = tree_paths(api, repo_id, repo_type=repo_type, recursive=True) + except Exception as e: # noqa: BLE001 + return {"ok": False, "repo": repo_id, "reason": f"{type(e).__name__}: {str(e)[:200]}"} + gens: Dict[int, List[str]] = {} + for p in paths: + m = _GEN.search(p) + if m: + gens.setdefault(int(m.group(1)), []).append(p) + out = {"ok": True, "repo": repo_id, "repo_type": repo_type, "files": len(paths), + "generations": {str(g): {"files": len(v), + "adapter": any(x.endswith("adapter_model.safetensors") for x in v), + "merged": any(x.endswith("model.safetensors") and "adapter" not in x for x in v)} + for g, v in sorted(gens.items())}} + if local_runs: + root = Path(local_runs) + local = sorted(int(m.group(1)) for d in root.glob("gen*") if d.is_dir() for m in [_GEN.search(d.name + "/")] if m) + out["local"] = local + out["unpublished"] = [g for g in local if g not in gens] + return out + + +# ── Spaces ──────────────────────────────────────────────────────────────────── +def push_space(folder: Path | str, space_id: str, *, token: Optional[str] = None, private: bool = True, + hardware: Optional[str] = "zero-a10g", variables: Optional[Dict[str, str]] = None, + secrets: Optional[Dict[str, str]] = None) -> Dict[str, Any]: + """Push a Gradio folder as a Space. Existence is checked BEFORE creation (the Hub tests the + ZeroGPU quota first and answers 402 on a repo that already exists), and a README + `short_description` longer than 60 characters is refused by the Hub, so it is checked here.""" + api, err = _api(token) + if err: + return err + src = Path(folder) + if not (src / "app.py").is_file(): + return {"ok": False, "reason": f"{src} has no app.py"} + readme = src / "README.md" + if readme.is_file(): + m = re.search(r"^short_description:\s*(.+)$", readme.read_text(), re.M) + if m and len(m.group(1).strip()) > 60: + return {"ok": False, "reason": f"short_description is {len(m.group(1).strip())} chars; the Hub allows 60"} + try: + exists = api.repo_exists(space_id, repo_type="space") + if not exists: + api.create_repo(space_id, repo_type="space", space_sdk="gradio", private=private, + **({"space_hardware": hardware} if hardware else {})) + ci = api.upload_folder(folder_path=str(src), repo_id=space_id, repo_type="space", + ignore_patterns=["__pycache__/*", "**/__pycache__/*", "*.pyc"], + commit_message="mindXtrain UI") + for k, v in (variables or {}).items(): + api.add_space_variable(space_id, k, v) + for k, v in (secrets or {}).items(): + api.add_space_secret(space_id, k, v) + rt = api.get_space_runtime(space_id) + except Exception as e: # noqa: BLE001 + return {"ok": False, "space": space_id, "reason": f"{type(e).__name__}: {str(e)[:300]}"} + return {"ok": True, "space": space_id, "existed": exists, "stage": str(rt.stage), + "url": f"https://huggingface.co/spaces/{space_id}", + "host": "https://" + space_id.replace("/", "-").replace("_", "-").lower() + ".hf.space", + "note": "free personal accounts host 2 ZeroGPU Spaces; a free org hosts none (402)"} diff --git a/mindxtrain/models/__init__.py b/mindxtrain/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/mindxtrain/models/chat_template.py b/mindxtrain/models/chat_template.py new file mode 100644 index 0000000000000000000000000000000000000000..11e27b08de836e7f06c2c6194a0bdfb997a9f255 --- /dev/null +++ b/mindxtrain/models/chat_template.py @@ -0,0 +1,126 @@ +"""Chat templates — Hermes / Qwen3-Coder / Qwen3 reasoning parsers. + +Pure-Python rendering and response parsing. Used by: + 1. `mindxtrain serve` to set the right `--chat-template` on vLLM-ROCm. + 2. `mindxtrain.operator.app` to format ChatRequest messages before forwarding. + +Single canonical home per mindxtrain2.md §Part 4 `models.chat_template`. Merges +the previous `xtrain.serve.parsers` and `automindx.templates.registry` modules. +""" + +from __future__ import annotations + +import re +from collections.abc import Iterable +from dataclasses import dataclass +from typing import Literal, Protocol + +Role = Literal["system", "user", "assistant", "tool"] + + +@dataclass(frozen=True) +class ChatMessage: + role: Role + content: str + + +class ChatTemplate(Protocol): + """Callable interface: list[ChatMessage] -> rendered prompt str.""" + + name: str + + def render(self, messages: Iterable[ChatMessage], add_generation_prompt: bool = True) -> str: ... + def parse_response(self, response: str) -> dict[str, str]: ... + + +# ---- Hermes (ChatML) ------------------------------------------------------- + + +class HermesTemplate: + """ChatML-flavored format used by Hermes-3 / Qwen / many open models.""" + + name: str = "hermes" + + def render(self, messages: Iterable[ChatMessage], add_generation_prompt: bool = True) -> str: + parts: list[str] = [] + for m in messages: + parts.append(f"<|im_start|>{m.role}\n{m.content}<|im_end|>") + rendered = "\n".join(parts) + if add_generation_prompt: + rendered += "\n<|im_start|>assistant\n" + return rendered + + def parse_response(self, response: str) -> dict[str, str]: + cleaned = response.split("<|im_end|>", 1)[0].rstrip() + return {"content": cleaned} + + +# ---- Qwen3-Coder ----------------------------------------------------------- + + +class Qwen3CoderTemplate: + """Qwen3-Coder uses Hermes-style framing plus a `<tool_call>` JSON tag.""" + + name: str = "qwen3_coder" + + _TOOL_CALL_RE = re.compile(r"<tool_call>(.*?)</tool_call>", re.DOTALL) + + def render(self, messages: Iterable[ChatMessage], add_generation_prompt: bool = True) -> str: + return HermesTemplate().render(messages, add_generation_prompt=add_generation_prompt) + + def parse_response(self, response: str) -> dict[str, str]: + cleaned = response.split("<|im_end|>", 1)[0].rstrip() + tool_calls = self._TOOL_CALL_RE.findall(cleaned) + content = self._TOOL_CALL_RE.sub("", cleaned).strip() + out: dict[str, str] = {"content": content} + if tool_calls: + out["tool_call"] = tool_calls[0].strip() + return out + + +# ---- Qwen3 reasoning ------------------------------------------------------- + + +class Qwen3ReasoningTemplate: + """Qwen3 / Qwen3.5 / Qwen3.6 thinking format with `<think>...</think>` blocks.""" + + name: str = "qwen3_reasoning" + + _THINK_RE = re.compile(r"<think>(.*?)</think>", re.DOTALL) + + def render(self, messages: Iterable[ChatMessage], add_generation_prompt: bool = True) -> str: + return HermesTemplate().render(messages, add_generation_prompt=add_generation_prompt) + + def parse_response(self, response: str) -> dict[str, str]: + cleaned = response.split("<|im_end|>", 1)[0].rstrip() + thoughts = self._THINK_RE.findall(cleaned) + content = self._THINK_RE.sub("", cleaned).strip() + out: dict[str, str] = {"content": content} + if thoughts: + out["thinking"] = thoughts[0].strip() + return out + + +# ---- registry -------------------------------------------------------------- + +_TEMPLATES: dict[str, ChatTemplate] = { + "hermes": HermesTemplate(), + "qwen3_coder": Qwen3CoderTemplate(), + "qwen3": Qwen3ReasoningTemplate(), + "qwen3_reasoning": Qwen3ReasoningTemplate(), + "deepseek_r1": Qwen3ReasoningTemplate(), +} + + +def get_template(name: str) -> ChatTemplate: + """Return the named template; default to Hermes if unknown.""" + return _TEMPLATES.get(name, _TEMPLATES["hermes"]) + + +def list_templates() -> list[str]: + """Return the names of all registered chat templates.""" + return sorted(_TEMPLATES) + + +# Back-compat alias for code that previously called `get_chat_template`. +get_chat_template = get_template diff --git a/mindxtrain/models/deepseek_v32.py b/mindxtrain/models/deepseek_v32.py new file mode 100644 index 0000000000000000000000000000000000000000..dd3a5daa301b83402d1372af969093f5aa9ae38b --- /dev/null +++ b/mindxtrain/models/deepseek_v32.py @@ -0,0 +1,38 @@ +"""DeepSeek V3.2 base-model preset. + +Reasoning-tuned checkpoint with `<think>...</think>` framing. Auto-registers. +""" + +from __future__ import annotations + +from pydantic import BaseModel, ConfigDict + + +class DeepSeekV32Preset(BaseModel): + model_config = ConfigDict(extra="forbid") + + hf_id: str = "deepseek-ai/DeepSeek-V3.2" + chat_template: str = "deepseek_r1" + max_seq_len: int = 65536 + fsdp_layer_class: str = "DeepseekV3DecoderLayer" + moe: bool = True + + +_PRESET = DeepSeekV32Preset() + + +def preset() -> DeepSeekV32Preset: + return _PRESET + + +def _register() -> None: + from mindxtrain.models.registry import register_preset + + register_preset(_PRESET.hf_id, _PRESET) + register_preset("deepseek_v32", _PRESET) + + +_register() + + +__all__ = ["DeepSeekV32Preset", "preset"] diff --git a/mindxtrain/models/glm51.py b/mindxtrain/models/glm51.py new file mode 100644 index 0000000000000000000000000000000000000000..ce7c643de22bdbf7cd498f9532c1cc4ba3761899 --- /dev/null +++ b/mindxtrain/models/glm51.py @@ -0,0 +1,40 @@ +"""GLM-5.1 base-model preset (Z.ai, 754B/40B-A MoE, MIT-licensed). + +Specialist track only — see LICENSE-MIT-upstream-glm51 for upstream license +terms. Auto-registers with `ModelRegistry` at import time. +""" + +from __future__ import annotations + +from pydantic import BaseModel, ConfigDict + + +class Glm51Preset(BaseModel): + model_config = ConfigDict(extra="forbid") + + hf_id: str = "zai-org/GLM-5.1-Base" + chat_template: str = "qwen3_reasoning" + max_seq_len: int = 32768 + fsdp_layer_class: str = "GLMDecoderLayer" + moe: bool = True + + +_PRESET = Glm51Preset() + + +def preset() -> Glm51Preset: + return _PRESET + + +# Auto-register so `ModelRegistry.presets()` knows about us. +def _register() -> None: + from mindxtrain.models.registry import register_preset + + register_preset(_PRESET.hf_id, _PRESET) + register_preset("glm51", _PRESET) + + +_register() + + +__all__ = ["Glm51Preset", "preset"] diff --git a/mindxtrain/models/mistral3.py b/mindxtrain/models/mistral3.py new file mode 100644 index 0000000000000000000000000000000000000000..9c4959daa6b03658063d4a115d1599f5b0abac2c --- /dev/null +++ b/mindxtrain/models/mistral3.py @@ -0,0 +1,35 @@ +"""Mistral Large 3 base-model preset. Auto-registers.""" + +from __future__ import annotations + +from pydantic import BaseModel, ConfigDict + + +class Mistral3Preset(BaseModel): + model_config = ConfigDict(extra="forbid") + + hf_id: str = "mistralai/Mistral-Large-3" + chat_template: str = "hermes" + max_seq_len: int = 32768 + fsdp_layer_class: str = "MistralDecoderLayer" + moe: bool = False + + +_PRESET = Mistral3Preset() + + +def preset() -> Mistral3Preset: + return _PRESET + + +def _register() -> None: + from mindxtrain.models.registry import register_preset + + register_preset(_PRESET.hf_id, _PRESET) + register_preset("mistral3", _PRESET) + + +_register() + + +__all__ = ["Mistral3Preset", "preset"] diff --git a/mindxtrain/models/phi4_mini.py b/mindxtrain/models/phi4_mini.py new file mode 100644 index 0000000000000000000000000000000000000000..fe52eaa576a8521d86ce48f6a05c26d092d3fd96 --- /dev/null +++ b/mindxtrain/models/phi4_mini.py @@ -0,0 +1,38 @@ +"""Phi-4-mini base-model preset (Microsoft). Auto-registers. + +Small-footprint default for laptop / single-GPU local fine-tuning. +""" + +from __future__ import annotations + +from pydantic import BaseModel, ConfigDict + + +class Phi4MiniPreset(BaseModel): + model_config = ConfigDict(extra="forbid") + + hf_id: str = "microsoft/Phi-4-mini-instruct" + chat_template: str = "hermes" + max_seq_len: int = 16384 + fsdp_layer_class: str = "Phi3DecoderLayer" + moe: bool = False + + +_PRESET = Phi4MiniPreset() + + +def preset() -> Phi4MiniPreset: + return _PRESET + + +def _register() -> None: + from mindxtrain.models.registry import register_preset + + register_preset(_PRESET.hf_id, _PRESET) + register_preset("phi4_mini", _PRESET) + + +_register() + + +__all__ = ["Phi4MiniPreset", "preset"] diff --git a/mindxtrain/models/qwen35.py b/mindxtrain/models/qwen35.py new file mode 100644 index 0000000000000000000000000000000000000000..b42276023fd4da807cdcaefad965feea1e787b59 --- /dev/null +++ b/mindxtrain/models/qwen35.py @@ -0,0 +1,38 @@ +"""Qwen3.5 base-model preset (Alibaba, Apache 2.0). + +Recommended primary base per mindxtrain2.md (Qwen3.5-122B-A10B). Auto-registers. +""" + +from __future__ import annotations + +from pydantic import BaseModel, ConfigDict + + +class Qwen35Preset(BaseModel): + model_config = ConfigDict(extra="forbid") + + hf_id: str = "Qwen/Qwen3.5-8B" + chat_template: str = "qwen3_reasoning" + max_seq_len: int = 32768 + fsdp_layer_class: str = "Qwen3DecoderLayer" + moe: bool = False + + +_PRESET = Qwen35Preset() + + +def preset() -> Qwen35Preset: + return _PRESET + + +def _register() -> None: + from mindxtrain.models.registry import register_preset + + register_preset(_PRESET.hf_id, _PRESET) + register_preset("qwen35", _PRESET) + + +_register() + + +__all__ = ["Qwen35Preset", "preset"] diff --git a/mindxtrain/models/registry.py b/mindxtrain/models/registry.py new file mode 100644 index 0000000000000000000000000000000000000000..c28fa89484180f8f4f0c82bfe02eba69a2edefb2 --- /dev/null +++ b/mindxtrain/models/registry.py @@ -0,0 +1,160 @@ +"""ModelRegistry — backend protocol + factory dispatch. + +Canonical home per mindxtrain2.md §Part 4 `models.registry`. Merges the previous +`automindx.models.base` (Backend ABC + Pydantic chat schemas) and +`automindx.models.factory` (decorator-style register/build). + +`Backend` is the plug interface; concrete backends live under +`mindxtrain.operator.backends.*` (vllm, openai_compat, ...). +""" + +from __future__ import annotations + +from abc import ABC, abstractmethod +from collections.abc import AsyncIterator, Callable +from typing import Literal + +from pydantic import BaseModel, ConfigDict, Field + +Role = Literal["system", "user", "assistant", "tool"] + + +class ChatMessage(BaseModel): + model_config = ConfigDict(extra="forbid") + role: Role + content: str + + +class ChatRequest(BaseModel): + model_config = ConfigDict(extra="forbid") + + model: str + messages: list[ChatMessage] + temperature: float = Field(default=0.7, ge=0.0, le=2.0) + max_tokens: int = Field(default=512, ge=1, le=131072) + stream: bool = False + + +class ChatResponse(BaseModel): + model_config = ConfigDict(extra="forbid") + + model: str + content: str + finish_reason: Literal["stop", "length", "error"] = "stop" + prompt_tokens: int = 0 + completion_tokens: int = 0 + + +class Backend(ABC): + """Pluggable inference backend; one of vLLM, OpenAI-compatible, HF Transformers, etc.""" + + name: str + + @abstractmethod + async def chat(self, request: ChatRequest) -> ChatResponse: ... + + @abstractmethod + async def stream_chat(self, request: ChatRequest) -> AsyncIterator[str]: ... + + +# Back-compat alias — old code imported `ModelBackend`. +ModelBackend = Backend + + +# ---- registry ------------------------------------------------------------- + +_REGISTRY: dict[str, Callable[..., Backend]] = {} + + +def register_backend(name: str) -> Callable[[Callable[..., Backend]], Callable[..., Backend]]: + """Decorator: register a Backend constructor under `name`.""" + + def _wrap(ctor: Callable[..., Backend]) -> Callable[..., Backend]: + if name in _REGISTRY: + msg = f"backend {name!r} already registered" + raise ValueError(msg) + _REGISTRY[name] = ctor + return ctor + + return _wrap + + +def build_backend(name: str, **kwargs: object) -> Backend: + """Instantiate the backend registered under `name`.""" + if name not in _REGISTRY: + available = ", ".join(sorted(_REGISTRY)) or "(none registered)" + msg = f"unknown backend {name!r}. registered: {available}" + raise KeyError(msg) + return _REGISTRY[name](**kwargs) + + +def list_backends() -> list[str]: + return sorted(_REGISTRY) + + +# ---- per-base-model preset registry -------------------------------------- + +_PRESETS: dict[str, BaseModel] = {} + + +def register_preset(name: str, preset: BaseModel) -> None: + """Register a per-base-model preset (Pydantic model).""" + _PRESETS[name] = preset + + +def get_preset(name: str) -> BaseModel: + if name not in _PRESETS: + available = ", ".join(sorted(_PRESETS)) or "(none registered)" + msg = f"unknown base model {name!r}. registered: {available}" + raise KeyError(msg) + return _PRESETS[name] + + +def list_presets() -> list[str]: + return sorted(_PRESETS) + + +def chat_template_for(name: str) -> str: + """Return the canonical chat-template name for a registered base model.""" + preset = get_preset(name) + return str(getattr(preset, "chat_template", "hermes")) + + +class ModelRegistry: + """Class facade over the module-level registry — preferred API per mindxtrain2.md.""" + + @staticmethod + def register(name: str) -> Callable[[Callable[..., Backend]], Callable[..., Backend]]: + return register_backend(name) + + @staticmethod + def build(name: str, **kwargs: object) -> Backend: + return build_backend(name, **kwargs) + + @staticmethod + def names() -> list[str]: + return list_backends() + + @staticmethod + def register_preset(name: str, preset: BaseModel) -> None: + register_preset(name, preset) + + @staticmethod + def preset(name: str) -> BaseModel: + return get_preset(name) + + @staticmethod + def presets() -> list[str]: + return list_presets() + + +# Side-effect imports — register the built-in backends. +# Side-effect imports — register the built-in base-model presets. +from mindxtrain.models import deepseek_v32 as _deepseek_v32 # noqa: E402, F401 +from mindxtrain.models import glm51 as _glm51 # noqa: E402, F401 +from mindxtrain.models import mistral3 as _mistral3 # noqa: E402, F401 +from mindxtrain.models import phi4_mini as _phi4_mini # noqa: E402, F401 +from mindxtrain.models import qwen35 as _qwen35 # noqa: E402, F401 +from mindxtrain.operator.backends import ollama as _ollama # noqa: E402, F401 +from mindxtrain.operator.backends import openai_compat as _openai_compat # noqa: E402, F401 +from mindxtrain.operator.backends import vllm as _vllm # noqa: E402, F401 diff --git a/mindxtrain/operator/__init__.py b/mindxtrain/operator/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/mindxtrain/operator/agent_loop.py b/mindxtrain/operator/agent_loop.py new file mode 100644 index 0000000000000000000000000000000000000000..ece404b48e16f531855ce0e03325cd1b46224c01 --- /dev/null +++ b/mindxtrain/operator/agent_loop.py @@ -0,0 +1,101 @@ +"""Bounded ReAct loop with doom-loop detector (ml-intern pattern). + +Caps at `max_steps` iterations; aborts if the same `(tool_name, arguments)` +pair fires `repeat_threshold` times in a row (doom loop). Pure stdlib. +""" + +from __future__ import annotations + +import json +from collections import deque +from collections.abc import Awaitable, Callable +from typing import Any + +from pydantic import BaseModel, ConfigDict, Field + + +class AgentLoopConfig(BaseModel): + model_config = ConfigDict(extra="forbid") + + max_steps: int = Field(default=20, ge=1, le=200) + repeat_threshold: int = Field(default=3, ge=2) + + +class DoomLoopDetected(RuntimeError): + """Raised when the same tool call repeats `repeat_threshold` times.""" + + +def _tool_signature(message: dict[str, Any]) -> str | None: + """Return a stable hash of `(tool_name, arguments)` if this is a tool call.""" + calls = message.get("tool_calls") or [] + if not calls: + return None + parts: list[str] = [] + for c in calls: + name = c.get("function", {}).get("name", "") + args = c.get("function", {}).get("arguments", "") + parts.append(f"{name}:{args}") + return "|".join(parts) + + +async def run_agent_loop( + chat: Callable[[list[dict[str, Any]]], Awaitable[dict[str, Any]]], + initial_messages: list[dict[str, Any]], + cfg: AgentLoopConfig | None = None, +) -> list[dict[str, Any]]: + """Run a bounded ReAct loop; return the full message trajectory. + + `chat` is a user-supplied async callable that takes the current message list + and returns the assistant's next reply (a dict). If the reply has no + `tool_calls`, the loop terminates. Otherwise the loop expects the caller to + have appended the tool result(s) to `messages` before the next iteration — + in this canonical implementation we simply re-call `chat(messages)` each + step, so the host integration is responsible for inserting tool outputs. + """ + cfg = cfg or AgentLoopConfig() + messages: list[dict[str, Any]] = list(initial_messages) + recent_signatures: deque[str | None] = deque(maxlen=cfg.repeat_threshold) + + for step in range(cfg.max_steps): + reply = await chat(messages) + messages.append(reply) + sig = _tool_signature(reply) + + if sig is None: + # Plain assistant turn with no tool calls — loop terminates cleanly. + return messages + + recent_signatures.append(sig) + if ( + len(recent_signatures) == cfg.repeat_threshold + and len(set(recent_signatures)) == 1 + ): + msg = ( + f"doom loop on step {step}: tool call {sig!r} repeated " + f"{cfg.repeat_threshold} times" + ) + raise DoomLoopDetected(msg) + + msg = f"max_steps={cfg.max_steps} exhausted without a final non-tool turn" + raise RuntimeError(msg) + + +def trajectory_summary(messages: list[dict[str, Any]]) -> dict[str, int]: + """Return per-role + tool-call counts for telemetry.""" + summary: dict[str, int] = {"system": 0, "user": 0, "assistant": 0, "tool": 0, "tool_calls": 0} + for m in messages: + role = m.get("role", "") + if role in summary: + summary[role] += 1 + for _ in m.get("tool_calls") or []: + summary["tool_calls"] += 1 + return summary + + +__all__ = ["AgentLoopConfig", "DoomLoopDetected", "run_agent_loop", "trajectory_summary"] + + +# Helper kept for backwards compatibility with prose docs that reference the +# function name. +def _serialize_signature(sig: str | None) -> str: + return json.dumps(sig) diff --git a/mindxtrain/operator/app.py b/mindxtrain/operator/app.py new file mode 100644 index 0000000000000000000000000000000000000000..a7a5235df1ca068363e05d82def6b40d49c59701 --- /dev/null +++ b/mindxtrain/operator/app.py @@ -0,0 +1,305 @@ +"""automindXtrain FastAPI app. + +Exposes: + GET / — coach UI (mindXtrain Coach) + GET /health — liveness check + POST /v1/chat/completions — OpenAI-compatible chat + POST /v1/agentic — mindX-native agentic dispatch (Day 5+) + /v1/training/jobs/* — public training-jobs API (mindX agents, + external clients). Bearer auth via + MINDXTRAIN_API_KEY when set. + GET /coach/* — Coach UI + API (recipes, autotune, cost) + +The production deployment lives at https://mindx.pythai.net — the Coach UI +is at /coach/ and the public training-jobs API is at /v1/training/jobs. +""" + +from __future__ import annotations + +import logging +import os +from contextlib import asynccontextmanager +from pathlib import Path +from typing import TYPE_CHECKING, Any + +import httpx +from fastapi import FastAPI, HTTPException + +if TYPE_CHECKING: + from collections.abc import AsyncIterator +from fastapi.responses import RedirectResponse +from fastapi.staticfiles import StaticFiles +from pydantic import BaseModel + +from mindxtrain import __version__ +from mindxtrain.models.registry import ChatRequest, ChatResponse, build_backend +from mindxtrain.operator.coach import router as coach_router +from mindxtrain.operator.training_api import router as training_router + +# ---- backend resolution -------------------------------------------------- + + +def _ollama_reachable(timeout_s: float = 1.0) -> bool: + """Probe ollama at MINDXTRAIN_OLLAMA_BASE_URL. + + Used by auto-detect to pick `ollama` as the default backend on hosts + where ollama is the only thing running (e.g., the laptop dev + environment). Strips `/v1` from the configured base URL because + ollama's health-style endpoint is `/api/tags`, not OpenAI-shaped. + """ + base = os.environ.get("MINDXTRAIN_OLLAMA_BASE_URL", "http://localhost:11434/v1") + probe_url = base.rstrip("/").removesuffix("/v1") + "/api/tags" + try: + with httpx.Client(timeout=timeout_s) as client: + return client.get(probe_url).status_code == 200 + except (httpx.HTTPError, OSError): + return False + + +def _vllm_reachable(timeout_s: float = 1.0) -> bool: + """Probe vLLM at MINDXTRAIN_VLLM_BASE_URL. + + Hits `/v1/models` — the OpenAI-compatible models listing endpoint + vLLM always exposes. This is what flips the production Coach chat + card on the MI300X droplet from "(no backend configured)" to + live, and what `/health` consults so a load balancer knows when + inference is actually warm. + """ + base = os.environ.get( + "MINDXTRAIN_VLLM_BASE_URL", + os.environ.get("AUTOMINDX_VLLM_BASE_URL", "http://localhost:8000/v1"), + ) + probe_url = base.rstrip("/") + "/models" + try: + with httpx.Client(timeout=timeout_s) as client: + return client.get(probe_url).status_code == 200 + except (httpx.HTTPError, OSError): + return False + + +def _vllm_first_model() -> str | None: + """Return the first model id vLLM lists, or None on failure. + + Lets `/coach/api/health` render "vllm (Qwen/Qwen3-8B) ready" in + prod the same way ollama does on the laptop. Best-effort: probe + failure → None and the UI degrades to just the backend name. + """ + base = os.environ.get( + "MINDXTRAIN_VLLM_BASE_URL", + os.environ.get("AUTOMINDX_VLLM_BASE_URL", "http://localhost:8000/v1"), + ) + probe_url = base.rstrip("/") + "/models" + try: + with httpx.Client(timeout=1.0) as client: + resp = client.get(probe_url) + if resp.status_code != 200: + return None + body = resp.json() + models = body.get("data", []) + if not models: + return None + first = models[0] + return first.get("id") if isinstance(first, dict) else None + except (httpx.HTTPError, OSError, ValueError, IndexError): + return None + + +def backend_reachable(name: str) -> bool: + """Live probe for a backend by name. Used by both /health and /coach health.""" + if name == "ollama": + return _ollama_reachable() + if name == "vllm": + return _vllm_reachable() + # openai_compat and unknown backends: we don't have a generic probe, + # so the chat-completions failure path remains the authoritative signal. + return False + + +def backend_first_model(name: str) -> str | None: + """Best-effort first-model lookup; None when the backend doesn't list one.""" + if name == "ollama": + return ollama_first_model() + if name == "vllm": + return _vllm_first_model() + return None + + +def resolve_backend_name() -> str: + """Pick the active backend. + + Resolution order: + 1. Explicit `MINDXTRAIN_BACKEND` env var (canonical). + 2. Legacy `AUTOMINDX_BACKEND` (back-compat with the pre-rename code). + 3. Auto-detect: ollama if reachable on localhost:11434, else vllm. + """ + explicit = ( + os.environ.get("MINDXTRAIN_BACKEND") + or os.environ.get("AUTOMINDX_BACKEND") + ) + if explicit: + return explicit + if _ollama_reachable(): + return "ollama" + return "vllm" + + +def ollama_first_model() -> str | None: + """Return the name of the first model ollama lists, or None on failure. + + Used by the Coach health endpoint to render + `ollama (qwen3:0.6b) ready` instead of just `ollama ready`. Best-effort: + a timeout / parse failure returns None, the UI still shows the backend + name without a model qualifier. + """ + base = os.environ.get("MINDXTRAIN_OLLAMA_BASE_URL", "http://localhost:11434/v1") + probe_url = base.rstrip("/").removesuffix("/v1") + "/api/tags" + try: + with httpx.Client(timeout=1.0) as client: + resp = client.get(probe_url) + if resp.status_code != 200: + return None + body = resp.json() + models = body.get("models", []) + # Prefer local (non-cloud) models first; the user's qwen3:0.6b + # ranks ahead of glm-5.1:cloud, deepseek-v4-pro:cloud, etc. + local = [m for m in models if ":cloud" not in (m.get("name") or "")] + chosen = (local or models)[0] if (local or models) else None + return chosen.get("name") if chosen else None + except (httpx.HTTPError, OSError, ValueError, IndexError): + return None + +@asynccontextmanager +async def _lifespan(_app: FastAPI) -> AsyncIterator[None]: + """Operator startup — optionally auto-launch a hands-free CPU training run. + + When `MINDXTRAIN_AUTOSTART` is set the operator kicks off a CPU + training run the moment uvicorn boots, so the Coach UI shows a live + session without anyone pressing "Run training". Autostart is off by + default so `TestClient` lifespans and CI never spawn a trainer. + Failures are swallowed — a bad autostart must never block boot. + """ + from mindxtrain.operator.coach.api import autostart_cpu_training + + try: + await autostart_cpu_training() + except Exception: # boot must survive any autostart fault + logging.getLogger("mindxtrain.operator").exception( + "autostart raised — Coach UI still available, launch manually", + ) + yield + + +app = FastAPI( + title="automindXtrain", + version=__version__, + description="Pluggable LLM cognitive runtime for the mindXtrain pipeline.", + lifespan=_lifespan, +) + +# --- coach UI ------------------------------------------------------------- + + +class _NoCacheStaticFiles(StaticFiles): + """StaticFiles that forces browser revalidation. + + The Coach JS/CSS change frequently; without this, browsers serve a stale + `coach.js` against fresh `index.html` (visible controls that don't wire up). + `no-cache` still allows efficient 304s via ETag — it just never serves stale. + """ + + async def get_response(self, path: str, scope: Any) -> Any: + response = await super().get_response(path, scope) + response.headers["Cache-Control"] = "no-cache, must-revalidate" + return response + + +_COACH_STATIC = Path(__file__).parent / "coach" / "static" +app.mount("/coach/static", _NoCacheStaticFiles(directory=_COACH_STATIC), name="coach-static") +app.include_router(coach_router) +app.include_router(training_router) + + +@app.get("/", include_in_schema=False) +async def root() -> RedirectResponse: + """Land on the Coach UI.""" + return RedirectResponse(url="/coach/") + + +class HealthResponse(BaseModel): + status: str + version: str + backend: str + backend_ready: bool + backend_model: str = "" + coach_url: str + + +@app.get("/health", response_model=HealthResponse) +async def health() -> HealthResponse: + """Liveness — always 200. + + `status` is "ok" even when the backend is unreachable so simple + load-balancer health checks don't take the operator out of + rotation just because vLLM is still warming. The structured + `backend_ready` field is what an inference-aware probe should + consult; `/readyz` enforces it as the HTTP status. + """ + backend = resolve_backend_name() + ready = backend_reachable(backend) + return HealthResponse( + status="ok", + version=__version__, + backend=backend, + backend_ready=ready, + backend_model=(backend_first_model(backend) or "") if ready else "", + coach_url="/coach/", + ) + + +@app.get("/readyz", include_in_schema=False) +async def readyz() -> dict[str, object]: + """Readiness gate — 503 when the resolved backend is unreachable. + + Use this when you want a probe that *fails* until inference is + actually warm (e.g., k8s readiness probe, uptime monitor that + pages on inference outage rather than process death). + """ + backend = resolve_backend_name() + if not backend_reachable(backend): + raise HTTPException( + status_code=503, + detail={"backend": backend, "reachable": False}, + ) + return {"backend": backend, "reachable": True} + + +@app.post("/v1/chat/completions", response_model=ChatResponse) +async def chat_completions(request: ChatRequest) -> ChatResponse: + backend_name = resolve_backend_name() + backend_kwargs: dict[str, object] = {} + if backend_name == "vllm": + backend_kwargs["base_url"] = os.environ.get( + "MINDXTRAIN_VLLM_BASE_URL", + os.environ.get("AUTOMINDX_VLLM_BASE_URL", "http://localhost:8000/v1"), + ) + elif backend_name == "ollama": + backend_kwargs["base_url"] = os.environ.get( + "MINDXTRAIN_OLLAMA_BASE_URL", "http://localhost:11434/v1", + ) + elif backend_name == "openai_compat": + backend_kwargs["base_url"] = os.environ["MINDXTRAIN_OPENAI_BASE_URL"] + backend_kwargs["api_key"] = os.environ.get("MINDXTRAIN_OPENAI_API_KEY", "") + + try: + backend = build_backend(backend_name, **backend_kwargs) + return await backend.chat(request) + except NotImplementedError as exc: + raise HTTPException(status_code=501, detail=str(exc)) from exc + except KeyError as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + + +@app.post("/v1/agentic") +async def agentic() -> dict[str, str]: + """mindX-native agentic endpoint (Day 5+).""" + raise HTTPException(status_code=501, detail="TODO Day 5: wire mindX MASTERMIND dispatch") diff --git a/mindxtrain/operator/approval.py b/mindxtrain/operator/approval.py new file mode 100644 index 0000000000000000000000000000000000000000..3120f3d712c560214dd25972437b36747987b0c2 --- /dev/null +++ b/mindxtrain/operator/approval.py @@ -0,0 +1,139 @@ +"""Approval flow — gate destructive tool calls behind explicit confirmation. + +Three transports: + - CLITransport: prompts on stdin (sync wrapped in async). + - WebTransport: registers a pending approval, served via the operator + FastAPI app's /approval/{id} endpoint (caller must wire that route). + - SlackTransport: posts an interactive message; replies via httpx-driven + polling against a configured webhook. +""" + +from __future__ import annotations + +import asyncio +import os +import uuid +from typing import Literal, Protocol + +import httpx +from pydantic import BaseModel, ConfigDict, Field + + +class ApprovalRequest(BaseModel): + model_config = ConfigDict(extra="forbid") + + request_id: str = Field(default_factory=lambda: uuid.uuid4().hex) + run_id: str + tool_name: str + arguments: dict[str, object] = Field(default_factory=dict) + reason: str = "" + + +class ApprovalTransport(Protocol): + async def request(self, req: ApprovalRequest) -> bool: ... + + +class CLITransport: + """Read y/N from stdin via asyncio.to_thread.""" + + name = "cli" + + async def request(self, req: ApprovalRequest) -> bool: + prompt = ( + f"\n[approval] run={req.run_id} tool={req.tool_name} " + f"reason={req.reason or '<none>'}\n" + f" arguments: {req.arguments}\n" + f" approve? [y/N] " + ) + resp = await asyncio.to_thread(input, prompt) + return resp.strip().lower() in {"y", "yes"} + + +class WebTransport: + """In-memory pending-approvals dict; the operator FastAPI app polls/resolves.""" + + name = "web" + + def __init__(self) -> None: + self._pending: dict[str, asyncio.Future[bool]] = {} + + async def request(self, req: ApprovalRequest) -> bool: + loop = asyncio.get_running_loop() + fut: asyncio.Future[bool] = loop.create_future() + self._pending[req.request_id] = fut + try: + return await fut + finally: + self._pending.pop(req.request_id, None) + + def resolve(self, request_id: str, approved: bool) -> bool: + """Called from the FastAPI route handler when the user clicks approve/deny.""" + fut = self._pending.get(request_id) + if fut is None or fut.done(): + return False + fut.set_result(approved) + return True + + def pending(self) -> list[str]: + return list(self._pending) + + +class SlackTransport: + """Post an interactive message; resolve via webhook POST back to us.""" + + name = "slack" + + def __init__(self, webhook_url: str | None = None, timeout_s: float = 300.0) -> None: + self.webhook_url = webhook_url or os.environ.get("MINDXTRAIN_SLACK_WEBHOOK", "") + self.timeout_s = timeout_s + self._pending: dict[str, asyncio.Future[bool]] = {} + + async def request(self, req: ApprovalRequest) -> bool: + if not self.webhook_url: + msg = "SlackTransport requires MINDXTRAIN_SLACK_WEBHOOK to be set" + raise RuntimeError(msg) + async with httpx.AsyncClient(timeout=10.0) as client: + await client.post( + self.webhook_url, + json={ + "text": f"approve {req.tool_name} on run {req.run_id}? ({req.reason})", + "request_id": req.request_id, + }, + ) + loop = asyncio.get_running_loop() + fut: asyncio.Future[bool] = loop.create_future() + self._pending[req.request_id] = fut + try: + return await asyncio.wait_for(fut, timeout=self.timeout_s) + except TimeoutError: + return False + finally: + self._pending.pop(req.request_id, None) + + def resolve(self, request_id: str, approved: bool) -> bool: + fut = self._pending.get(request_id) + if fut is None or fut.done(): + return False + fut.set_result(approved) + return True + + +def get_transport(name: Literal["cli", "web", "slack"] = "cli") -> ApprovalTransport: + if name == "cli": + return CLITransport() + if name == "web": + return WebTransport() + if name == "slack": + return SlackTransport() + msg = f"unknown approval transport: {name}" + raise ValueError(msg) + + +__all__ = [ + "ApprovalRequest", + "ApprovalTransport", + "CLITransport", + "SlackTransport", + "WebTransport", + "get_transport", +] diff --git a/mindxtrain/operator/backends/__init__.py b/mindxtrain/operator/backends/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/mindxtrain/operator/backends/ollama.py b/mindxtrain/operator/backends/ollama.py new file mode 100644 index 0000000000000000000000000000000000000000..6a913b7e13c8c28f0290d624d4fbbb2eea48914d --- /dev/null +++ b/mindxtrain/operator/backends/ollama.py @@ -0,0 +1,41 @@ +"""Ollama backend — the laptop dev counterpart of vLLM-rocm. + +Ollama serves an OpenAI-compatible `/v1/chat/completions` at port 11434 +by default. The wire protocol is identical to vLLM's, so this backend +is a thin alias of `OpenAICompatBackend` with sane defaults — no env vars +needed when ollama is the only thing running locally. + +The same OpenAI wire protocol runs on three deployment targets in the +mindXtrain ecosystem: + +- **Laptop dev:** ollama (this backend), serving local GGUF quantized models. +- **mindx.pythai.net VPS:** vLLM-rocm + ollama-as-fallback (per mindX's + `models/ollama.yaml` + `models/vllm.yaml`). +- **AMD Dev Cloud MI300X droplet:** vLLM-rocm post-train serving. + +`MINDXTRAIN_OLLAMA_BASE_URL` overrides the URL if ollama is on a non-default +host or port. +""" + +from __future__ import annotations + +import os + +from mindxtrain.models.registry import register_backend +from mindxtrain.operator.backends.openai_compat import OpenAICompatBackend + + +@register_backend("ollama") +class OllamaBackend(OpenAICompatBackend): + name = "ollama" + + def __init__(self, base_url: str | None = None, timeout_s: float = 120.0) -> None: + super().__init__( + base_url=base_url + or os.environ.get("MINDXTRAIN_OLLAMA_BASE_URL", "http://localhost:11434/v1"), + api_key=None, + timeout_s=timeout_s, + ) + + +__all__ = ["OllamaBackend"] diff --git a/mindxtrain/operator/backends/openai_compat.py b/mindxtrain/operator/backends/openai_compat.py new file mode 100644 index 0000000000000000000000000000000000000000..12c1b4629ced6e565ed0f01d4bc0239abec8f81c --- /dev/null +++ b/mindxtrain/operator/backends/openai_compat.py @@ -0,0 +1,96 @@ +"""OpenAI-compatible backend — works against OpenAI, Anthropic-via-proxy, +Groq, ZAI, or any HTTP endpoint that follows the OpenAI Chat Completions +protocol. + +Reads `MINDXTRAIN_OPENAI_BASE_URL` (default `https://api.openai.com/v1`) and +`MINDXTRAIN_OPENAI_API_KEY`. +""" + +from __future__ import annotations + +import json +import os +from collections.abc import AsyncIterator + +import httpx + +from mindxtrain.models.registry import ChatRequest, ChatResponse, ModelBackend, register_backend + + +@register_backend("openai_compat") +class OpenAICompatBackend(ModelBackend): + name = "openai_compat" + + def __init__( + self, + base_url: str | None = None, + api_key: str | None = None, + timeout_s: float = 120.0, + ) -> None: + self.base_url = (base_url or os.environ.get("MINDXTRAIN_OPENAI_BASE_URL", "https://api.openai.com/v1")).rstrip("/") + self.api_key = api_key or os.environ.get("MINDXTRAIN_OPENAI_API_KEY", "") + self.timeout_s = timeout_s + + def _headers(self) -> dict[str, str]: + h = {"Content-Type": "application/json"} + if self.api_key: + h["Authorization"] = f"Bearer {self.api_key}" + return h + + def _payload(self, request: ChatRequest, *, stream: bool) -> dict[str, object]: + return { + "model": request.model, + "messages": [m.model_dump() for m in request.messages], + "temperature": request.temperature, + "max_tokens": request.max_tokens, + "stream": stream, + } + + async def chat(self, request: ChatRequest) -> ChatResponse: + async with httpx.AsyncClient(timeout=self.timeout_s) as client: + resp = await client.post( + f"{self.base_url}/chat/completions", + json=self._payload(request, stream=False), + headers=self._headers(), + ) + resp.raise_for_status() + data = resp.json() + choice = (data.get("choices") or [{}])[0] + usage = data.get("usage") or {} + return ChatResponse( + model=data.get("model", request.model), + content=(choice.get("message") or {}).get("content", "") or "", + finish_reason=choice.get("finish_reason", "stop") or "stop", + prompt_tokens=int(usage.get("prompt_tokens", 0)), + completion_tokens=int(usage.get("completion_tokens", 0)), + ) + + async def stream_chat(self, request: ChatRequest) -> AsyncIterator[str]: + async def _gen() -> AsyncIterator[str]: + async with httpx.AsyncClient(timeout=self.timeout_s) as client: + async with client.stream( + "POST", + f"{self.base_url}/chat/completions", + json=self._payload(request, stream=True), + headers=self._headers(), + ) as resp: + resp.raise_for_status() + async for raw in resp.aiter_lines(): + if not raw or not raw.startswith("data:"): + continue + data = raw[5:].strip() + if data == "[DONE]": + return + try: + chunk = json.loads(data) + except json.JSONDecodeError: + continue + delta = (chunk.get("choices") or [{}])[0].get("delta", {}) + token = delta.get("content") + if token: + yield token + + return _gen() + + +__all__ = ["OpenAICompatBackend"] diff --git a/mindxtrain/operator/backends/vllm.py b/mindxtrain/operator/backends/vllm.py new file mode 100644 index 0000000000000000000000000000000000000000..098a0f2b29069579e43131e5d15c3d9846ccacab --- /dev/null +++ b/mindxtrain/operator/backends/vllm.py @@ -0,0 +1,88 @@ +"""vLLM backend — OpenAI-compatible HTTP client to a local vLLM-ROCm server. + +Reads `MINDXTRAIN_VLLM_BASE_URL` (default `http://localhost:8000/v1`). +Streams via the OpenAI `text/event-stream` protocol vLLM exposes. +""" + +from __future__ import annotations + +import json +import os +from collections.abc import AsyncIterator + +import httpx + +from mindxtrain.models.registry import ChatRequest, ChatResponse, ModelBackend, register_backend + + +@register_backend("vllm") +class VllmBackend(ModelBackend): + name = "vllm" + + def __init__( + self, + base_url: str | None = None, + timeout_s: float = 120.0, + ) -> None: + self.base_url = (base_url or os.environ.get("MINDXTRAIN_VLLM_BASE_URL", "http://localhost:8000/v1")).rstrip("/") + self.timeout_s = timeout_s + + def _payload(self, request: ChatRequest, *, stream: bool) -> dict[str, object]: + return { + "model": request.model, + "messages": [m.model_dump() for m in request.messages], + "temperature": request.temperature, + "max_tokens": request.max_tokens, + "stream": stream, + } + + async def chat(self, request: ChatRequest) -> ChatResponse: + async with httpx.AsyncClient(timeout=self.timeout_s) as client: + resp = await client.post( + f"{self.base_url}/chat/completions", + json=self._payload(request, stream=False), + ) + resp.raise_for_status() + data = resp.json() + choice = (data.get("choices") or [{}])[0] + usage = data.get("usage") or {} + return ChatResponse( + model=data.get("model", request.model), + content=(choice.get("message") or {}).get("content", "") or "", + finish_reason=choice.get("finish_reason", "stop") or "stop", + prompt_tokens=int(usage.get("prompt_tokens", 0)), + completion_tokens=int(usage.get("completion_tokens", 0)), + ) + + async def stream_chat(self, request: ChatRequest) -> AsyncIterator[str]: + async def _gen() -> AsyncIterator[str]: + async with httpx.AsyncClient(timeout=self.timeout_s) as client: + async with client.stream( + "POST", + f"{self.base_url}/chat/completions", + json=self._payload(request, stream=True), + ) as resp: + resp.raise_for_status() + async for raw in resp.aiter_lines(): + if not raw or not raw.startswith("data:"): + continue + data = raw[5:].strip() + if data == "[DONE]": + return + try: + chunk = json.loads(data) + except json.JSONDecodeError: + continue + delta = (chunk.get("choices") or [{}])[0].get("delta", {}) + token = delta.get("content") + if token: + yield token + + return _gen() + + +# Back-compat alias (old code referenced VLLMBackend with all-caps). +VLLMBackend = VllmBackend + + +__all__ = ["VLLMBackend", "VllmBackend"] diff --git a/mindxtrain/operator/coach/__init__.py b/mindxtrain/operator/coach/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2f0465a0860a939500439feef591c6693d723034 --- /dev/null +++ b/mindxtrain/operator/coach/__init__.py @@ -0,0 +1,8 @@ +"""coach — interactive UI that walks users through the mindxtrain pipeline. + +Mounted under /coach by mindxtrain.operator.app. Static assets at /coach/static/*. +""" + +from mindxtrain.operator.coach.api import router + +__all__ = ["router"] diff --git a/mindxtrain/operator/coach/api.py b/mindxtrain/operator/coach/api.py new file mode 100644 index 0000000000000000000000000000000000000000..48d512602605f4e86b3d91952752447f87ec72ce --- /dev/null +++ b/mindxtrain/operator/coach/api.py @@ -0,0 +1,2409 @@ +"""mindXtrain Coach — FastAPI router. + +Surfaces the differentiating pieces of the pipeline (recipes, autotune, +Axolotl compilation, cost vs H100) behind tiny JSON endpoints the static +HTML/JS UI consumes. + +The /api/runs/* routes own the live training-feedback loop. Events are +pushed to the browser via Server-Sent Events; see `mindxtrain.operator.runs` +for the registry + event schema. + +The /api/{github,droplet}/* routes share the same SSE pipeline by creating +synthetic Runs with reserved recipe names (`_github_push`, +`_droplet_provision`, `_droplet_sync`) and chaining shell-out steps via +`mindxtrain.deploy._orchestrator`. +""" + +from __future__ import annotations + +import asyncio +import json +import logging +import os +import re +import subprocess +import time +from collections.abc import AsyncIterator, Callable +from pathlib import Path +from typing import Any, Literal + +import yaml +from fastapi import APIRouter, HTTPException, Request +from fastapi.responses import FileResponse, StreamingResponse +from pydantic import BaseModel, ConfigDict, Field, ValidationError + +from mindxtrain.autotune.benchmark import run_autotune +from mindxtrain.autotune.plan import AutotunePlan +from mindxtrain.budget.pricing import MI300X_USDC_PER_HOUR +from mindxtrain.config.loader import list_recipes, render_recipe +from mindxtrain.config.schema import XTrainConfig +from mindxtrain.deploy import ( + amd_dev_cloud as _adc, +) +from mindxtrain.deploy import ( + droplet as _droplet_mod, +) +from mindxtrain.deploy import ( + github_push as _gh, +) +from mindxtrain.deploy._orchestrator import ( + droplet_provision_pipeline, + droplet_sync_pipeline, + github_push_pipeline, +) +from mindxtrain.operator import runs as _runs +from mindxtrain.train import compile_axolotl_yaml + +router = APIRouter(prefix="/coach", tags=["coach"]) + +_STATIC_DIR = Path(__file__).parent / "static" +_REGISTRY = _runs.default_registry() + +# Read-only map of the 2026 decentralized-training networks for the dcoach panel. +# Sourced from docs/decentralized-training-deep-dive-2026.md — referenced, not +# vendored; mindXtrain does not mine on any of these (all CUDA-first / gated). +_DECENTRALIZED_NETWORKS: list[dict[str, str]] = [ + { + "name": "Prime Intellect", + "what": "Open superintelligence stack: OpenDiLoCo + Environments Hub. " + "Flagship INTELLECT-3 (106B MoE) trained centralized — honest signal " + "that frontier RL post-training still favours co-located clusters.", + "hardware": "Compute Exchange aggregates heterogeneous supply (incl. some AMD).", + "token": "No token (Base Sepolia contracts, RewardsDistributor pattern).", + "fit": "RL post-training is the decentralization sweet spot — an x402-payable, " + "receipt-verified RL job is a natural contribution surface.", + }, + { + "name": "Templar · Bittensor SN3", + "what": "Covenant-72B (Mar 2026): 72B / ~1.1T tokens, 70+ permissionless miners " + "over home internet via SparseLoCo. The only live, incentivized, " + "permissionless training market.", + "hardware": "Prosumer multi-GPU (NVIDIA/CUDA-first).", + "token": "Live — dTAO alpha (τemplar) + TAO; Gauntlet loss-scoring + slashing.", + "fit": "Gauntlet is statistical/economic verification (does your update cut " + "loss?). A reproducible AOT artifact makes a contribution auditable.", + }, + { + "name": "Nous · Psyche", + "what": "DisTrO momentum-decoupling coordinated on Solana; Consilience-40B is " + "the largest internet pre-training run by params x tokens.", + "hardware": "3090-class inference target; larger training nodes (CUDA-first).", + "token": "No official token (beware impostor 'NOUS' Solana pairs).", + "fit": "On-chain checkpointing for churn tolerance pairs with a BLAKE3 " + "checkpoint receipt for end-to-end provenance.", + }, + { + "name": "Gensyn", + "what": "Verification-first ML compute protocol: execution / verification / " + "communication / coordination on an Ethereum rollup. RL Swarm + Verde.", + "hardware": "Low floor — CPU+32GB RAM or NVIDIA 3090/4090/5090/A100/H100.", + "token": "Testnet points → expected token at mainnet.", + "fit": "Verde/RepOps needs bitwise-deterministic execution — exactly what " + "mindXtrain's AOT-only policy guarantees. The closest verification match.", + }, + { + "name": "Pluralis · Node0", + "what": "First public model-parallel internet pretraining: Node0-7.5B, 1,642 GPUs " + "/ 300+ participants / 198 cities via Protocol Models (99% activation " + "compression). Weights sharded so no node holds the full model.", + "hardware": "Single 16GB consumer GPU (3090-class); CUDA ≤12.x required.", + "token": "No token (dashboard/reputational credit).", + "fit": "Unextractable-model ownership ↔ on-protocol asset registration " + "(AgenticPlace / ERC-8004) — provenance receipts make attribution real.", + }, +] + +_DECENTRALIZED_FIT: list[dict[str, str]] = [ + { + "primitive": "AOT-only autotune plan", + "mindxtrain": "The 60s probe freezes attention backend / GEMM / RCCL before " + "step 0 — no JIT autotune in the loop, so a run is reproducible.", + "maps_to": "Gensyn Verde + RepOps bitwise-reproducible training verification.", + }, + { + "primitive": "BLAKE3 verifiable receipt", + "mindxtrain": "manifest.json binds config + dataset + checkpoint + eval hashes; " + "`mindxtrain receipt` re-hashes and verifies the round-trip.", + "maps_to": "TOPLOC / checkpoint-hash verification; Templar Gauntlet auditing.", + }, + { + "primitive": "x402-metered training surface", + "mindxtrain": "A per-job x402 paywall in front of a verifiable training endpoint " + "(Algorand x402-avm) — pay-per-train with a receipt on completion.", + "maps_to": "Unbuilt territory — no network natively meters per-job crypto pay.", + }, + { + "primitive": "AgenticPlace / ERC-8004 registration", + "mindxtrain": "Publish the receipt → register the trained actor on AgenticPlace, " + "attest provenance, wire mindX fallback to the new checkpoint.", + "maps_to": "Pluralis unextractable-model ownership / on-chain asset attribution.", + }, +] + +# Strong refs to per-run watchdog tasks (otherwise the garbage collector +# can reap them mid-await and the sampler keeps running after a terminal +# status). Cleaned up by the watchdog itself once it returns. +_METRICS_WATCHDOGS: dict[str, asyncio.Task] = {} + +_log = logging.getLogger("mindxtrain.operator.coach") + +# Hands-free CPU training: the operator can auto-launch a run at boot so +# the Coach UI is live without anyone pressing "Run training". Safe ~90s +# smoke recipe by default; override with MINDXTRAIN_AUTOSTART_RECIPE. +_DEFAULT_AUTOSTART_RECIPE = "mindx_fallback_qwen3_1_5b_cpu_smoke" + +# Reference datacenter-GPU $/hr + VRAM for the (background) cost calculator. +H100_USDC_PER_HOUR = 4.00 +H200_USDC_PER_HOUR = 6.00 +A100_USDC_PER_HOUR = 1.50 + +# GPU VRAM (GB) — used to compute whether a workload fits per card. +_GPU_VRAM_GB = {"mi300x": 192, "h200": 141, "h100": 80, "a100": 80} + +# Approximate per-parameter training memory (bytes) by method: +# full = weights(2) + grads(2) + AdamW fp32 m/v + master (~12) ≈ 16; LoRA/QLoRA +# freeze the base so only a small adapter carries grads/opt state. +_BYTES_PER_PARAM = {"full": 16.0, "lora": 3.0, "qlora": 1.5} + + +def _workload_vram_gb(params_b: float, method: str, batch: int, seq_len: int) -> float: + """Rough peak training VRAM (GB) for a workload — weights/opt + activations.""" + base = params_b * _BYTES_PER_PARAM.get(method, 16.0) + # Activation memory grows with batch×seq and (weakly) model width. + activations = batch * seq_len * 1.0e-4 * (params_b ** 0.5) + return base + activations + + +class RecipeSummary(BaseModel): + name: str + base_model: str + method: str + gpus: int + description: str + + +class RecipeDetail(BaseModel): + name: str + yaml: str + summary: RecipeSummary + + +class CompileRequest(BaseModel): + recipe: str = Field(description="recipe name, e.g. qwen3_8b_sft_lora") + plan: AutotunePlan | None = None + + +class CompileResponse(BaseModel): + recipe: str + config_summary: RecipeSummary + plan: AutotunePlan + axolotl_yaml: dict[str, Any] + overrides: list[str] + + +class CostRequest(BaseModel): + model_config = ConfigDict(extra="forbid") + gpus: int = Field(default=1, ge=1, le=64) + hours: float = Field(default=1.5, gt=0.0, le=720.0) + safety_margin: float = Field(default=1.15, ge=1.0, le=2.0) + # Generalize beyond the hardcoded Qwen3-8B: the calculator now sizes VRAM + # from the actual workload (params, method, batch, seq). + params_b: float = Field(default=8.0, gt=0.0, le=2000.0, description="model size, billions of params") + method: Literal["full", "lora", "qlora"] = "full" + batch: int = Field(default=8, ge=1, le=4096) + seq_len: int = Field(default=4096, ge=64, le=1_048_576) + + +class CostBreakdown(BaseModel): + name: str + rate_usdc_per_hour: float + gpus: int + cost_usdc: float + fits_qwen3_8b_bf16_bs8_seq4096: bool + note: str + + +class CostResponse(BaseModel): + hours: float + safety_margin: float + needed_vram_gb: float + mi300x: CostBreakdown + h100: CostBreakdown + h200: CostBreakdown + a100: CostBreakdown + comparisons: list[CostBreakdown] = Field(default_factory=list) + cheapest_that_fits: str = "" + speedup_vs_h100_x: float + + +class CoachHealthResponse(BaseModel): + coach_version: str = "0.1.0" + chat_backend_ready: bool = False + chat_backend_name: str = "" + chat_backend_model: str = Field( + default="", + description=( + "When the detected backend is ollama, the first available model " + "name (e.g. 'qwen3:0.6b'). Empty for vllm/openai_compat or when " + "the probe fails." + ), + ) + recipes_available: int + + +def _summarize(cfg: XTrainConfig, name: str) -> RecipeSummary: + method = cfg.train.method.kind + desc = cfg.meta.description or f"{method.upper()} of {cfg.model.name} on {cfg.data.hf_id}." + return RecipeSummary( + name=name, + base_model=cfg.model.name, + method=method, + gpus=cfg.hardware.gpus, + description=desc, + ) + + +# ---- routes --------------------------------------------------------------- + +@router.get("/", response_class=FileResponse, include_in_schema=False) +async def coach_index() -> FileResponse: + return FileResponse(_STATIC_DIR / "index.html") + + +@router.get("/modelfile", response_class=FileResponse, include_in_schema=False) +async def coach_modelfile_page() -> FileResponse: + """Standalone Ollama Modelfile builder (opened in a separate window).""" + return FileResponse(_STATIC_DIR / "modelfile.html") + + +@router.get("/dcoach", response_class=FileResponse, include_in_schema=False) +async def coach_dcoach_page() -> FileResponse: + """dcoach — decentralized-aware proof loop (imprint a persona → prove recall).""" + return FileResponse(_STATIC_DIR / "dcoach.html") + + +@router.get("/prompts", response_class=FileResponse, include_in_schema=False) +async def coach_prompts_page() -> FileResponse: + """Ollama prompt-tools — cheap non-permanent tests, promote to a Modelfile.""" + return FileResponse(_STATIC_DIR / "prompts.html") + + +@router.get("/api/modelfile/params") +async def api_modelfile_params() -> dict[str, Any]: + """The full PARAMETER catalogue so the builder can render toggles + inputs.""" + from mindxtrain.deploy.modelfile import MODELFILE_PARAMS + + return {"parameters": [p.model_dump() for p in MODELFILE_PARAMS]} + + +@router.post("/api/modelfile/build") +async def api_modelfile_build(spec: dict[str, Any]) -> dict[str, str]: + """Render a Modelfile from a spec body → `{modelfile: <text>}`.""" + from pydantic import ValidationError + + from mindxtrain.deploy.modelfile import ModelfileSpec, render_modelfile + + try: + parsed = ModelfileSpec.model_validate(spec) + except ValidationError as exc: + raise HTTPException(status_code=422, detail=str(exc)) from exc + return {"modelfile": render_modelfile(parsed)} + + +@router.post("/api/modelfile/create") +async def api_modelfile_create(body: dict[str, Any]) -> dict[str, str]: + """Write the Modelfile and run `ollama create <tag>` (off the event loop).""" + from pydantic import ValidationError + + from mindxtrain.deploy.modelfile import ModelfileSpec, create_model + + tag = str(body.get("tag", "")).strip() + if not tag: + raise HTTPException(status_code=422, detail="a `tag` is required to create the model") + try: + parsed = ModelfileSpec.model_validate(body.get("spec", {})) + except ValidationError as exc: + raise HTTPException(status_code=422, detail=str(exc)) from exc + return await asyncio.to_thread(create_model, tag, parsed) + + +@router.get("/api/recipes", response_model=list[RecipeSummary]) +async def api_recipes() -> list[RecipeSummary]: + out: list[RecipeSummary] = [] + for name in list_recipes(): + cfg = XTrainConfig.model_validate(yaml.safe_load(render_recipe(name))) + out.append(_summarize(cfg, name)) + return out + + +@router.get("/api/recipes/{name}", response_model=RecipeDetail) +async def api_recipe(name: str) -> RecipeDetail: + if name not in list_recipes(): + raise HTTPException(status_code=404, detail=f"unknown recipe {name!r}") + yaml_text = render_recipe(name) + cfg = XTrainConfig.model_validate(yaml.safe_load(yaml_text)) + return RecipeDetail(name=name, yaml=yaml_text, summary=_summarize(cfg, name)) + + +@router.post("/api/bench", response_model=AutotunePlan) +async def api_bench() -> AutotunePlan: + """Day-1 dry-run; Day-2 swap to GPU-backed `run_autotune(dry_run=False)`.""" + return run_autotune(dry_run=True) + + +@router.post("/api/compile", response_model=CompileResponse) +async def api_compile(req: CompileRequest) -> CompileResponse: + if req.recipe not in list_recipes(): + raise HTTPException(status_code=404, detail=f"unknown recipe {req.recipe!r}") + cfg = XTrainConfig.model_validate(yaml.safe_load(render_recipe(req.recipe))) + plan = req.plan or run_autotune(dry_run=True) + axolotl_yaml = compile_axolotl_yaml(cfg, plan) + from mindxtrain.train.axolotl_compile import autotune_overrides_summary + + return CompileResponse( + recipe=req.recipe, + config_summary=_summarize(cfg, req.recipe), + plan=plan, + axolotl_yaml=axolotl_yaml, + overrides=autotune_overrides_summary(plan), + ) + + +@router.post("/api/cost", response_model=CostResponse) +async def api_cost(req: CostRequest) -> CostResponse: + """Cost + fit comparison vs datacenter GPUs (background; not shown in the UI). + + Sizes peak training VRAM from the workload (params/method/batch/seq), then for + each GPU computes the card count needed to fit, the cost, and whether a single + card fits. Generalized beyond the old hardcoded Qwen3-8B slide. + """ + needed = _workload_vram_gb(req.params_b, req.method, req.batch, req.seq_len) + rates = { + "mi300x": MI300X_USDC_PER_HOUR, "h200": H200_USDC_PER_HOUR, + "h100": H100_USDC_PER_HOUR, "a100": A100_USDC_PER_HOUR, + } + labels = { + "mi300x": "MI300X (192 GB HBM3)", "h200": "H200 (141 GB HBM3e)", + "h100": "H100 (80 GB HBM3)", "a100": "A100 (80 GB)", + } + + def _breakdown(key: str) -> CostBreakdown: + vram = _GPU_VRAM_GB[key] + fits_one = vram >= needed + # Cards needed to hold the workload (sharded), honoring the user's gpu count. + cards = max(req.gpus, -(-int(needed) // vram)) # ceil-div + cost = cards * req.hours * rates[key] * req.safety_margin + note = ( + f"fits on one card ({vram} GB ≥ {needed:.0f} GB needed)." + if fits_one + else f"needs {cards}x to fit {needed:.0f} GB (or quantize)." + ) + return CostBreakdown( + name=labels[key], rate_usdc_per_hour=rates[key], gpus=cards, + cost_usdc=round(cost, 2), fits_qwen3_8b_bf16_bs8_seq4096=fits_one, note=note, + ) + + mi300x, h200, h100, a100 = (_breakdown(k) for k in ("mi300x", "h200", "h100", "a100")) + comparisons = [mi300x, h200, h100, a100] + fitting = [c for c in comparisons if c.fits_qwen3_8b_bf16_bs8_seq4096] or comparisons + cheapest = min(fitting, key=lambda c: c.cost_usdc) + + return CostResponse( + hours=req.hours, + safety_margin=req.safety_margin, + needed_vram_gb=round(needed, 1), + mi300x=mi300x, h100=h100, h200=h200, a100=a100, + comparisons=comparisons, + cheapest_that_fits=cheapest.name, + speedup_vs_h100_x=round(h100.cost_usdc / mi300x.cost_usdc, 2) if mi300x.cost_usdc > 0 else 0.0, + ) + + +@router.get("/api/health", response_model=CoachHealthResponse) +async def api_health() -> CoachHealthResponse: + """Coach health. + + Reports the auto-detected chat backend (ollama if reachable on the + loopback, vllm otherwise) plus a `chat_backend_ready` boolean from a + live reachability probe. For ollama, also includes the first model + name so the UI can render "ollama (qwen3:0.6b) ready". + """ + from mindxtrain.operator.app import ( + backend_first_model, + backend_reachable, + resolve_backend_name, + ) + + backend = resolve_backend_name() + ready = backend_reachable(backend) + model_name = (backend_first_model(backend) or "") if ready else "" + return CoachHealthResponse( + chat_backend_ready=ready, + chat_backend_name=backend, + chat_backend_model=model_name, + recipes_available=len(list_recipes()), + ) + + +# ---- preflight + dream-corpus (training-run launch gate) ---------------- + +# Env vars the Coach UI surfaces as a preflight gate before kicking off a +# production training run. Required = the run will fail without them. +# Optional = the run still works but post-train steps (publish to HF Hub, +# Lighthouse pin, mindX fallback swap) silently no-op. +_PREFLIGHT_REQUIRED = ( + "AMD_DEV_CLOUD_TOKEN", + "AMD_DEV_CLOUD_SSH_KEY_ID", + "HF_TOKEN", + "HF_HUB_USERNAME", +) +_PREFLIGHT_OPTIONAL = ( + "MINDXTRAIN_API_KEY", + "MINDXTRAIN_MINDX_HOME", + "LIGHTHOUSE_API_KEY", +) + + +class PreflightResponse(BaseModel): + """Per-env-var presence (no values exposed) + readiness summary.""" + + vars: dict[str, bool] = Field( + description="Which env vars are present (True) or unset (False).", + ) + required: list[str] = Field(description="Subset of vars considered required.") + optional: list[str] = Field(description="Subset of vars considered optional.") + required_missing: list[str] = Field( + description="Required vars currently unset — the run is gated until these are populated.", + ) + ready: bool = Field(description="True iff required_missing is empty.") + + +class CorpusBucketStats(BaseModel): + """File / line / unique-row counts for one bucket of dream-cycle output.""" + + files: int = 0 + raw_lines: int = 0 + unique_rows: int = 0 + + +class DreamCorpusResponse(BaseModel): + """Sanity check that mindX's dream-cycle JSONL corpus is reachable. + + The dream cycle writes two JSONL streams per cycle: + - `*_training.jsonl` — STM-to-insight consolidation (phase 5b) + - `*_evolutions.jsonl` — insight-to-evolution proposals (phase 5c) + + Both are reported here; a recipe with `data.include_evolutions: true` + consumes the union. + """ + + root: str = Field(description="Filesystem root inspected.") + exists: bool + consolidation: CorpusBucketStats = Field(default_factory=CorpusBucketStats) + evolutions: CorpusBucketStats = Field(default_factory=CorpusBucketStats) + ready: bool = Field( + description="True iff exists and at least one bucket has unique rows.", + ) + note: str | None = Field( + default=None, + description="Friendly error message when the path is missing or empty.", + ) + + +@router.get("/api/preflight", response_model=PreflightResponse) +async def api_preflight() -> PreflightResponse: + """Report which env vars the launch flow needs, without exposing values. + + Used by the Coach UI's first step card to gate the training-run launch. + Returns `ready=False` when any required var is unset so the UI can halt + the auto-advance flow and prompt the operator to populate `.env`. + """ + all_vars = list(_PREFLIGHT_REQUIRED) + list(_PREFLIGHT_OPTIONAL) + vars_present = {name: bool(os.environ.get(name, "").strip()) for name in all_vars} + required_missing = [n for n in _PREFLIGHT_REQUIRED if not vars_present[n]] + return PreflightResponse( + vars=vars_present, + required=list(_PREFLIGHT_REQUIRED), + optional=list(_PREFLIGHT_OPTIONAL), + required_missing=required_missing, + ready=not required_missing, + ) + + +@router.get("/api/dream-corpus", response_model=DreamCorpusResponse) +async def api_dream_corpus(root: str | None = None) -> DreamCorpusResponse: + """Stats for the mindX dream-cycle JSONL corpus the recipe will consume. + + Resolution order for the corpus root: + 1. Explicit `?root=` query arg. + 2. `$MINDXTRAIN_MINDX_HOME/data/memory` if the env var is set. + 3. `/home/hacker/mindX/data/memory` (the documented default). + + Returns `ready=False` with a `note` if the path doesn't exist or has no + unique rows yet (e.g. a fresh mindX install before its first dream cycle). + """ + from mindxtrain.data.sources.mindx_dreams import ( + count_mindx_dreams, + count_mindx_evolutions, + ) + + if root is not None: + corpus_root = Path(root).expanduser() + else: + home = os.environ.get("MINDXTRAIN_MINDX_HOME", "/home/hacker/mindX") + corpus_root = Path(home).expanduser() / "data" / "memory" + + if not corpus_root.exists(): + return DreamCorpusResponse( + root=str(corpus_root), + exists=False, + ready=False, + note=( + f"corpus root not found: {corpus_root}. " + "Set MINDXTRAIN_MINDX_HOME or pass ?root=… to point at the " + "mindX data/memory directory." + ), + ) + + consolidation = CorpusBucketStats(**count_mindx_dreams(corpus_root)) + evolutions = CorpusBucketStats(**count_mindx_evolutions(corpus_root)) + ready = (consolidation.unique_rows + evolutions.unique_rows) > 0 + note = ( + None + if ready + else ( + "corpus root exists but contains no dream JSONL — run a dream " + "cycle in mindX (agents/machine_dreaming.py) before training." + ) + ) + return DreamCorpusResponse( + root=str(corpus_root), + exists=True, + consolidation=consolidation, + evolutions=evolutions, + ready=ready, + note=note, + ) + + +# ---- create dataset (author a script for an actor) ---------------------- +# A model is an actor; an actor has a persona (voice) and a script (the +# training examples). This lets the operator author a small script in the +# browser and save it as `source: local` JSONL the recipes can imprint from. + +_SAFE_NAME = re.compile(r"[^a-z0-9_-]+") + + +def _datasets_root() -> Path: + """Where authored scripts live. Override with MINDXTRAIN_DATASETS_DIR.""" + return Path(os.environ.get("MINDXTRAIN_DATASETS_DIR", "./out/datasets")) + + +def _safe_dataset_name(name: str) -> str: + cleaned = _SAFE_NAME.sub("-", name.strip().lower()).strip("-") + return cleaned or "script" + + +class ExchangeIn(BaseModel): + model_config = ConfigDict(extra="forbid") + user: str + assistant: str + + +class CreateScriptRequest(BaseModel): + model_config = ConfigDict(extra="forbid") + name: str = Field(description="dataset name; becomes out/datasets/<name>/script.jsonl") + persona: str = Field(default="", description="built-in persona key (overrides persona_name/system_prompt)") + persona_name: str = "actor" + system_prompt: str = "" + voice_examples: list[str] = Field(default_factory=list) + exchanges: list[ExchangeIn] = Field(default_factory=list) + skills: list[str] = Field(default_factory=list, description="skill bundles to mix in") + seed_voice: bool = True + + +class ScriptInfo(BaseModel): + model_config = ConfigDict(extra="forbid") + name: str + path: str + rows: int + persona_name: str + skills: list[str] = Field(default_factory=list) + train_params: dict[str, int] = Field(default_factory=dict) + + +class ScriptPreview(BaseModel): + model_config = ConfigDict(extra="forbid") + name: str + path: str + rows: int + sample: list[dict[str, Any]] + + +@router.get("/api/persona", response_model=dict) +async def api_persona() -> dict[str, Any]: + """The persona Coach pre-fills the create-script form with (clean-room). + + Loaded from `MINDXTRAIN_PERSONA_PATH` if set, else a minimal default. Never + copies mindX bytes — reads recognised fields at runtime. + """ + from mindxtrain.data.scripts import load_persona + + p = load_persona() + return { + "name": p.name, + "system_prompt": p.system_prompt, + "voice_examples": list(p.voice_examples), + } + + +@router.get("/api/personas") +async def api_personas() -> dict[str, Any]: + """Built-in personas + toggleable skills for the Create-script picker.""" + from mindxtrain.data import personas as _pz + + return {"personas": _pz.list_personas(), "skills": _pz.list_skills()} + + +@router.post("/api/datasets", response_model=ScriptInfo) +async def api_create_dataset(req: CreateScriptRequest) -> ScriptInfo: + """Author a script from a persona + optional skills + exchanges → `source: local` JSONL. + + Skills (software_engineer / platform_architect / bash / solidity) mix their + in-domain exchanges into the script. Returns the row count + training params + auto-derived from the dataset size. + """ + from mindxtrain.data import personas as _pz + from mindxtrain.data.scripts import ( + Exchange, + Persona, + build_script_rows, + derive_training_params, + write_script_jsonl, + ) + + # Base persona: a built-in (with skills mixed in) or the explicit fields. + if req.persona: + persona, skill_exchanges = _pz.compose(req.persona, req.skills) + else: + base = Persona( + name=req.persona_name or "actor", + system_prompt=req.system_prompt, + voice_examples=list(req.voice_examples), + ) + persona, skill_exchanges = _pz.compose(base, req.skills) + + exchanges = [Exchange(user=e.user, assistant=e.assistant) for e in req.exchanges] + exchanges.extend(skill_exchanges) + + if not exchanges and not (req.seed_voice and persona.voice_examples): + raise HTTPException( + status_code=422, + detail="provide an exchange, a skill, or a voice example to seed.", + ) + + name = _safe_dataset_name(req.name) + out_path = _datasets_root() / name / "script.jsonl" + rows_list = build_script_rows(persona, exchanges, seed_voice=req.seed_voice) + write_script_jsonl(rows_list, out_path) + rows = len(rows_list) + return ScriptInfo( + name=name, path=str(out_path), rows=rows, persona_name=persona.name, + skills=[s for s in req.skills if s in _pz.SKILLS], + train_params=derive_training_params(rows), + ) + + +@router.get("/api/datasets", response_model=list[ScriptInfo]) +async def api_list_datasets() -> list[ScriptInfo]: + """List authored scripts under the datasets root (newest dirs first).""" + if not _datasets_root().exists(): + return [] + out: list[ScriptInfo] = [] + for d in sorted(_datasets_root().iterdir(), reverse=True): + script = d / "script.jsonl" + if not script.is_file(): + continue + rows = sum(1 for line in script.read_text().splitlines() if line.strip()) + out.append(ScriptInfo(name=d.name, path=str(script), rows=rows, persona_name="")) + return out + + +@router.get("/api/datasets/{name}", response_model=ScriptPreview) +async def api_preview_dataset(name: str) -> ScriptPreview: + """Preview the first few rows of an authored script.""" + script = _datasets_root() / _safe_dataset_name(name) / "script.jsonl" + if not script.is_file(): + raise HTTPException(status_code=404, detail=f"no script for {name!r}") + sample: list[dict[str, Any]] = [] + rows = 0 + for line in script.read_text().splitlines(): + line = line.strip() + if not line: + continue + rows += 1 + if len(sample) < 5: + try: + sample.append(json.loads(line)) + except json.JSONDecodeError: + continue + return ScriptPreview(name=_safe_dataset_name(name), path=str(script), rows=rows, sample=sample) + + +# ---- imprint measurement (recall before/after) -------------------------- +# Score how much an actor's utterances moved toward the persona voice after +# training. Scoring is fast + dependency-light; the heavy generation that +# produces the before/after utterances runs in `mindxtrain imprint` (CLI) or +# the e2e test so the operator event loop never blocks on model inference. + + +class ImprintScoreRequest(BaseModel): + model_config = ConfigDict(extra="forbid") + inquiries: list[str] + before: list[str] + after: list[str] + baseline: list[str] + + +@router.post("/api/imprint/score") +async def api_imprint_score(req: ImprintScoreRequest) -> dict[str, Any]: + """Score a persona imprint from supplied before/after utterances + baseline.""" + from mindxtrain.eval.imprint import score_imprint + + report = score_imprint(req.inquiries, req.before, req.after, req.baseline) + return report.model_dump() + + +# ---- classroom test + autotune feedback (dcoach proof loop) ------------- + + +class ClassroomEvalRequest(BaseModel): + model_config = ConfigDict(extra="forbid") + inquiries: list[str] + before: list[str] + after: list[str] + baseline: list[str] + use_judge: bool = False + model: str = "" + base_url: str | None = None + + +class FeedbackRequest(BaseModel): + model_config = ConfigDict(extra="forbid") + run_id: str + params: dict[str, int] + classroom_score: float + passed: bool + boardroom_outcome: str = "unknown" + + +@router.post("/api/classroom/evaluate") +async def api_classroom_evaluate(req: ClassroomEvalRequest) -> dict[str, Any]: + """Run the classroom before/after test — did the trained model recall the persona? + + Scores from supplied before/after utterances + the persona baseline (the heavy model + generation that produces the utterances happens client-side / in the loop). Optional + `use_judge` runs the pairwise LLM judge off the event loop. + """ + from mindxtrain.governance.classroom import evaluate_classroom + + report = await asyncio.to_thread( + evaluate_classroom, req.inquiries, req.before, req.after, req.baseline, + use_judge=req.use_judge, model=(req.model or None), base_url=req.base_url, + ) + return report.model_dump() + + +@router.post("/api/autotune/feedback") +async def api_autotune_feedback(req: FeedbackRequest) -> dict[str, Any]: + """Record a training outcome and suggest improved params for the next run.""" + from mindxtrain.autotune import feedback as _fb + + outcome = req.boardroom_outcome if req.boardroom_outcome in ( + "approved", "rejected", "disputed", "unknown") else "unknown" + _fb.record( + run_id=req.run_id, params=req.params, classroom_score=req.classroom_score, + passed=req.passed, boardroom_outcome=outcome, # type: ignore[arg-type] + ) + suggestion = _fb.suggest_next_params( + req.params, passed=req.passed, classroom_score=req.classroom_score, + ) + return {"recorded": True, "suggested_next_params": suggestion} + + +# ---- prompt-tools: cheap non-permanent eval (similarity + optional judge) ---- + + +class PromptEvalRequest(BaseModel): + model_config = ConfigDict(extra="forbid") + query: str = "" + response: str + reference: str + use_judge: bool = False + model: str = "" + base_url: str | None = None + guidelines: str = "" + + +@router.post("/api/eval/prompt") +async def api_eval_prompt(req: PromptEvalRequest) -> dict[str, Any]: + """Score a model response against a reference — the cheap, non-permanent test + behind the prompt-tools page. Always runs the (free) semantic-similarity + evaluator; `use_judge` adds the LLM correctness judge, and a non-empty + `guidelines` adds the rubric judge. Judges run off the event loop. + """ + from mindxtrain.eval.llama_evals import ( + CorrectnessEvaluator, + GuidelineEvaluator, + SemanticSimilarityEvaluator, + ) + + scores: dict[str, Any] = {} + sim = SemanticSimilarityEvaluator().evaluate(req.response, req.reference) + scores["semantic_similarity"] = sim.model_dump() + + if req.use_judge: + judge = CorrectnessEvaluator(model=(req.model or "default"), base_url=req.base_url) + corr = await asyncio.to_thread(judge.evaluate, req.query, req.response, req.reference) + scores["correctness"] = corr.model_dump() + + if req.guidelines.strip(): + gj = GuidelineEvaluator(model=(req.model or "default"), base_url=req.base_url) + gres = await asyncio.to_thread(gj.evaluate, req.response, req.guidelines) + scores["guideline"] = gres.model_dump() + + vals = [s["score"] for s in scores.values()] + overall = sum(vals) / len(vals) if vals else 0.0 + return {"scores": scores, "overall": round(overall, 4), + "advantageous": overall >= 0.6} + + +# ---- dcoach: the full proof loop, streamed (imprint → classroom → boardroom) -- + + +class DcoachRunRequest(BaseModel): + model_config = ConfigDict(extra="forbid") + persona: str = "codephreak" + skills: list[str] = Field(default_factory=list) + base_model: str = "HuggingFaceTB/SmolLM2-135M" + board_preset: str = "classic_triad" + board_model: str | None = None + max_new_tokens: int = Field(default=48, ge=8, le=256) + run_id: str | None = None + + +@router.post("/api/dcoach/run") +async def api_dcoach_run(req: DcoachRunRequest) -> StreamingResponse: + """Run the dcoach proof loop and stream every phase as SSE. + + Heavy (real CPU imprint-training + before/after generation), so it runs in a + worker thread; `on_event(phase, msg)` is bridged onto the event loop through a + queue. Each `data:` line is a JSON `{phase, msg}`; the terminal `{phase:"result"}` + carries the full `ProofResult`, then `data: [DONE]`. + """ + import tempfile + import threading + + loop = asyncio.get_running_loop() + queue: asyncio.Queue[dict[str, Any] | None] = asyncio.Queue() + run_id = req.run_id or f"dcoach-{int(time.time())}" + + def _push(item: dict[str, Any] | None) -> None: + loop.call_soon_threadsafe(queue.put_nowait, item) + + def _emit(phase: str, msg: str) -> None: + _push({"phase": phase, "msg": msg}) + + def _work() -> None: + try: + from mindxtrain.governance.proof_loop import run_proof_loop + + out = tempfile.mkdtemp(prefix="dcoach-") + result = run_proof_loop( + run_id=run_id, + persona=req.persona, + skills=req.skills, + base_model=req.base_model, + out_dir=out, + board_preset=req.board_preset, + board_model=req.board_model, + force_cpu=True, + max_new_tokens=req.max_new_tokens, + on_event=_emit, + ) + _push({"phase": "result", "result": result.model_dump()}) + except Exception as exc: # surface in-stream, never 500 mid-stream + _push({"phase": "error", "msg": str(exc)}) + finally: + _push(None) + + threading.Thread(target=_work, name=f"dcoach-{run_id}", daemon=True).start() + + async def _gen() -> AsyncIterator[str]: + yield f"data: {json.dumps({'phase': 'start', 'run_id': run_id})}\n\n" + while True: + item = await queue.get() + if item is None: + break + yield f"data: {json.dumps(item)}\n\n" + yield "data: [DONE]\n\n" + + return StreamingResponse(_gen(), media_type="text/event-stream", headers=_sse_headers()) + + +@router.get("/api/decentralized") +async def api_decentralized() -> dict[str, Any]: + """Read-only map of the 2026 decentralized-training networks + how mindXtrain + fits each (AOT-only ⇒ verifiable receipt; x402-metered job; AgenticPlace). + + Sourced from `docs/decentralized-training-deep-dive-2026.md`. mindXtrain does + not mine on these networks (all are CUDA-locked / hardware-gated) — it exposes + a *verifiable, payable* training surface compatible with their verification + primitives. + """ + return { + "thesis": ( + "mindXtrain's AOT-only discipline (the autotune plan is frozen before " + "the first step) makes a run bit-for-bit reproducible — the same " + "property Verde/RepOps verification needs. Bind that to a BLAKE3 " + "receipt and the run becomes an x402-payable, ERC-8004-attestable job " + "registerable on AgenticPlace." + ), + "networks": _DECENTRALIZED_NETWORKS, + "fit": _DECENTRALIZED_FIT, + } + + +# ---- governance: boardroom (any-N) + dojo (prime-N) --------------------- +# A model is an actor; the classroom graduates it; the boardroom decides about +# the graduation; a disputed boardroom is settled by a prime-sized dojo. The +# boardroom/dojo can be backed by real models (use_models) or tallied from +# supplied votes. Model deliberation runs in a worker thread so the operator +# event loop never blocks on inference. + + +class MemberIn(BaseModel): + model_config = ConfigDict(extra="forbid") + id: str + role: str = "generalist" + model: str = "" + + +class ConveneRequest(BaseModel): + model_config = ConfigDict(extra="forbid") + motion: str + members: list[MemberIn] + quorum: float = Field(default=0.5, ge=0.0, le=1.0) + votes: dict[str, str] | None = None + use_models: bool = False + base_url: str | None = None + + +class DojoSettleRequest(BaseModel): + model_config = ConfigDict(extra="forbid") + motion: str + size: int = 3 + model: str = "" + votes: dict[str, str] | None = None + use_models: bool = False + base_url: str | None = None + + +@router.get("/api/boardroom/presets") +async def api_boardroom_presets() -> dict[str, list[str]]: + """Named preset boards → their advisor roles.""" + from mindxtrain.governance.boardroom import PRESET_BOARDS + + return {name: list(roles) for name, roles in PRESET_BOARDS.items()} + + +@router.get("/api/models") +async def api_models() -> dict[str, Any]: + """Model ids the configured chat backend exposes, so the Boardroom card can + pick a model that's actually installed (best-effort; `[]` if unreachable).""" + import httpx + + from mindxtrain.governance.panel import resolve_chat_base_url + + base = resolve_chat_base_url() + models: list[str] = [] + try: + with httpx.Client(timeout=3.0) as client: + resp = client.get(f"{base}/models") + resp.raise_for_status() + data = resp.json() + models = [m.get("id") for m in (data.get("data") or []) if m.get("id")] + except (httpx.HTTPError, OSError, ValueError): + models = [] + # Local models first so the chat/boardroom default isn't a cloud model + # (ollama cloud tags end in ":cloud" or "-cloud"). + models.sort(key=lambda m: ("cloud" in m.lower(), m)) + return {"base_url": base, "models": models} + + +# ---- chat: AI-SDK-style streaming + ollama controls --------------------- +# The chat streams token deltas as Server-Sent Events (the AI SDK "text stream" +# pattern) so responses render live, and exposes start/stop/status for the local +# ollama server + model interaction. See docs/coach.md. + + +def _resolve_chat_backend() -> Any: + """Build the active chat backend (ollama / vllm / openai_compat).""" + from mindxtrain.models.registry import build_backend + from mindxtrain.operator.app import resolve_backend_name + + name = resolve_backend_name() + kwargs: dict[str, Any] = {} + if name == "vllm": + kwargs["base_url"] = os.environ.get( + "MINDXTRAIN_VLLM_BASE_URL", + os.environ.get("AUTOMINDX_VLLM_BASE_URL", "http://localhost:8000/v1"), + ) + elif name == "ollama": + kwargs["base_url"] = os.environ.get("MINDXTRAIN_OLLAMA_BASE_URL", "http://localhost:11434/v1") + elif name == "openai_compat": + kwargs["base_url"] = os.environ.get("MINDXTRAIN_OPENAI_BASE_URL", "") + kwargs["api_key"] = os.environ.get("MINDXTRAIN_OPENAI_API_KEY", "") + return build_backend(name, **kwargs) + + +@router.post("/api/chat/stream") +async def api_chat_stream(body: dict[str, Any]) -> StreamingResponse: + """Stream a chat completion as an SSE text stream of token deltas. + + Body: `{model, messages:[{role,content}], max_tokens?, temperature?}`. Each SSE + `data:` line is a JSON-encoded token; the stream ends with `data: [DONE]`. Mirrors + the AI SDK text-stream protocol so the client renders tokens as they arrive. + """ + from pydantic import ValidationError + + from mindxtrain.models.registry import ChatRequest + + payload = { + "model": str(body.get("model") or "").strip() or "default", + "messages": body.get("messages") or [], + "max_tokens": int(body.get("max_tokens") or 512), + "temperature": float(body.get("temperature", 0.7)), + "stream": True, + } + try: + req = ChatRequest.model_validate(payload) + except (ValidationError, ValueError, TypeError) as exc: + raise HTTPException(status_code=422, detail=str(exc)) from exc + + backend = _resolve_chat_backend() + + async def _gen() -> Any: + try: + stream = await backend.stream_chat(req) + async for token in stream: + yield f"data: {json.dumps(token)}\n\n" + except Exception as exc: # surface backend errors in-stream, never 500 mid-stream + yield f"event: error\ndata: {json.dumps(str(exc))}\n\n" + yield "data: [DONE]\n\n" + + return StreamingResponse(_gen(), media_type="text/event-stream", headers=_sse_headers()) + + +def _ollama_serve_pids() -> list[int]: + """PIDs of running `ollama serve` processes (best-effort).""" + import subprocess + + try: + out = subprocess.run( + ["pgrep", "-f", "ollama serve"], capture_output=True, text=True, timeout=5, check=False, + ) + except (OSError, subprocess.SubprocessError): + return [] + return [int(x) for x in out.stdout.split() if x.strip().isdigit()] + + +@router.get("/api/ollama/status") +async def api_ollama_status() -> dict[str, Any]: + """Whether the local ollama server is reachable + installed + running.""" + import shutil + + import httpx + + from mindxtrain.governance.panel import resolve_chat_base_url + + base = resolve_chat_base_url() + reachable = False + try: + with httpx.Client(timeout=2.0) as client: + reachable = client.get(f"{base}/models").status_code == 200 + except (httpx.HTTPError, OSError): + reachable = False + return { + "reachable": reachable, + "has_ollama_bin": shutil.which("ollama") is not None, + "serve_pids": _ollama_serve_pids(), + "base_url": base, + } + + +@router.post("/api/ollama/start") +async def api_ollama_start() -> dict[str, Any]: + """Start `ollama serve` (detached) if it isn't already running.""" + import shutil + import subprocess + + if shutil.which("ollama") is None: + raise HTTPException(status_code=422, detail="ollama binary not found on PATH") + if _ollama_serve_pids(): + return {"started": False, "note": "ollama serve already running"} + try: + subprocess.Popen( + ["ollama", "serve"], + stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, start_new_session=True, + ) + except (OSError, subprocess.SubprocessError) as exc: + raise HTTPException(status_code=500, detail=f"failed to start ollama: {exc}") from exc + return {"started": True} + + +@router.post("/api/ollama/stop") +async def api_ollama_stop() -> dict[str, Any]: + """Stop the local `ollama serve` process(es).""" + import subprocess + + pids = _ollama_serve_pids() + if not pids: + return {"stopped": False, "note": "no `ollama serve` process found"} + try: + subprocess.run(["pkill", "-f", "ollama serve"], timeout=5, check=False) + except (OSError, subprocess.SubprocessError) as exc: + raise HTTPException(status_code=500, detail=str(exc)) from exc + return {"stopped": True, "pids": pids} + + +@router.post("/api/boardroom/convene") +async def api_boardroom_convene(req: ConveneRequest) -> dict[str, Any]: + """Convene a boardroom on a motion. Tally supplied `votes`, or `use_models` + to have each member's model deliberate (run off the event loop).""" + from pydantic import ValidationError + + from mindxtrain.governance import Boardroom, Member + + try: + members = [Member(id=m.id, role=m.role, model=m.model) for m in req.members] # type: ignore[arg-type] + except ValidationError as exc: + raise HTTPException(status_code=422, detail=str(exc)) from exc + if not members: + raise HTTPException(status_code=422, detail="a boardroom needs at least one member") + board = Boardroom(members=members, quorum=req.quorum) + + deliberations: list[dict[str, Any]] = [] + if req.use_models: + from mindxtrain.governance import panel as _panel + + async def _one(m: Member) -> Any: + return await asyncio.to_thread(_panel.deliberate, m, req.motion, base_url=req.base_url) + + delibs = await asyncio.gather(*[_one(m) for m in members]) + votes = {d.member_id: d.vote for d in delibs} + deliberations = [d.model_dump() for d in delibs] + decision = board.convene(req.motion, votes) + elif req.votes is not None: + try: + decision = board.convene(req.motion, dict(req.votes)) # type: ignore[arg-type] + except (ValidationError, ValueError) as exc: + raise HTTPException(status_code=422, detail=str(exc)) from exc + else: + raise HTTPException(status_code=422, detail="provide `votes` or set `use_models: true`") + + return {"decision": decision.model_dump(), "deliberations": deliberations} + + +@router.post("/api/dojo/settle") +async def api_dojo_settle(req: DojoSettleRequest) -> dict[str, Any]: + """Settle a dispute with a prime-sized dojo. Tally supplied `votes` (keyed + `judge-0..`) or `use_models` to have the judges rule (off the event loop).""" + from mindxtrain.governance import Dojo + + dojo = Dojo.sized(req.size) + if req.use_models: + from mindxtrain.governance import panel as _panel + + kw = {"base_url": req.base_url} + if req.model: + kw["default_model"] = req.model + ballot = _panel.model_judge_ballot(**kw) + verdict = await asyncio.to_thread(dojo.settle, req.motion, ballot) + elif req.votes is not None: + try: + verdict = dojo.settle(req.motion, dict(req.votes)) # type: ignore[arg-type] + except ValueError as exc: + raise HTTPException(status_code=422, detail=str(exc)) from exc + else: + raise HTTPException(status_code=422, detail="provide `votes` or set `use_models: true`") + return verdict.model_dump() + + +# ---- live training runs (SSE) ------------------------------------------- + + +class LaunchRequest(BaseModel): + recipe: str = Field(description="recipe name, e.g. qwen3_8b_sft_lora") + plan: AutotunePlan | None = None + out_dir: str | None = Field( + default=None, + description="optional override for the run output directory", + ) + + +SpawnFn = Callable[["_runs.Run", XTrainConfig, AutotunePlan], None] + + +def _real_spawn(run: _runs.Run, cfg: XTrainConfig, plan: AutotunePlan) -> None: + """Default spawn: route by backend. + + - `trl_cpu` runs in-process on a daemon thread (uses the same code + path as `/v1/training/jobs` so the Coach UI sees the same events + whether the run was kicked off via Coach or the public API). + - Everything else compiles to Axolotl YAML + streams a subprocess. + + Tests monkey-patch the module-level `_SPAWN` to bypass the real + subprocess and emit canned events instead. + """ + if cfg.train.backend in ("trl_cpu", "trl_local"): + import threading + + from mindxtrain.train.backend_trl_cpu import run_trl_cpu, run_trl_local + + # trl_local auto-detects a local GPU (else CPU fallback); trl_cpu pins CPU. + _run_inprocess = run_trl_local if cfg.train.backend == "trl_local" else run_trl_cpu + + def _on_line(line: str) -> None: + _REGISTRY.publish_threadsafe( + run.id, _runs.LogEvent(run_id=run.id, line=line, level="stdout"), + ) + + def _on_event(ev: dict[str, Any]) -> None: + """Translate trl_cpu structured logs into registry events. + + Drives Coach's Chart.js loss curve directly — same kind=step + and kind=eval contract the axolotl subprocess streamer + satisfies via stdout regex. + """ + kind = ev.get("kind") + if kind == "step": + _REGISTRY.publish_threadsafe(run.id, _runs.StepEvent( + run_id=run.id, + step=int(ev["step"]), + loss=float(ev["loss"]), + lr=ev.get("lr"), + grad_norm=ev.get("grad_norm"), + tokens_per_s=ev.get("tokens_per_s"), + # Realtime-feedback fields — drive the Coach progress + # bar + "is it learning" accuracy chart. + total_steps=ev.get("total_steps"), + mean_token_accuracy=ev.get("mean_token_accuracy"), + entropy=ev.get("entropy"), + )) + elif kind == "eval": + _REGISTRY.publish_threadsafe(run.id, _runs.EvalEvent( + run_id=run.id, + step=int(ev["step"]), + suite=str(ev.get("suite", "mid_train")), + metrics={k: float(v) for k, v in ev.get("metrics", {}).items()}, + )) + + def _thread() -> None: + _REGISTRY.publish_threadsafe( + run.id, + _runs.StatusEvent(run_id=run.id, status="running", message="cpu lane"), + ) + try: + _run_inprocess( + cfg, plan, run.out_dir, on_line=_on_line, on_event=_on_event, + ) + except Exception as exc: + _REGISTRY.publish_threadsafe( + run.id, + _runs.StatusEvent(run_id=run.id, status="failed", message=str(exc)), + ) + _REGISTRY.close_subscribers(run.id) + return + # Bind the AutotunePlan + checkpoint hashes into a verifiable + # manifest before announcing success, so a UI subscriber can fetch + # the receipt the moment it sees `succeeded`. + from mindxtrain.operator.receipt_emit import emit_run_receipt + emit_run_receipt(_REGISTRY, run, cfg, plan) + _REGISTRY.publish_threadsafe( + run.id, + _runs.StatusEvent( + run_id=run.id, status="succeeded", message="cpu lane done", + ), + ) + _REGISTRY.close_subscribers(run.id) + + threading.Thread(target=_thread, daemon=True, name=f"trl-cpu-{run.id}").start() + return + + from mindxtrain.train.sft import prepare_run + + prepared = prepare_run(cfg, plan, run.out_dir) + _runs.spawn_subprocess_streaming( + cmd=prepared.cmd, + env=prepared.env, + log_path=prepared.log_path, + run_id=run.id, + registry=_REGISTRY, + ) + + +_SPAWN: SpawnFn = _real_spawn + + +def _sse_headers() -> dict[str, str]: + return { + "Cache-Control": "no-cache", + "X-Accel-Buffering": "no", + "Connection": "keep-alive", + } + + +@router.post("/api/runs/launch", response_model=_runs.Run) +async def api_runs_launch(req: LaunchRequest) -> _runs.Run: + """Spawn a training run and return its `Run` snapshot immediately. + + Does not block on the subprocess — the spawn helper attaches a + background line-reader thread that publishes events into the registry. + """ + if req.recipe not in list_recipes(): + raise HTTPException(status_code=404, detail=f"unknown recipe {req.recipe!r}") + cfg = XTrainConfig.model_validate(yaml.safe_load(render_recipe(req.recipe))) + plan = req.plan or run_autotune(dry_run=True) + + out_dir = Path(req.out_dir) if req.out_dir else Path("./out/runs") / cfg.meta.run_name + run = _REGISTRY.create(req.recipe, out_dir) + _REGISTRY.attach_loop(asyncio.get_running_loop()) + _REGISTRY.publish(run.id, _runs.StatusEvent(run_id=run.id, status="pending", message="launching")) + + try: + _SPAWN(run, cfg, plan) + except RuntimeError as exc: + # Most common cause: `accelerate` not on PATH (no --extra ml). + # Surface as a 503 + emit a failure event so any subscriber sees it. + _REGISTRY.publish( + run.id, + _runs.StatusEvent(run_id=run.id, status="failed", message=str(exc)), + ) + _REGISTRY.close_subscribers(run.id) + raise HTTPException(status_code=503, detail=str(exc)) from exc + + # Start the per-run system-metrics sampler. For trl_cpu the trainer + # runs in-process so trainer-PID == operator-PID. Axolotl-subprocess + # would need its own PID — handled in a follow-up. + from mindxtrain.operator.coach.run_metrics import start_metrics_sampler + start_metrics_sampler(run.id, os.getpid()) + # Watchdog stops the sampler on terminal status. The reference is + # stored alongside the sampler tasks so the task survives until + # cancellation; without this binding GC could reap it mid-watch. + _METRICS_WATCHDOGS[run.id] = asyncio.create_task( + _stop_metrics_on_terminal(run.id), + name=f"metrics-watchdog-{run.id}", + ) + + snapshot = _REGISTRY.get(run.id) + assert snapshot is not None + return snapshot + + +async def _stop_metrics_on_terminal(run_id: str) -> None: + """Watchdog — stops the metrics sampler when its run hits a terminal status. + + Subscribes to the run's event stream filtered to `status` kinds and + bails on the first terminal value. If the registry is torn down or + the run vanishes, the subscribe iterator ends and the task exits + quietly. + """ + from mindxtrain.operator.coach.run_metrics import stop_metrics_sampler + + terminal = {"succeeded", "failed", "cancelled"} + try: + async for ev in _REGISTRY.subscribe(run_id, kinds=("status",)): + if isinstance(ev, _runs.StatusEvent) and ev.status in terminal: + break + except Exception: + pass + await stop_metrics_sampler(run_id) + _METRICS_WATCHDOGS.pop(run_id, None) + + +def autostart_enabled() -> bool: + """True when MINDXTRAIN_AUTOSTART opts the operator into hands-free training.""" + return os.environ.get("MINDXTRAIN_AUTOSTART", "").strip().lower() in { + "1", "true", "yes", "on", + } + + +def autostart_recipe() -> str: + """Recipe the operator auto-launches at boot (MINDXTRAIN_AUTOSTART_RECIPE).""" + return ( + os.environ.get("MINDXTRAIN_AUTOSTART_RECIPE", "").strip() + or _DEFAULT_AUTOSTART_RECIPE + ) + + +def _mindx_root() -> Path: + """Filesystem root of the mindX install (MINDXTRAIN_MINDX_ROOT, else ~/mindX).""" + explicit = os.environ.get("MINDXTRAIN_MINDX_ROOT", "").strip() + return Path(explicit) if explicit else Path.home() / "mindX" + + +def sea_decision_path() -> Path: + """Path to the SEA agent's training-recommendation file. + + The mindX StrategicEvolutionAgent writes its go/no-go verdict here; + the operator reads it to gate autonomous training. Override with + `MINDXTRAIN_SEA_DECISION`, else default under the mindX data dir. + """ + explicit = os.environ.get("MINDXTRAIN_SEA_DECISION", "").strip() + if explicit: + return Path(explicit) + return _mindx_root() / "data" / "training_recommendation.json" + + +def read_sea_decision() -> dict[str, Any] | None: + """Parse the SEA decision file. `None` when absent or unreadable/invalid.""" + try: + raw = sea_decision_path().read_text(encoding="utf-8") + except OSError: + return None + try: + data = json.loads(raw) + except (json.JSONDecodeError, ValueError): + return None + return data if isinstance(data, dict) else None + + +def sea_training_gate() -> dict[str, Any]: + """Evaluate the SEA agent's training recommendation. + + Returns a status dict consumed by both the autostart path and the + `/api/sea-decision` endpoint. `open` is True only when SEA explicitly + recommends training *and* the record is still fresh (within `ttl_s`). + A missing file means SEA has not spoken — the gate stays closed. + """ + data = read_sea_decision() + if data is None: + return { + "open": False, "available": False, "decision": None, + "reason": "no SEA decision file — autonomous training stands down", + } + reason = str(data.get("reason", "")).strip() or "no reason given" + if not bool(data.get("recommend", False)): + return { + "open": False, "available": True, "decision": data, + "reason": f"SEA decided against training: {reason}", + } + ts = data.get("ts") + ttl = data.get("ttl_s", 3600) + if isinstance(ts, (int, float)) and isinstance(ttl, (int, float)): + age = time.time() - float(ts) + if age > float(ttl): + return { + "open": False, "available": True, "decision": data, + "reason": ( + f"SEA recommendation is stale " + f"({age:.0f}s old > ttl {float(ttl):.0f}s)" + ), + } + return { + "open": True, "available": True, "decision": data, + "reason": f"SEA recommends training — {reason}", + } + + +async def autostart_cpu_training() -> _runs.Run | None: + """Launch a CPU training run at boot — autonomous, but only if SEA agrees. + + Two gates. `MINDXTRAIN_AUTOSTART` arms autonomous mode (off by + default so a `TestClient` / CI lifespan never spawns a trainer). The + mindX `StrategicEvolutionAgent`'s decision file is the actual + decider — the run launches only when SEA recommends training and the + record is fresh. SEA's chosen recipe (if any) wins; otherwise the + `MINDXTRAIN_AUTOSTART_RECIPE` default applies. Idempotent — skips + when a run is already pending/running. Returns the launched `Run`, + or `None` when a gate is closed / the launch failed (logged, never + raised). + """ + if not autostart_enabled(): + return None + gate = sea_training_gate() + if not gate["open"]: + _log.info("autostart: SEA gate closed — %s", gate["reason"]) + return None + for existing in _REGISTRY.list_runs(): + if existing.status in ("pending", "running"): + _log.info( + "autostart: run %s already %s — skipping", + existing.id, existing.status, + ) + return None + decision = gate["decision"] or {} + recipe = str(decision.get("recipe") or "").strip() or autostart_recipe() + _log.info("autostart: SEA gate OPEN — %s; launching %r", gate["reason"], recipe) + try: + run = await api_runs_launch(LaunchRequest(recipe=recipe)) + except HTTPException as exc: + _log.warning( + "autostart: launch failed (HTTP %s) — %s. The Coach UI stays " + "available; press 'Run training' to start a session manually.", + exc.status_code, exc.detail, + ) + return None + _log.info("autostart: run %s launched on SEA's recommendation", run.id) + return run + + +@router.get("/api/sea-decision") +async def api_sea_decision() -> dict[str, Any]: + """The SEA agent's current training recommendation + the gate verdict. + + The Coach UI polls this so the user can see whether autonomous + training is armed and why SEA did (or did not) recommend a run. + `open` True means a boot right now would auto-launch; the user can + always start a session by hand with the "Run training" button. + """ + gate = sea_training_gate() + gate["autostart_enabled"] = autostart_enabled() + gate["decision_path"] = str(sea_decision_path()) + return gate + + +@router.get("/api/runs", response_model=list[_runs.Run]) +async def api_runs_list() -> list[_runs.Run]: + return _REGISTRY.list_runs() + + +@router.get("/api/runs/{run_id}", response_model=_runs.Run) +async def api_run_get(run_id: str) -> _runs.Run: + snap = _REGISTRY.get(run_id) + if snap is None: + raise HTTPException(status_code=404, detail=f"unknown run {run_id!r}") + return snap + + +@router.get("/api/runs/{run_id:path}/metrics") +async def api_run_metrics(run_id: str, since: float = 0.0) -> dict[str, Any]: + """Backfill the system-metrics sparklines on tab-switch. + + Returns samples newer than `since` (unix seconds, default 0 = all + cached). 404 when the run id is unknown to the registry, [] when + the run exists but the sampler hasn't produced any samples yet + (e.g., between launch and the first 1 Hz tick). + """ + from mindxtrain.operator.coach.run_metrics import get_buffer + + if _REGISTRY.get(run_id) is None: + raise HTTPException(status_code=404, detail=f"unknown run {run_id!r}") + return {"samples": get_buffer(run_id, since=since)} + + +async def _stream(run_id: str, kinds: tuple[str, ...] | None) -> AsyncIterator[str]: + if _REGISTRY.get(run_id) is None: + # Yield a single error frame and close. + yield "event: error\ndata: {\"detail\":\"unknown run\"}\n\n" + return + async for event in _REGISTRY.subscribe(run_id, kinds=kinds): + yield _runs.format_sse(event) + + +@router.get("/api/runs/{run_id}/events") +async def api_run_events(run_id: str) -> StreamingResponse: + return StreamingResponse( + _stream(run_id, kinds=None), + media_type="text/event-stream", + headers=_sse_headers(), + ) + + +@router.get("/api/runs/{run_id}/logs") +async def api_run_logs(run_id: str) -> StreamingResponse: + return StreamingResponse( + _stream(run_id, kinds=("log",)), + media_type="text/event-stream", + headers=_sse_headers(), + ) + + +@router.post("/api/runs/{run_id}/cancel") +async def api_run_cancel(run_id: str) -> dict[str, Any]: + if _REGISTRY.get(run_id) is None: + raise HTTPException(status_code=404, detail=f"unknown run {run_id!r}") + cancelled = await _REGISTRY.cancel(run_id, grace_s=2.0) + return {"run_id": run_id, "cancelled": cancelled} + + +class PushToOllamaRequest(BaseModel): + tag: str | None = Field( + default=None, + description="Ollama tag for the new model. Defaults to the run's recipe name.", + ) + system_prompt: str | None = None + base_model: str | None = Field( + default=None, + description=( + "Override the base model name resolved from the recipe. Useful " + "when the training adapter was produced against a snapshot that " + "differs from the recipe's `model.name` field." + ), + ) + register_with_mindx: bool = Field( + default=False, + description=( + "After ollama create succeeds, PATCH the new tag into mindX as " + "the local fallback (best-effort; failure does NOT abort the push)." + ), + ) + mindx_base_url: str | None = Field( + default=None, + description="Override the mindX base URL for the fallback PATCH.", + ) + + +class PushToOllamaResponse(BaseModel): + run_id: str + tag: str + merged_dir: str + modelfile: str + mindx_fallback_swapped: bool = False + mindx_fallback_swap: dict[str, str] | None = None + message: str = "pushed" + + +@router.post( + "/api/runs/{run_id:path}/push-to-ollama", + response_model=PushToOllamaResponse, +) +async def api_run_push_to_ollama( + run_id: str, req: PushToOllamaRequest, +) -> PushToOllamaResponse: + """Merge the run's LoRA adapter into the base weights, write a + Modelfile, and call `ollama create`. Streams the push log into the + run's SSE channel so the Coach UI can replay it in the train card. + + The adapter is expected at `<run.out_dir>/checkpoint/` — the same + location both trl_cpu and the axolotl subprocess write to. + """ + snap = _REGISTRY.get(run_id) + if snap is None: + raise HTTPException(status_code=404, detail=f"unknown run {run_id!r}") + + adapter = snap.out_dir / "checkpoint" + if not adapter.exists(): + raise HTTPException( + status_code=409, + detail=( + f"no checkpoint at {adapter}; let the training run finish " + f"before pushing to ollama" + ), + ) + + # Resolve the base model from the recipe unless the caller overrode it. + if req.base_model: + base_model = req.base_model + else: + try: + cfg = XTrainConfig.model_validate( + yaml.safe_load(render_recipe(snap.recipe)), + ) + except (KeyError, ValueError) as exc: + raise HTTPException( + status_code=409, + detail=f"can't resolve base model from recipe {snap.recipe!r}: {exc}", + ) from exc + base_model = cfg.model.name + + tag = req.tag or snap.recipe + + # Bind the registry to the current loop so the threaded merge can publish + # back into it via call_soon_threadsafe. Without this, repeat requests + # under TestClient (which closes its loop after each call) hit + # "Event loop is closed" the second time around. + _REGISTRY.attach_loop(asyncio.get_running_loop()) + + def _log(line: str) -> None: + _REGISTRY.publish_threadsafe( + run_id, _runs.LogEvent(run_id=run_id, line=line, level="stdout"), + ) + + _REGISTRY.publish( + run_id, + _runs.StatusEvent( + run_id=run_id, status="running", + message=f"push-to-ollama: {base_model} + {adapter} -> {tag}", + ), + ) + + try: + from mindxtrain.deploy.ollama_push import push_to_ollama + result = await asyncio.to_thread( + push_to_ollama, + base_model=base_model, + adapter_dir=adapter, + tag=tag, + system_prompt=req.system_prompt, + sink=_log, + register_with_mindx=req.register_with_mindx, + mindx_base_url=req.mindx_base_url, + ) + except (FileNotFoundError, ImportError) as exc: + _REGISTRY.publish( + run_id, + _runs.StatusEvent(run_id=run_id, status="failed", message=str(exc)), + ) + raise HTTPException(status_code=503, detail=str(exc)) from exc + except subprocess.CalledProcessError as exc: + msg = (exc.stderr or exc.stdout or "").strip() or f"ollama create exit={exc.returncode}" + _REGISTRY.publish( + run_id, + _runs.StatusEvent(run_id=run_id, status="failed", message=msg), + ) + raise HTTPException(status_code=502, detail=msg) from exc + + _REGISTRY.publish( + run_id, + _runs.StatusEvent( + run_id=run_id, status="succeeded", message=f"pushed to ollama as {result.tag}", + ), + ) + swap = result.mindx_fallback_swap + return PushToOllamaResponse( + run_id=run_id, + tag=result.tag, + merged_dir=str(result.merged_dir), + modelfile=str(result.modelfile), + mindx_fallback_swapped=swap is not None, + mindx_fallback_swap=swap, + ) + + +@router.post("/api/runs/{run_id}/ingest") +async def api_run_ingest(run_id: str, request: Request) -> dict[str, str]: + """Loopback-only ingest used by the in-process StreamCallback.""" + host = request.client.host if request.client else None + if not _runs.is_loopback(host): + raise HTTPException(status_code=403, detail="loopback only") + if _REGISTRY.get(run_id) is None: + raise HTTPException(status_code=404, detail=f"unknown run {run_id!r}") + body = await request.json() + body["run_id"] = run_id # trust the URL, never the body + try: + # Re-validate via the discriminated union so unknown kinds 422 cleanly. + from pydantic import TypeAdapter + + ta = TypeAdapter(_runs.TrainEvent) + event = ta.validate_python(body) + except ValidationError as exc: + raise HTTPException(status_code=422, detail=exc.errors()) from exc + _REGISTRY.publish(run_id, event) + return {"status": "ok"} + + +# ---- deploy: github push + droplet sync/provision ----------------------- + +_GITHUB_PUSH_RECIPE = "_github_push" +_DROPLET_SYNC_RECIPE = "_droplet_sync" +_DROPLET_PROVISION_RECIPE = "_droplet_provision" +_DEPLOY_BUSY_RECIPES = frozenset({_DROPLET_SYNC_RECIPE, _DROPLET_PROVISION_RECIPE}) + + +class DeployStatus(BaseModel): + """Returned by /api/{github,droplet}/status to drive the UI's enabled state.""" + + configured: bool + missing: list[str] + target: str + + +class GithubPushRequest(BaseModel): + commit_message: str = "mindXtrain initial push" + force: bool = False + + +class DropletSyncRequest(BaseModel): + run_bench: bool = True + fetch_plan: bool = True + + +class DropletProvisionRequest(BaseModel): + name: str = "mindxtrain" + repo: str | None = None + branch: str | None = None + container: str | None = None + extras: str = "ml,eval,data,obs" + wait_for_bootstrap: bool = True + recipe: str | None = Field( + default=None, + description=( + "Built-in recipe to run on the droplet via `mindxtrain train` " + "after cloud-init bench. When set, the operator SSH-tails the " + "training log and bridges per-step events into this run's SSE " + "stream so the Coach Train card populates live." + ), + ) + + +# Spawn shims — same _SPAWN injection pattern used by training. Tests +# monkey-patch these to bypass real subprocess execution. +GithubSpawnFn = Callable[["_runs.Run", GithubPushRequest], None] +DropletSyncSpawnFn = Callable[["_runs.Run", DropletSyncRequest], None] +DropletProvisionSpawnFn = Callable[["_runs.Run", DropletProvisionRequest], None] + + +def _real_github_spawn(run: _runs.Run, req: GithubPushRequest) -> None: + cfg = _gh.GithubConfig( + token=os.environ["GITHUB_TOKEN"], + repo=os.environ["GITHUB_REPO"], + branch=os.environ.get("GITHUB_DEFAULT_BRANCH", "main"), + author_name=os.environ.get("GITHUB_AUTHOR_NAME", "mindXtrain bot"), + author_email=os.environ.get("GITHUB_AUTHOR_EMAIL", "noreply@pythai.net"), + ) + github_push_pipeline( + cfg, + run_id=run.id, + out_dir=run.out_dir, + commit_message=req.commit_message, + force=req.force, + registry=_REGISTRY, + ) + + +def _real_droplet_sync_spawn(run: _runs.Run, req: DropletSyncRequest) -> None: + cfg = _droplet_mod.from_env() + droplet_sync_pipeline( + cfg, + repo_root=Path.cwd(), + run_id=run.id, + out_dir=run.out_dir, + run_bench=req.run_bench, + fetch_plan=req.fetch_plan, + registry=_REGISTRY, + ) + + +def _real_droplet_provision_spawn(run: _runs.Run, req: DropletProvisionRequest) -> None: + cloud_cfg = _adc.from_env() + droplet_provision_pipeline( + cloud_cfg, + name=req.name, + repo=req.repo or os.environ.get("GITHUB_REPO", "professor-codephreak/mindXtrain"), + branch=req.branch or os.environ.get("GITHUB_DEFAULT_BRANCH", "main"), + container=req.container or os.environ.get("DROPLET_CONTAINER", "rocm/primus:v26.2"), + extras=req.extras, + run_id=run.id, + out_dir=run.out_dir, + wait_for_bootstrap=req.wait_for_bootstrap, + recipe=req.recipe, + registry=_REGISTRY, + ) + + +_GITHUB_SPAWN: GithubSpawnFn = _real_github_spawn +_DROPLET_SYNC_SPAWN: DropletSyncSpawnFn = _real_droplet_sync_spawn +_DROPLET_PROVISION_SPAWN: DropletProvisionSpawnFn = _real_droplet_provision_spawn + + +def _bootstrap_run(recipe: str) -> _runs.Run: + out_dir = Path("./out/deploy") / recipe.lstrip("_") + run = _REGISTRY.create(recipe, out_dir / "pending") # path is rewritten below + final_out = Path("./out/deploy") / recipe.lstrip("_") / run.id + final_out.mkdir(parents=True, exist_ok=True) + _REGISTRY._update(run.id, out_dir=final_out) + _REGISTRY.attach_loop(asyncio.get_running_loop()) + _REGISTRY.publish(run.id, _runs.StatusEvent(run_id=run.id, status="pending", message="launching")) + snap = _REGISTRY.get(run.id) + assert snap is not None + return snap + + +def _busy_deploy_run() -> _runs.Run | None: + """Return the first in-flight deploy run, or None.""" + busy: set[_runs.RunStatus] = {"pending", "running"} + for run in _REGISTRY.list_runs(): + if run.recipe in _DEPLOY_BUSY_RECIPES and run.status in busy: + return run + return None + + +def _fail_run(run: _runs.Run, message: str) -> None: + _REGISTRY.publish(run.id, _runs.StatusEvent(run_id=run.id, status="failed", message=message)) + _REGISTRY.close_subscribers(run.id) + + +# -- /api/github/status + /api/github/push -------------------------------- + + +@router.get("/api/github/status", response_model=DeployStatus) +async def api_github_status() -> DeployStatus: + missing = _gh.status_missing() + return DeployStatus( + configured=not missing, + missing=missing, + target=_gh.status_target(), + ) + + +@router.post("/api/github/push", response_model=_runs.Run) +async def api_github_push(req: GithubPushRequest) -> _runs.Run: + missing = _gh.status_missing() + if missing: + raise HTTPException( + status_code=503, + detail={"error": "github push not configured", "missing": missing}, + ) + run = _bootstrap_run(_GITHUB_PUSH_RECIPE) + try: + _GITHUB_SPAWN(run, req) + except Exception as exc: + _fail_run(run, str(exc)) + raise HTTPException(status_code=503, detail=str(exc)) from exc + snap = _REGISTRY.get(run.id) + assert snap is not None + return snap + + +# -- /api/droplet/{status,sync,provision,list} ---------------------------- + + +@router.get("/api/droplet/status", response_model=dict) +async def api_droplet_status() -> dict[str, Any]: + """Both modes' configured-ness in one payload — UI uses each independently.""" + sync_missing = _droplet_mod.status_missing() + provision_missing = _adc.missing_env() + return { + "sync": DeployStatus( + configured=not sync_missing, + missing=sync_missing, + target=_droplet_mod.status_target(), + ).model_dump(), + "provision": DeployStatus( + configured=not provision_missing, + missing=provision_missing, + target=_adc.status_target(), + ).model_dump(), + } + + +@router.post("/api/droplet/sync", response_model=_runs.Run) +async def api_droplet_sync(req: DropletSyncRequest) -> _runs.Run: + busy = _busy_deploy_run() + if busy is not None: + raise HTTPException(status_code=409, detail={ + "error": "another deploy run is in progress", + "active_run_id": busy.id, + "active_recipe": busy.recipe, + }) + missing = _droplet_mod.status_missing() + if missing: + raise HTTPException(status_code=503, detail={ + "error": "droplet sync not configured", + "missing": missing, + }) + run = _bootstrap_run(_DROPLET_SYNC_RECIPE) + try: + _DROPLET_SYNC_SPAWN(run, req) + except Exception as exc: + _fail_run(run, str(exc)) + raise HTTPException(status_code=503, detail=str(exc)) from exc + snap = _REGISTRY.get(run.id) + assert snap is not None + return snap + + +@router.post("/api/droplet/provision", response_model=_runs.Run) +async def api_droplet_provision(req: DropletProvisionRequest) -> _runs.Run: + busy = _busy_deploy_run() + if busy is not None: + raise HTTPException(status_code=409, detail={ + "error": "another deploy run is in progress", + "active_run_id": busy.id, + "active_recipe": busy.recipe, + }) + missing = _adc.missing_env() + if missing: + raise HTTPException(status_code=503, detail={ + "error": "AMD Dev Cloud provision not configured", + "missing": missing, + }) + if req.recipe is not None and req.recipe not in list_recipes(): + raise HTTPException( + status_code=404, + detail=f"unknown recipe {req.recipe!r}", + ) + run = _bootstrap_run(_DROPLET_PROVISION_RECIPE) + try: + _DROPLET_PROVISION_SPAWN(run, req) + except Exception as exc: + _fail_run(run, str(exc)) + raise HTTPException(status_code=503, detail=str(exc)) from exc + snap = _REGISTRY.get(run.id) + assert snap is not None + return snap + + +@router.get("/api/droplet/list", response_model=list[dict]) +async def api_droplet_list(name: str | None = None) -> list[dict[str, Any]]: + """Proxy `GET /v2/droplets` (optionally filtered by name).""" + missing = _adc.missing_env() + if missing: + raise HTTPException(status_code=503, detail={ + "error": "AMD Dev Cloud not configured", + "missing": missing, + }) + cfg = _adc.from_env() + with _adc.AmdDevCloudClient(cfg) as client: + return client.list(name=name) + + +# ---- hardware diagnostics ------------------------------------------------ + + +@router.get("/api/diagnostics/hardware") +async def api_diagnostics_hardware() -> dict[str, Any]: + """Return a CPU/AMD/NVIDIA hardware profile + recommended training lane. + + The probes shell out to `rocm-smi` / `nvidia-smi` when available. + Each probe has a short timeout so a hung driver tool can't stall the + Coach UI. The composite profile is JSON-stable: the UI can poll this + repeatedly to refresh hardware state (e.g., after `rocm` installs). + """ + from mindxtrain.operator.coach.hw_diagnostics import probe_all + + profile = probe_all() + return profile.model_dump() + + +@router.get("/api/diagnostics/live") +async def api_diagnostics_live() -> dict[str, Any]: + """Cheap (~ms) live sample of host pressure + operator process state. + + Backbone of the Advanced Admin card. Returns load avgs, RAM%, disk%, + and the operator's own RSS + thread count. The UI polls this at 1-2 Hz + while the admin card is visible. + """ + from mindxtrain.operator.coach.hw_diagnostics import probe_live_metrics + + return probe_live_metrics().model_dump() + + +@router.get("/api/diagnostics/chronos") +async def api_diagnostics_chronos() -> dict[str, Any]: + """Aggregate chronos.agent state for the UI's promised-time card. + + Calls mindX's `/v1/oracle/{time,anchors,drift}` and merges the + responses into one payload. Degrades to `consensus: unavailable` + when mindX is unreachable so the UI never blanks out. + """ + from mindxtrain.operator.coach import chronos_client + + promised = await chronos_client.now() + anchors_resp = await chronos_client.anchors(limit=100) + drift_resp = await chronos_client.drift(hours=24) + return { + "promised_time": promised, + "anchors": anchors_resp.get("anchors", []), + "anchor_count": anchors_resp.get("n", 0), + "drift_history": drift_resp, + } + + +@router.get("/api/diagnostics/measurement-confidence") +async def api_diagnostics_measurement_confidence() -> dict[str, Any]: + """psutil vs `ps -A` cross-check — flags container/cgroup bias. + + `confidence_band` is the headline: `tight` < 5pp / 100 MB, + `loose` < 15pp / 500 MB, `divergent` otherwise, `unknown` when + either source isn't available. + """ + from mindxtrain.operator.coach.cli_diagnostics import measurement_confidence + + return measurement_confidence() + + +@router.get("/api/diagnostics/cli-samplers") +async def api_diagnostics_cli_samplers() -> dict[str, Any]: + """All six Linux terminal samplers in one shot.""" + from mindxtrain.operator.coach.cli_diagnostics import run_samplers + + return run_samplers() + + +@router.get("/api/diagnostics/runs", response_model=list[_runs.Run]) +async def api_diagnostics_runs() -> list[_runs.Run]: + """Snapshot of every run the registry currently knows about. + + Newest first. Powers the admin card's "Active runs" panel — the user + can see at a glance what's training, what's deploying, and which + runs have terminated. Same data as `/api/runs` (returned all-runs + rather than filtered) but lives under /api/diagnostics/* for + discoverability.""" + rows = _REGISTRY.list_runs() + # Newest first by created_at. + rows.sort(key=lambda r: r.created_at, reverse=True) + return rows + + +# ---- MEI (mindX Efficiency Index) endpoints ----------------------------- +# Surface the score layer for the Coach UI. The score itself is computed +# in `mindxtrain.eval.mei.score`; this layer reads the history ledger and +# exposes promotion gating to the operator. + + +class MEIHistoryRow(BaseModel): + """Compact row for the Coach's history list. Maps a HistoryEntry to a + flat shape the JS renderer can consume without nested unwrapping.""" + + model_config = ConfigDict(extra="forbid") + + timestamp: str + run_id: str + model_id: str + promoted: bool + composite: float + quality: float + decode_throughput: float + prefill_throughput: float + memory: float + energy: float + mab_provisional: bool + + +class MEIScoreView(BaseModel): + """Full score plus promotion preview for one run.""" + + model_config = ConfigDict(extra="forbid") + + run_id: str + model_id: str + composite: float + quality: float + decode_throughput: float + prefill_throughput: float + memory: float + energy: float + quality_bands: dict[str, float] + mab_provisional: bool + notes: list[str] + promotable: bool + promotion_reasons: list[str] + + +class MEIPromoteResponse(BaseModel): + model_config = ConfigDict(extra="forbid") + + run_id: str + promoted: bool + reasons: list[str] = Field( + default_factory=list, + description="When promoted=False, the failing-gate reasons.", + ) + + +def _history_row_from_entry(entry: Any) -> MEIHistoryRow: + s = entry.score + return MEIHistoryRow( + timestamp=entry.timestamp, + run_id=entry.run_id, + model_id=entry.model_id, + promoted=entry.promoted, + composite=s.composite, + quality=s.quality, + decode_throughput=s.decode_throughput, + prefill_throughput=s.prefill_throughput, + memory=s.memory, + energy=s.energy, + mab_provisional=s.mab_provisional, + ) + + +@router.get("/api/mei/history", response_model=list[MEIHistoryRow]) +async def api_mei_history(last: int = 20) -> list[MEIHistoryRow]: + """Return the last N MEI history entries, newest-first. + + Empty list when no scores exist yet. The Coach UI renders this as the + "Recent MEI scores" mini-list under the MEI card. + """ + from mindxtrain.eval.mei import history as _mei_history + + rows = _mei_history.read_all() + if last > 0: + rows = rows[-last:] + return [_history_row_from_entry(e) for e in reversed(rows)] + + +@router.get("/api/mei/score/{run_id:path}", response_model=MEIScoreView) +async def api_mei_score(run_id: str) -> MEIScoreView: + """Return the most recent MEIScore for `run_id`, plus promotability. + + The run-id is the registry id used by the training pipeline. Returns + 404 when no score has been recorded for that run yet (the operator + can rerun `mindxtrain mei score` against the run's record to populate). + """ + from mindxtrain.eval.mei import history as _mei_history + from mindxtrain.eval.mei.score import is_promotable + + entries = [e for e in _mei_history.read_all() if e.run_id == run_id] + if not entries: + raise HTTPException( + status_code=404, + detail=f"no MEI score recorded for run_id={run_id!r}", + ) + # Most-recent (file-order last) wins when a run was scored multiple times. + entry = entries[-1] + prior = _mei_history.currently_promoted() + prior_score = prior.score if prior is not None and prior.run_id != run_id else None + ok, reasons = is_promotable(entry.score, prior_promoted=prior_score) + sc = entry.score + return MEIScoreView( + run_id=entry.run_id, + model_id=entry.model_id, + composite=sc.composite, + quality=sc.quality, + decode_throughput=sc.decode_throughput, + prefill_throughput=sc.prefill_throughput, + memory=sc.memory, + energy=sc.energy, + quality_bands=dict(sc.quality_bands), + mab_provisional=sc.mab_provisional, + notes=list(sc.notes), + promotable=ok, + promotion_reasons=reasons, + ) + + +@router.post("/api/mei/promote/{run_id:path}", response_model=MEIPromoteResponse) +async def api_mei_promote(run_id: str) -> MEIPromoteResponse: + """Promote `run_id` to AgenticPlace if all §8 gates pass. + + Idempotent in the sense that repeated promotion of the same run only + appends new history entries (each with promoted=True). The currently- + promoted entry is whatever the last `promoted=True` row says — append- + only ledger semantics. + """ + from mindxtrain.eval.mei import history as _mei_history + from mindxtrain.eval.mei.score import is_promotable + + entries = [e for e in _mei_history.read_all() if e.run_id == run_id] + if not entries: + raise HTTPException( + status_code=404, + detail=f"no MEI score recorded for run_id={run_id!r}", + ) + entry = entries[-1] + prior = _mei_history.currently_promoted() + prior_score = prior.score if prior is not None and prior.run_id != run_id else None + ok, reasons = is_promotable(entry.score, prior_promoted=prior_score) + if not ok: + return MEIPromoteResponse(run_id=run_id, promoted=False, reasons=reasons) + _mei_history.append( + entry.score, + run_id=entry.run_id, + model_id=entry.model_id, + model_sha256=entry.model_sha256, + promoted=True, + ) + return MEIPromoteResponse(run_id=run_id, promoted=True, reasons=[]) + + +# ---- Verifiable training receipt ---------------------------------------- +# The AOT-only discipline is a verification primitive: a frozen AutotunePlan +# hash bound to the checkpoint hash proves which compiled backend/heuristic/ +# RCCL config produced these weights (cf. Verde/RepOps bitwise reproducibility). +# This layer re-verifies the manifest emitted at run completion and surfaces a +# "verified ✓" badge in the Coach UI. + + +class ReceiptHashesView(BaseModel): + """Flat BLAKE3 hashes for the Coach receipt card. Empty string = artifact + not produced by this run (e.g. a CPU run has no dataset/eval JSON).""" + + model_config = ConfigDict(extra="forbid", frozen=True) + + config_yaml: str + checkpoint: str + autotune_plan: str + dataset: str + eval_json: str + + +class ReceiptView(BaseModel): + """Re-verified receipt for one run, consumed directly by coach.js.""" + + model_config = ConfigDict(extra="forbid", frozen=True) + + run_id: str + base_model: str + git_sha: str + created_at: str + hashes: ReceiptHashesView + verified: bool + checks: dict[str, bool] + + +@router.get("/api/receipt/{run_id:path}", response_model=ReceiptView) +async def api_receipt(run_id: str) -> ReceiptView: + """Re-verify the manifest emitted at run completion. + + 404 when the run id is unknown; 409 when the run exists but hasn't produced + a manifest yet (still training, or it failed before the receipt was sealed). + """ + from mindxtrain.provenance.manifest import Manifest + from mindxtrain.provenance.verify import verify_receipt + + snap = _REGISTRY.get(run_id) + if snap is None: + raise HTTPException(status_code=404, detail=f"unknown run {run_id!r}") + + run_dir = Path(snap.out_dir) + manifest_path = run_dir / "manifest.json" + if not manifest_path.is_file(): + raise HTTPException( + status_code=409, + detail="no receipt yet for this run (run is still training or failed)", + ) + + manifest = Manifest.model_validate_json(manifest_path.read_text()) + plan_path = run_dir / "autotune_plan.json" + plan_json = plan_path.read_bytes() if plan_path.is_file() else None + config_snapshot = run_dir / "config.snapshot.yaml" + + try: + checks = verify_receipt( + manifest, + config_yaml_path=config_snapshot, + dataset_manifest_path=run_dir / "dataset_manifest.json", + checkpoint_dir=run_dir / "checkpoint", + eval_json_path=run_dir / "eval" / "lm_eval.json", + plan_json=plan_json, + ) + except (FileNotFoundError, NotADirectoryError): + # A required artifact (config snapshot or checkpoint dir) vanished after + # the receipt was written — report unverified rather than 500. + checks = { + "config_yaml": False, + "checkpoint": False, + "dataset": False, + "eval_json": False, + "autotune_plan": False, + } + + return ReceiptView( + run_id=manifest.run_id, + base_model=manifest.base_model, + git_sha=manifest.git_sha, + created_at=manifest.created_at.isoformat(), + hashes=ReceiptHashesView( + config_yaml=manifest.blake3.config_yaml, + checkpoint=manifest.blake3.checkpoint, + autotune_plan=manifest.blake3.autotune_plan, + dataset=manifest.blake3.dataset, + eval_json=manifest.blake3.eval_json, + ), + verified=all(checks.values()), + checks=checks, + ) diff --git a/mindxtrain/operator/coach/chronos_client.py b/mindxtrain/operator/coach/chronos_client.py new file mode 100644 index 0000000000000000000000000000000000000000..ac3dbf49f0d7d992447e97ff8e6aa719c9681bbd --- /dev/null +++ b/mindxtrain/operator/coach/chronos_client.py @@ -0,0 +1,122 @@ +"""Coach client for mindX's chronos.agent HTTP surface. + +Wraps `GET /v1/oracle/{time,anchors,drift}` on the mindX backend. The +purpose is to let Coach (and provenance manifests) stamp artefacts with +mindX's *promised time* — a number that comes with a measured +confidence interval instead of raw `time.time()`. + +Degrades gracefully: if mindX is unreachable, every method returns a +shape consumers can still render (with `consensus: "unavailable"`) so +the UI never blanks out and manifests can fall back to local time +with `attested: false`. +""" + +from __future__ import annotations + +import logging +import os +from typing import Any + +import httpx + +logger = logging.getLogger("mindxtrain.chronos_client") + +_DEFAULT_BASE_URL = "http://localhost:8000" +_DEFAULT_TIMEOUT_S = 2.0 + + +def _base_url(override: str | None) -> str: + if override: + return override.rstrip("/") + return os.environ.get("MINDX_BASE_URL", _DEFAULT_BASE_URL).rstrip("/") + + +async def now( + *, + base_url: str | None = None, + timeout_s: float = _DEFAULT_TIMEOUT_S, +) -> dict[str, Any]: + """Fetch chronos.agent's `PromisedTime`. + + Always returns a dict with the same keys as the PromisedTime + contract (`unix_18dp`, `utc`, `consensus`, `confidence_ms`, + `sources`, `anchor_count_24h`, `promised_by`). On any failure the + dict carries `consensus: "unavailable"` and the rest of the + fields populated with safe defaults so callers don't have to + branch. + """ + url = f"{_base_url(base_url)}/v1/oracle/time" + try: + async with httpx.AsyncClient(timeout=timeout_s) as client: + resp = await client.get(url) + resp.raise_for_status() + body = resp.json() + except (httpx.HTTPError, OSError, ValueError) as exc: + logger.debug("chronos /time unreachable: %r", exc) + return _unavailable_promised_time(str(exc)) + # Be defensive about partial responses — mindX upstream might + # change shape slightly. Fill any missing keys. + body.setdefault("consensus", "unavailable") + body.setdefault("confidence_ms", 0.0) + body.setdefault("sources", {}) + body.setdefault("anchor_count_24h", 0) + body.setdefault("promised_by", "chronos.agent") + body.setdefault("unix_18dp", "") + body.setdefault("utc", "") + return body + + +async def anchors( + *, + limit: int = 100, + base_url: str | None = None, + timeout_s: float = _DEFAULT_TIMEOUT_S, +) -> dict[str, Any]: + """Recent transaction-time anchors. Returns `{anchors: [...], n: int}`.""" + url = f"{_base_url(base_url)}/v1/oracle/anchors" + try: + async with httpx.AsyncClient(timeout=timeout_s) as client: + resp = await client.get(url, params={"limit": int(limit)}) + resp.raise_for_status() + return resp.json() + except (httpx.HTTPError, OSError, ValueError) as exc: + logger.debug("chronos /anchors unreachable: %r", exc) + return {"anchors": [], "n": 0, "error": str(exc)} + + +async def drift( + *, + hours: int = 24, + base_url: str | None = None, + timeout_s: float = _DEFAULT_TIMEOUT_S, +) -> dict[str, Any]: + """Bucketed drift history. Returns the DriftHistory dict shape.""" + url = f"{_base_url(base_url)}/v1/oracle/drift" + try: + async with httpx.AsyncClient(timeout=timeout_s) as client: + resp = await client.get(url, params={"hours": int(hours)}) + resp.raise_for_status() + return resp.json() + except (httpx.HTTPError, OSError, ValueError) as exc: + logger.debug("chronos /drift unreachable: %r", exc) + return { + "hours": hours, "bucket_count": 0, "buckets": [], + "drift_std_ms": 0.0, "drift_max_abs_ms": 0.0, + "anchor_count": 0, "error": str(exc), + } + + +def _unavailable_promised_time(error: str) -> dict[str, Any]: + """The "unavailable" PromisedTime shape — keep keys aligned with the live one.""" + return { + "unix_18dp": "", + "utc": "", + "consensus": "unavailable", + "confidence_ms": 0.0, + "sources": {"error": error}, + "anchor_count_24h": 0, + "promised_by": "chronos.agent", + } + + +__all__ = ["anchors", "drift", "now"] diff --git a/mindxtrain/operator/coach/cli_diagnostics.py b/mindxtrain/operator/coach/cli_diagnostics.py new file mode 100644 index 0000000000000000000000000000000000000000..1de722ae2c7f4e28caa7b11406d71066a8c132aa --- /dev/null +++ b/mindxtrain/operator/coach/cli_diagnostics.py @@ -0,0 +1,160 @@ +"""Linux terminal sampler integration + psutil-vs-ps cross-validation. + +The user's framing: + optimized using linux terminal commands when possible for minimum + and efficiency in time. + +This module is Coach's surface for the samplers defined in mindX's +`utils/cli_time_samplers.py`. It also adds a *measurement confidence* +helper that compares psutil's view of the host (the existing +`hw_diagnostics.py` path) against `ps -A`'s view. A wide divergence +flags container/cgroup/sandbox bias — exactly the failure mode that +makes raw psutil numbers misleading. + +Falls back to a `"degraded"` payload when the mindX samplers module +can't be imported (e.g., Coach running standalone without mindX +checked out) so the endpoint still returns shape-stable data. +""" + +from __future__ import annotations + +import logging +import os +import sys +from pathlib import Path +from typing import Any + +logger = logging.getLogger("mindxtrain.cli_diagnostics") + +_MINDX_ROOT = Path(os.environ.get("MINDX_ROOT", "/home/hacker/mindX")) + + +def _load_samplers() -> Any: + """Import mindX's cli_time_samplers without dragging in agents/__init__.py. + + The chronos plan put the samplers in `mindX/utils/cli_time_samplers.py`; + Coach lives in a different project, so we add the mindX root to + sys.path on first call and import directly. Cached at the module + level via the import system. + """ + samplers_path = _MINDX_ROOT / "utils" / "cli_time_samplers.py" + if not samplers_path.exists(): + return None + parent = str(_MINDX_ROOT) + if parent not in sys.path: + sys.path.insert(0, parent) + try: + from utils import cli_time_samplers # type: ignore + return cli_time_samplers + except ImportError as exc: + logger.debug("cli_time_samplers unimportable: %r", exc) + return None + + +def run_samplers() -> dict[str, Any]: + """Run every sampler defined in `utils/cli_time_samplers.py`. + + Returns `{ok: bool, supported: bool, samplers: {name -> record}}` so + callers can branch on whether the mindX samplers are reachable + without rummaging through individual records. + """ + mod = _load_samplers() + if mod is None: + return { + "ok": False, + "supported": False, + "reason": "cli_time_samplers.py not found under MINDX_ROOT", + "samplers": {}, + } + try: + return { + "ok": True, + "supported": mod.is_supported(), + "samplers": mod.sample_all(), + } + except Exception as exc: + logger.warning("sample_all failed: %r", exc) + return { + "ok": False, "supported": False, + "reason": f"sample_all: {exc!r}", "samplers": {}, + } + + +# ---- psutil vs ps cross-check ------------------------------------------- + + +def _psutil_totals() -> dict[str, Any]: + """Whole-system CPU% + total RSS via psutil (existing primitive).""" + try: + import psutil # type: ignore + except ImportError: + return {"ok": False, "reason": "psutil not installed"} + # cpu_percent(interval=None) is non-blocking; reflects load since last call. + cpu = psutil.cpu_percent(interval=None) + vm = psutil.virtual_memory() + rss_used_mb = (vm.total - vm.available) / (1024 * 1024) + return {"ok": True, "cpu_pct": float(cpu), "rss_used_mb": float(rss_used_mb)} + + +def _ps_totals(samplers_mod: Any) -> dict[str, Any]: + """Whole-system CPU% + total RSS via `ps -A` (proc_snapshot sampler).""" + if samplers_mod is None: + return {"ok": False, "reason": "samplers unavailable"} + rec = samplers_mod.proc_snapshot() + if not rec["ok"]: + return {"ok": False, "reason": rec.get("error", "proc_snapshot failed")} + val = rec["value"] + return { + "ok": True, + "cpu_pct": float(val["total_cpu_pct"]), + "rss_used_mb": float(val["total_rss_kb"]) / 1024.0, + "duration_us": rec["duration_us"], + } + + +def _classify_band(cpu_delta_pp: float, rss_delta_mb: float) -> str: + """Translate raw deltas into a tight | loose | divergent label. + + Thresholds picked to flag the container/cgroup measurement-bias + case: psutil sees cgroup-clipped values while `ps` sees whole- + system. A `divergent` band is the actionable signal — Coach should + surface it red. + """ + if cpu_delta_pp < 5 and rss_delta_mb < 100: + return "tight" + if cpu_delta_pp < 15 and rss_delta_mb < 500: + return "loose" + return "divergent" + + +def measurement_confidence() -> dict[str, Any]: + """Compare psutil's view to `ps -A`'s view. Returns delta + band.""" + samplers_mod = _load_samplers() + psu = _psutil_totals() + ps = _ps_totals(samplers_mod) + + # If either source is missing, we can't compute a delta. Report + # honestly so the UI shows a "?" rather than fabricating a band. + if not (psu.get("ok") and ps.get("ok")): + return { + "ok": False, + "psutil": psu, + "ps": ps, + "confidence_band": "unknown", + } + + cpu_delta_pp = abs(psu["cpu_pct"] - ps["cpu_pct"]) + rss_delta_mb = abs(psu["rss_used_mb"] - ps["rss_used_mb"]) + return { + "ok": True, + "psutil_cpu_pct": round(psu["cpu_pct"], 2), + "ps_cpu_pct": round(ps["cpu_pct"], 2), + "cpu_delta_pp": round(cpu_delta_pp, 2), + "psutil_rss_mb": round(psu["rss_used_mb"], 1), + "ps_rss_mb": round(ps["rss_used_mb"], 1), + "rss_delta_mb": round(rss_delta_mb, 1), + "confidence_band": _classify_band(cpu_delta_pp, rss_delta_mb), + } + + +__all__ = ["measurement_confidence", "run_samplers"] diff --git a/mindxtrain/operator/coach/hw_diagnostics.py b/mindxtrain/operator/coach/hw_diagnostics.py new file mode 100644 index 0000000000000000000000000000000000000000..30d794749242c5fff2095265bec7edcbfb677f1d --- /dev/null +++ b/mindxtrain/operator/coach/hw_diagnostics.py @@ -0,0 +1,500 @@ +"""Hardware diagnostics for the Coach UI — CPU / AMD / NVIDIA. + +Surfaces what compute the operator actually has available so the user can +pick the right training lane: + +- **CPU**: model name (Ryzen / EPYC / Intel detection), physical cores, + current 1-minute load, total RAM, available RAM. +- **AMD GPU**: rocm-smi probe; reports each GPU's VRAM + driver if rocm + is installed; clean "unavailable" otherwise. +- **NVIDIA GPU**: nvidia-smi probe; reports each GPU's VRAM + driver if + the driver is installed; clean "unavailable" otherwise. + +Pure stdlib + subprocess. Lazy: importing the module is cheap; probing +is a function call. Every probe has a short timeout so a misbehaving +driver tool doesn't hang Coach. +""" +from __future__ import annotations + +import json +import os +import re +import shutil +import subprocess +from pathlib import Path + +from pydantic import BaseModel, ConfigDict, Field + +_PROBE_TIMEOUT_S = 4.0 + + +# ---- CPU -------------------------------------------------------------------- + + +class CPUInfo(BaseModel): + """What `Hardware available → CPU` reports.""" + + model_config = ConfigDict(extra="forbid") + + available: bool = True + model_name: str = "" + vendor: str = Field( + default="", + description="Coarse vendor tag: 'amd' / 'intel' / 'arm' / '' for unknown.", + ) + is_ryzen: bool = Field( + default=False, + description="True for AMD Ryzen / EPYC / Threadripper (CCX-aware OMP affinity helps).", + ) + cores: int = 0 + threads: int = 0 + load_avg_1m: float | None = None + ram_total_gb: float = 0.0 + ram_available_gb: float = 0.0 + note: str | None = None + + +def _parse_cpuinfo() -> tuple[str, str]: + """Return (model_name, vendor_tag) from /proc/cpuinfo. + + On non-Linux hosts /proc/cpuinfo is absent; returns empty strings so + the caller falls back to a generic 'CPU' label. + """ + path = Path("/proc/cpuinfo") + if not path.exists(): + return "", "" + try: + text = path.read_text() + except OSError: + return "", "" + model = "" + vendor = "" + for line in text.splitlines(): + if line.startswith("model name") and ":" in line and not model: + model = line.split(":", 1)[1].strip() + elif line.startswith("vendor_id") and ":" in line and not vendor: + vendor = line.split(":", 1)[1].strip() + if model and vendor: + break + return model, vendor + + +def _parse_meminfo() -> tuple[float, float]: + """Return (total_gb, available_gb) from /proc/meminfo, (0, 0) on failure.""" + path = Path("/proc/meminfo") + if not path.exists(): + return 0.0, 0.0 + try: + text = path.read_text() + except OSError: + return 0.0, 0.0 + total_kb = 0 + avail_kb = 0 + for line in text.splitlines(): + if line.startswith("MemTotal:"): + total_kb = int(line.split()[1]) + elif line.startswith("MemAvailable:"): + avail_kb = int(line.split()[1]) + return total_kb / (1024 * 1024), avail_kb / (1024 * 1024) + + +def probe_cpu() -> CPUInfo: + """Build a CPUInfo for the current host.""" + cores = os.cpu_count() or 1 + model, vendor_raw = _parse_cpuinfo() + vendor = "" + if "AMD" in vendor_raw or "AMD" in model or "AuthenticAMD" in vendor_raw: + vendor = "amd" + elif "Intel" in vendor_raw or "Intel" in model or "GenuineIntel" in vendor_raw: + vendor = "intel" + elif "ARM" in vendor_raw or "ARM" in model: + vendor = "arm" + is_ryzen = bool( + vendor == "amd" + and re.search(r"\b(Ryzen|EPYC|Threadripper)\b", model, re.IGNORECASE), + ) + + load_1m: float | None + try: + load_1m = os.getloadavg()[0] + except (OSError, AttributeError): + load_1m = None + + total_gb, avail_gb = _parse_meminfo() + + note: str | None = None + if not model: + note = "/proc/cpuinfo unavailable — CPU model name not detectable." + elif vendor == "amd" and not is_ryzen: + note = ( + "AMD CPU detected but not the Ryzen / EPYC family. CCX-aware " + "OMP affinity is still safe to enable; no harm if ignored." + ) + + return CPUInfo( + available=True, + model_name=model or "CPU", + vendor=vendor, + is_ryzen=is_ryzen, + cores=cores, + threads=cores, # logical = physical * SMT; /proc/cpuinfo gives logical + load_avg_1m=load_1m, + ram_total_gb=round(total_gb, 2), + ram_available_gb=round(avail_gb, 2), + note=note, + ) + + +# ---- AMD GPU ---------------------------------------------------------------- + + +class AMDGPU(BaseModel): + model_config = ConfigDict(extra="forbid") + + name: str + vram_gb: float + driver_version: str = "" + + +class AMDInfo(BaseModel): + model_config = ConfigDict(extra="forbid") + + available: bool = False + gpus: list[AMDGPU] = Field(default_factory=list) + rocm_version: str = "" + note: str | None = None + + +def _which(cmd: str) -> str | None: + return shutil.which(cmd) + + +def probe_amd() -> AMDInfo: + """Probe AMD GPUs via rocm-smi. + + Detects whether ROCm is installed and what GPUs it sees. Clean + "unavailable" return when rocm-smi isn't on PATH (the common case on + a CPU-only laptop). + """ + rocm_smi = _which("rocm-smi") + if rocm_smi is None: + return AMDInfo(available=False, note="rocm-smi not found on PATH") + try: + # `rocm-smi --showid --showmeminfo vram --showdriverversion --json` + # is the JSON entrypoint; some rocm versions don't accept --json + # so we fall back to the human-readable parser below if needed. + result = subprocess.run( + [rocm_smi, "--showid", "--showmeminfo", "vram", + "--showdriverversion", "--json"], + capture_output=True, text=True, timeout=_PROBE_TIMEOUT_S, check=False, + ) + except (subprocess.TimeoutExpired, OSError) as exc: + return AMDInfo(available=False, note=f"rocm-smi failed: {exc!s}") + if result.returncode != 0: + return AMDInfo( + available=False, + note=f"rocm-smi exited rc={result.returncode}: {result.stderr.strip()[:200]}", + ) + + try: + data = json.loads(result.stdout) + except json.JSONDecodeError: + # rocm-smi versions that don't support --json print plain text; + # surface a soft "available but indeterminate" instead of erroring. + return AMDInfo( + available=True, + note="rocm-smi present but --json not supported on this version", + ) + + gpus: list[AMDGPU] = [] + rocm_version = "" + for key, value in data.items(): + if not isinstance(value, dict): + continue + if key.lower().startswith("system"): + rocm_version = value.get("Driver version", "") + continue + # GPU entries are keyed "card0" / "card1" etc. + name = value.get("Card series") or value.get("Card model") or key + vram_str = value.get("VRAM Total Memory (B)") or "0" + try: + vram_b = int(vram_str) + except (TypeError, ValueError): + vram_b = 0 + gpus.append(AMDGPU( + name=str(name), + vram_gb=round(vram_b / (1024 ** 3), 2), + driver_version=rocm_version, + )) + + return AMDInfo( + available=bool(gpus), + gpus=gpus, + rocm_version=rocm_version, + note=None if gpus else "rocm-smi succeeded but reported no GPUs", + ) + + +# ---- NVIDIA GPU ------------------------------------------------------------- + + +class NVIDIAGPU(BaseModel): + model_config = ConfigDict(extra="forbid") + + name: str + vram_gb: float + driver_version: str = "" + cuda_version: str = "" + + +class NVIDIAInfo(BaseModel): + model_config = ConfigDict(extra="forbid") + + available: bool = False + gpus: list[NVIDIAGPU] = Field(default_factory=list) + driver_version: str = "" + cuda_version: str = "" + note: str | None = None + + +def probe_nvidia() -> NVIDIAInfo: + """Probe NVIDIA GPUs via nvidia-smi.""" + nvidia_smi = _which("nvidia-smi") + if nvidia_smi is None: + return NVIDIAInfo(available=False, note="nvidia-smi not found on PATH") + try: + result = subprocess.run( + [nvidia_smi, + "--query-gpu=name,memory.total,driver_version", + "--format=csv,noheader,nounits"], + capture_output=True, text=True, timeout=_PROBE_TIMEOUT_S, check=False, + ) + except (subprocess.TimeoutExpired, OSError) as exc: + return NVIDIAInfo(available=False, note=f"nvidia-smi failed: {exc!s}") + if result.returncode != 0: + return NVIDIAInfo( + available=False, + note=f"nvidia-smi exited rc={result.returncode}: {result.stderr.strip()[:200]}", + ) + + gpus: list[NVIDIAGPU] = [] + driver_version = "" + for line in result.stdout.strip().splitlines(): + parts = [p.strip() for p in line.split(",")] + if len(parts) < 3: + continue + name, vram_mb_str, driver = parts[0], parts[1], parts[2] + try: + vram_mb = float(vram_mb_str) + except ValueError: + vram_mb = 0.0 + gpus.append(NVIDIAGPU( + name=name, + vram_gb=round(vram_mb / 1024.0, 2), + driver_version=driver, + )) + driver_version = driver + + # CUDA version comes from a separate nvidia-smi call (the header). + cuda_version = "" + try: + header = subprocess.run( + [nvidia_smi], capture_output=True, text=True, + timeout=_PROBE_TIMEOUT_S, check=False, + ) + m = re.search(r"CUDA Version:\s*([0-9.]+)", header.stdout) + if m: + cuda_version = m.group(1) + except (subprocess.TimeoutExpired, OSError): + pass + + return NVIDIAInfo( + available=bool(gpus), + gpus=gpus, + driver_version=driver_version, + cuda_version=cuda_version, + note=None if gpus else "nvidia-smi succeeded but reported no GPUs", + ) + + +# ---- composite profile ------------------------------------------------------ + + +class HardwareProfile(BaseModel): + """What the Coach UI's `Hardware available` card consumes.""" + + model_config = ConfigDict(extra="forbid") + + cpu: CPUInfo + amd: AMDInfo + nvidia: NVIDIAInfo + recommended_lane: str = Field( + description=( + "`trl_cpu` / `trl_local` / `axolotl_amd` — picks the best training " + "lane based on what's actually detected. `trl_local` is the " + "device-aware in-process lane for consumer GPUs." + ), + ) + + +def _is_mi300x_class(name: str) -> bool: + """True for datacenter CDNA cards (MI2xx/MI3xx, Instinct) — the axolotl target. + + Matches "MI300X", "AMD Instinct MI325X", "MI250", etc. — `MI` + 2-3 digits, + not requiring a trailing word boundary (vendor strings append X/A suffixes). + """ + upper = name.upper() + return "INSTINCT" in upper or bool(re.search(r"\bMI\d{2,3}", upper)) + + +def recommend_lane(cpu: CPUInfo, amd: AMDInfo, nvidia: NVIDIAInfo) -> str: + """Pick the most capable detected training lane. + + - A datacenter AMD Instinct (MI300X-class) → `axolotl_amd` (the AOT subprocess + path with the seven MI300X env vars). + - Any other local GPU — a consumer Radeon, or any NVIDIA card — → `trl_local`, + the device-aware in-process lane (there is no CUDA-axolotl subprocess path). + - Nothing detected → `trl_cpu`. + + Returns one of: 'axolotl_amd', 'trl_local', 'trl_cpu'. + """ + if amd.available and amd.gpus: + if any(_is_mi300x_class(g.name) for g in amd.gpus): + return "axolotl_amd" + return "trl_local" # consumer Radeon + if nvidia.available and nvidia.gpus: + return "trl_local" # local NVIDIA (consumer RTX) — in-process lane + _ = cpu # CPU is the universal fallback; argument kept for signature symmetry + return "trl_cpu" + + +def probe_all() -> HardwareProfile: + """One call returns the full hardware profile + recommendation.""" + cpu = probe_cpu() + amd = probe_amd() + nvidia = probe_nvidia() + return HardwareProfile( + cpu=cpu, amd=amd, nvidia=nvidia, + recommended_lane=recommend_lane(cpu, amd, nvidia), + ) + + +# ---- live metrics (admin panel) -------------------------------------------- + + +class LiveMetrics(BaseModel): + """Snapshot of host pressure + the operator process's own state. + + Pollable at 1Hz from the Advanced Admin card. Pure stdlib — reads + /proc on Linux, falls back to os.getloadavg + os.statvfs elsewhere. + """ + + model_config = ConfigDict(extra="forbid") + + load_avg_1m: float | None = None + load_avg_5m: float | None = None + load_avg_15m: float | None = None + ram_total_gb: float = 0.0 + ram_available_gb: float = 0.0 + ram_used_pct: float = 0.0 + disk_total_gb: float = 0.0 + disk_used_pct: float = 0.0 + operator_rss_mb: float = 0.0 + operator_threads: int = 0 + cores: int = 1 + ts: float = Field(description="Unix seconds at sample time.") + + +def _read_operator_proc_stat() -> tuple[float, int]: + """Return (rss_mb, threads) for the current python process. (0, 0) on failure.""" + try: + with Path(f"/proc/{os.getpid()}/status").open("r", encoding="utf-8") as fh: + text = fh.read() + except OSError: + return 0.0, 0 + rss_kb = 0 + threads = 0 + for line in text.splitlines(): + if line.startswith("VmRSS:"): + try: + rss_kb = int(line.split()[1]) + except (IndexError, ValueError): + pass + elif line.startswith("Threads:"): + try: + threads = int(line.split()[1]) + except (IndexError, ValueError): + pass + return rss_kb / 1024.0, threads + + +def _disk_usage(path: str = "/") -> tuple[float, float]: + """Return (total_gb, used_pct) for the filesystem holding `path`.""" + try: + st = os.statvfs(path) + except OSError: + return 0.0, 0.0 + total_b = st.f_blocks * st.f_frsize + free_b = st.f_bavail * st.f_frsize + used_b = total_b - free_b + if total_b <= 0: + return 0.0, 0.0 + return total_b / (1024 ** 3), 100.0 * used_b / total_b + + +def probe_live_metrics() -> LiveMetrics: + """Cheap (~ms) sample of host + operator process state. + + Backbone of the Advanced Admin card — the UI polls this and graphs + the result. Never expensive, never throws on a missing /proc entry + (returns zeroes / Nones so the panel renders a "—" instead of + crashing). + """ + import time as _time + + load: tuple[float, float, float] | None + try: + load = os.getloadavg() + except (OSError, AttributeError): + load = None + + ram_total_gb, ram_avail_gb = _parse_meminfo() + ram_used_pct = ( + 100.0 * (1.0 - ram_avail_gb / ram_total_gb) + if ram_total_gb > 0 else 0.0 + ) + + disk_total_gb, disk_used_pct = _disk_usage("/") + rss_mb, threads = _read_operator_proc_stat() + + return LiveMetrics( + load_avg_1m=load[0] if load else None, + load_avg_5m=load[1] if load else None, + load_avg_15m=load[2] if load else None, + ram_total_gb=round(ram_total_gb, 2), + ram_available_gb=round(ram_avail_gb, 2), + ram_used_pct=round(ram_used_pct, 1), + disk_total_gb=round(disk_total_gb, 2), + disk_used_pct=round(disk_used_pct, 1), + operator_rss_mb=round(rss_mb, 1), + operator_threads=threads, + cores=os.cpu_count() or 1, + ts=_time.time(), + ) + + +__all__ = [ + "AMDGPU", + "NVIDIAGPU", + "AMDInfo", + "CPUInfo", + "HardwareProfile", + "LiveMetrics", + "NVIDIAInfo", + "probe_all", + "probe_amd", + "probe_cpu", + "probe_live_metrics", + "probe_nvidia", + "recommend_lane", +] diff --git a/mindxtrain/operator/coach/run_metrics.py b/mindxtrain/operator/coach/run_metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..e5a5e2efaa7c1efc934bd9c55d3b900b13b0ec7f --- /dev/null +++ b/mindxtrain/operator/coach/run_metrics.py @@ -0,0 +1,198 @@ +"""Per-run system-metrics sampler. + +While a training run is in `running` status, this module emits a +`MetricsEvent` (cpu_pct, ram_pct, load_1m, trainer-PID rss + cpu_seconds) +into the run's SSE channel once per second. The Coach UI's +`#step-train` panel renders the resulting time-series as d3 sparklines, +so the user can see — without leaving the browser — whether the +configured `cpu_throttle.percent` actually held, whether the trainer +RSS is growing the way the recipe predicted, and how much CPU-time +the run has actually consumed. + +Design notes: + +- One asyncio task per run_id, registered in `_TASKS`. Stopping a + run cancels the task and drops the buffer; multiple concurrent + training runs each get their own buffer + task. +- Each tick publishes via the run registry's `publish_threadsafe` + + also appends to a rolling deque (last 300 samples = 5 min @ 1 Hz) + so a tab-switch can re-fetch history via + `GET /coach/api/runs/{id}/metrics?since=N` without waiting for fresh + ticks. +- `psutil.NoSuchProcess` (the trainer died or operator-PID disappeared) + emits a single zero-filled MetricsEvent and exits — never raises + out of the task. +- Read `/proc/loadavg` directly: cheaper than running `uptime` for a + number we only need to a couple of decimal places. +""" + +from __future__ import annotations + +import asyncio +import contextlib +import logging +import time +from collections import deque +from typing import Any + +import psutil + +logger = logging.getLogger("mindxtrain.run_metrics") + +# Rolling buffer of last 300 samples per run (5 min at 1 Hz). The number +# is big enough to feel "live" for a coffee break without growing past +# ~15 KB per run. +_BUFFER_CAP = 300 + +# Active sampler tasks keyed by run_id; cancelled on stop. +_TASKS: dict[str, asyncio.Task] = {} + +# Rolling buffers keyed by run_id. Survive task cancellation so the +# UI can backfill the final frozen state on tab-switch. +_BUFFERS: dict[str, deque[dict[str, Any]]] = {} + + +def _read_loadavg() -> float: + """First field of /proc/loadavg — 1-minute load average.""" + try: + with open("/proc/loadavg", encoding="utf-8") as fh: + return float(fh.read().split()[0]) + except (OSError, ValueError, IndexError): + return 0.0 + + +def _sample(run_id: str, pid: int) -> dict[str, Any]: + """Build one MetricsEvent dict. Process-gone → zero-filled record.""" + cpu_pct = float(psutil.cpu_percent(interval=None)) + ram_pct = float(psutil.virtual_memory().percent) + load_1m = _read_loadavg() + try: + proc = psutil.Process(pid) + rss_bytes = proc.memory_info().rss + cpu_times = proc.cpu_times() + proc_rss_mb = rss_bytes / (1024 * 1024) + proc_cpu_s = float(cpu_times.user + cpu_times.system) + except psutil.NoSuchProcess: + proc_rss_mb = 0.0 + proc_cpu_s = 0.0 + return { + "kind": "metrics", + "run_id": run_id, + "ts": time.time(), + "cpu_pct": cpu_pct, + "ram_pct": ram_pct, + "load_1m": load_1m, + "proc_rss_mb": proc_rss_mb, + "proc_cpu_seconds": proc_cpu_s, + } + + +async def _sampler_loop( + run_id: str, + pid: int, + interval_s: float, + publish: Any, +) -> None: + """Loop body — runs until cancelled or the trainer PID disappears.""" + # Prime psutil.cpu_percent: the first reading-after-prime is 0.0 + # because it has no baseline. Run one priming call before the loop. + psutil.cpu_percent(interval=None) + buf = _BUFFERS.setdefault(run_id, deque(maxlen=_BUFFER_CAP)) + try: + while True: + sample = _sample(run_id, pid) + buf.append(sample) + try: + publish(run_id, sample) + except Exception as exc: + logger.debug("metrics publish failed: %r", exc) + # Process-gone: emit the zero-sample and exit. + if sample["proc_rss_mb"] == 0.0 and sample["proc_cpu_seconds"] == 0.0: + # Only treat as terminal if psutil told us the proc disappeared. + # A live process with 0 cpu_seconds during startup is normal — + # check explicitly. + if not psutil.pid_exists(pid): + logger.debug("metrics: pid %d gone, exiting sampler", pid) + return + await asyncio.sleep(interval_s) + except asyncio.CancelledError: + # Normal stop path — propagate so the task is properly cancelled. + raise + except Exception as exc: + logger.warning("metrics sampler crashed: %r", exc) + + +def start_metrics_sampler( + run_id: str, + pid: int, + *, + interval_s: float = 1.0, + publish: Any | None = None, +) -> asyncio.Task: + """Start the per-run sampler. Returns the asyncio.Task. + + `publish(run_id, dict)` is called synchronously inside the loop for + each sample. Defaults to the live run registry's + `publish_threadsafe` (which wraps the sample in a MetricsEvent and + forwards to subscribers). Tests can pass a capture lambda. + """ + if run_id in _TASKS and not _TASKS[run_id].done(): + return _TASKS[run_id] + if publish is None: + publish = _default_publish + task = asyncio.create_task( + _sampler_loop(run_id, pid, interval_s, publish), + name=f"metrics-{run_id}", + ) + _TASKS[run_id] = task + return task + + +def _default_publish(run_id: str, sample: dict[str, Any]) -> None: + """Publish into the live run registry. Lazy-imported to break a circle.""" + from mindxtrain.operator import runs as _runs + + event = _runs.MetricsEvent(**sample) + # Use publish (not publish_threadsafe) because the sampler runs on + # the same event loop the registry was attached to. + _runs._REGISTRY.publish(run_id, event) + + +async def stop_metrics_sampler(run_id: str) -> None: + """Cancel + await the sampler. Buffer is preserved for backfill.""" + task = _TASKS.pop(run_id, None) + if task is None or task.done(): + return + task.cancel() + with contextlib.suppress(asyncio.CancelledError): + await task + + +def get_buffer(run_id: str, *, since: float = 0.0) -> list[dict[str, Any]]: + """Return samples with `ts > since`. Used by the backfill endpoint.""" + buf = _BUFFERS.get(run_id) + if buf is None: + return [] + if since <= 0.0: + return list(buf) + return [s for s in buf if s["ts"] > since] + + +def clear_buffer(run_id: str) -> None: + """Drop the rolling buffer for a run. Called when the run is purged.""" + _BUFFERS.pop(run_id, None) + + +def has_sampler(run_id: str) -> bool: + """Test helper: is a sampler currently registered for this run?""" + task = _TASKS.get(run_id) + return task is not None and not task.done() + + +__all__ = [ + "clear_buffer", + "get_buffer", + "has_sampler", + "start_metrics_sampler", + "stop_metrics_sampler", +] diff --git a/mindxtrain/operator/coach/static/coach.js b/mindxtrain/operator/coach/static/coach.js new file mode 100644 index 0000000000000000000000000000000000000000..052ca6c57a64c19de88297522929f0d9d3d8a1a6 --- /dev/null +++ b/mindxtrain/operator/coach/static/coach.js @@ -0,0 +1,2594 @@ +// mindXtrain Coach — vanilla-JS state machine. +// No framework, no build step. Talk to /coach/api/* over fetch. + +const $ = (sel) => document.querySelector(sel); +const $$ = (sel) => document.querySelectorAll(sel); + +const state = { + recipe: null, // selected recipe name + plan: null, // AutotunePlan from /api/bench + compileResult: null, // CompileResponse from /api/compile + run: null, // active Run from /api/runs/launch + eventSource: null, // active EventSource for the active run + chart: null, // Chart.js instance, if Chart is available + logLines: 0, // capped at MAX_LOG_LINES client-side + preflightReady: false, + corpusReady: false, + hardware: null, // most recent HardwareProfile from /coach/api/diagnostics/hardware + mei: null, // most recent MEIScoreView from /coach/api/mei/score + meiChart: null, // Chart.js radar instance for the MEI sub-indices + chatBackendModel: "", // detected ollama/vllm model name (drives /v1/chat/completions body) + metrics: [], // rolling buffer of MetricsEvent samples (capped METRICS_BUFFER_CAP) + metricsTimer: null, // setInterval handle for elapsed-time counter + totalSteps: null, // StepEvent.total_steps — drives the progress bar + firstStepTs: null, // wall-clock ms at the first observed step (for ETA) + firstStep: 0, // step number of that first observed step + stepBuffer: [], // {step,loss,mean_token_accuracy} for the result banner +}; + +const METRICS_BUFFER_CAP = 300; + +const MAX_LOG_LINES = 2000; + +// Per-step rows kept in the (collapsed) metrics table DOM. The full count is +// always reported in the accordion summary so the cap is never silent. +const MAX_TABLE_ROWS = 50; + +// Loss-chart points retained. A real MI300X run logs thousands of steps; an +// unbounded canvas dataset makes the page janky. We keep a rolling window and +// surface "showing last X of N" so the compression is honest, not hidden. +const MAX_CHART_POINTS = 1500; + +// When a log accumulates more than this many lines, auto-collapse the +// oldest ~80% into a folded sub-accordion so the operator's eye stays on +// the most recent activity. Tuned to keep the "live tail" UX legible on +// a 13-15" laptop screen. +const LOG_FOLD_THRESHOLD = 400; +const LOG_FOLD_KEEP_RECENT = 80; + +function _foldLogElement(pre) { + // Idempotent: collapses lines older than the most recent + // LOG_FOLD_KEEP_RECENT once `pre` has > LOG_FOLD_THRESHOLD child text + // nodes. Replays produce the same DOM so callers can call this on + // every `appendLog` without rebuilding the world. + const children = Array.from(pre.childNodes).filter( + n => n.nodeType === Node.TEXT_NODE, + ); + if (children.length <= LOG_FOLD_THRESHOLD) return; + // Already folded earlier? Look for the sentinel <details> at the start. + if (pre.firstChild && pre.firstChild.tagName === "DETAILS") { + // The fold exists; just keep growing the visible tail. We re-fold + // periodically by reading the visible-tail node count and migrating + // overflow into the existing details summary. + const det = pre.firstChild; + const oldSummary = det.querySelector("summary"); + const folded = det.querySelector("pre"); + const recent = children.slice(LOG_FOLD_KEEP_RECENT * -1); + const earlier = children.slice(0, -recent.length); + for (const n of earlier) { + // Move into the folded pre. + if (n !== folded && (n.previousSibling !== det && n.parentNode === pre)) { + folded.appendChild(n); + } + } + const count = folded.childNodes.length; + oldSummary.textContent = `▸ ${count} earlier lines (expand)`; + return; + } + // First fold for this pre. + const recent = children.slice(LOG_FOLD_KEEP_RECENT * -1); + const earlier = children.slice(0, -recent.length); + const details = document.createElement("details"); + const summary = document.createElement("summary"); + summary.textContent = `▸ ${earlier.length} earlier lines (expand)`; + const foldedPre = document.createElement("pre"); + foldedPre.className = "log-tail folded"; + for (const n of earlier) { + foldedPre.appendChild(n); + } + details.appendChild(summary); + details.appendChild(foldedPre); + // Insert the fold at the top. + pre.insertBefore(details, pre.firstChild); +} + +// Ordered step ids — used by progressTo to compute "next" if a caller +// doesn't pass one explicitly, and by syncPipelineHeader to pick a stage. +const STEP_ORDER = [ + "step-preflight", + "step-dream-corpus", + "step-recipes", + "step-autotune", + "step-compile", + "step-deploy", + "step-train", + "step-chat", +]; + +// Map step ids to one of the three header pipeline stages. +const STAGE_FOR_STEP = { + "step-preflight": "automind", + "step-hardware": "automind", + "step-dream-corpus": "automind", + "step-create-dataset": "automind", + "step-recipes": "mind", + "step-autotune": "mind", + "step-compile": "mind", + "step-deploy": "mind", + "step-train": "mind", + "step-receipt": "cust", + "step-boardroom": "cust", + "step-mei": "cust", + "step-chat": "cust", +}; + +// --- auto-advance helpers ------------------------------------------------ + +function syncPipelineHeader(activeStepId) { + const want = STAGE_FOR_STEP[activeStepId]; + if (!want) return; + $$(".pipeline .stage").forEach((node) => { + node.classList.toggle("active", node.dataset.stage === want); + }); +} + +// Mark previous active card .done, mark `id` .active, scroll into view, sync +// the header pipeline. Safe to call repeatedly; idempotent for the same id. +function progressTo(id) { + const next = document.getElementById(id); + if (!next) return; + $$("section.card.active").forEach((c) => { + if (c.id !== id) { + c.classList.remove("active"); + c.classList.add("done"); + } + }); + next.classList.remove("done"); + next.classList.add("active"); + next.scrollIntoView({ behavior: "smooth", block: "start" }); + syncPipelineHeader(id); +} + +function markCardDone(id) { + const node = document.getElementById(id); + if (!node) return; + node.classList.remove("active"); + node.classList.add("done"); +} + +async function getJSON(url) { + const r = await fetch(url); + if (!r.ok) throw new Error(`${url} → ${r.status}`); + return r.json(); +} + +async function postJSON(url, body) { + const r = await fetch(url, { + method: "POST", + headers: { "content-type": "application/json" }, + body: JSON.stringify(body || {}), + }); + if (!r.ok) throw new Error(`${url} → ${r.status}`); + return r.json(); +} + +// --- step 1: preflight env -------------------------------------------------- + +async function runPreflight() { + const btn = $("#run-preflight"); + const summary = $("#preflight-summary"); + const list = $("#preflight-list"); + const badge = $("#preflight-badge"); + if (btn) btn.disabled = true; + summary.textContent = "checking…"; + try { + const res = await getJSON("/coach/api/preflight"); + list.innerHTML = ""; + for (const name of [...res.required, ...res.optional]) { + const present = !!res.vars[name]; + const li = document.createElement("li"); + const mark = present ? "✓" : "✗"; + const cls = present ? "fits-yes" : "fits-no"; + const tag = res.required.includes(name) ? "required" : "optional"; + li.innerHTML = `<span class="${cls}">${mark}</span> <code>${name}</code> <span class="muted">${tag}</span>`; + list.appendChild(li); + } + state.preflightReady = res.ready; + if (res.ready) { + summary.textContent = `all required env vars set (${res.required.length})`; + badge.textContent = "ready"; + badge.className = "badge-status succeeded"; + badge.hidden = false; + markCardDone("step-preflight"); + // Auto-advance: env check → hardware probe → corpus check. + progressTo("step-hardware"); + await runHardware(); + progressTo("step-dream-corpus"); + runDreamCorpus(); + } else { + summary.textContent = `missing: ${res.required_missing.join(", ")}`; + badge.textContent = "not ready"; + badge.className = "badge-status failed"; + badge.hidden = false; + } + } catch (e) { + summary.textContent = `preflight probe failed: ${e}`; + } finally { + if (btn) btn.disabled = false; + } +} + +// --- advanced admin (always-on-top diagnostics) -------------------------- + +const ADMIN_POLL_MS = 2000; +const ADMIN_FIREHOSE_MAX = 800; // hard cap on lines in admin firehose +const adminState = { + pollTimer: null, + paused: false, + firehoseSources: new Map(), // run_id → EventSource + firehoseLines: 0, +}; + +function _adminMetricCell(label, value, statusClass) { + return `<div class="admin-metric ${statusClass || ""}">` + + `<div class="admin-metric-label">${label}</div>` + + `<div class="admin-metric-value">${value}</div></div>`; +} + +function _classifyPct(pct, warn = 70, bad = 90) { + if (pct >= bad) return "bad"; + if (pct >= warn) return "warn"; + return ""; +} + +function _classifyLoad(load, cores) { + if (load == null) return ""; + const ratio = load / Math.max(1, cores); + if (ratio >= 1.5) return "bad"; + if (ratio >= 1.0) return "warn"; + return ""; +} + +async function refreshAdminMetrics() { + if (adminState.paused) return; + try { + const m = await getJSON("/coach/api/diagnostics/live"); + const grid = $("#admin-metrics"); + if (!grid) return; + const cores = m.cores || 1; + const load1 = m.load_avg_1m; + const cells = [ + _adminMetricCell("Load 1m", load1 == null ? "—" : `${load1.toFixed(2)} / ${cores}`, + _classifyLoad(load1, cores)), + _adminMetricCell("Load 5m", m.load_avg_5m == null ? "—" : m.load_avg_5m.toFixed(2), + _classifyLoad(m.load_avg_5m, cores)), + _adminMetricCell("Load 15m", m.load_avg_15m == null ? "—" : m.load_avg_15m.toFixed(2), + _classifyLoad(m.load_avg_15m, cores)), + _adminMetricCell("RAM used", + `${m.ram_used_pct.toFixed(1)}% (${m.ram_available_gb.toFixed(1)} GB free)`, + _classifyPct(m.ram_used_pct)), + _adminMetricCell("Disk used", + `${m.disk_used_pct.toFixed(1)}% of ${m.disk_total_gb.toFixed(0)} GB`, + _classifyPct(m.disk_used_pct, 80, 95)), + _adminMetricCell("Operator RSS", `${m.operator_rss_mb.toFixed(0)} MB`, ""), + _adminMetricCell("Operator threads", String(m.operator_threads), ""), + _adminMetricCell("Cores", String(cores), ""), + ]; + grid.innerHTML = cells.join(""); + } catch (e) { + // Silent; the admin card is non-critical. + } +} + +function _fmtRunStart(iso) { + try { + return new Date(iso).toLocaleTimeString(); + } catch (_) { + return iso; + } +} + +async function refreshAdminRuns() { + if (adminState.paused) return; + try { + const runs = await getJSON("/coach/api/diagnostics/runs"); + const tbody = $("#admin-runs-table tbody"); + if (!tbody) return; + tbody.innerHTML = ""; + for (const r of runs.slice(0, 12)) { + const tr = document.createElement("tr"); + const lossStr = r.last_loss == null ? "—" : r.last_loss.toFixed(4); + tr.innerHTML = + `<td class="mono">${r.id}</td>` + + `<td>${r.recipe}</td>` + + `<td class="status-${r.status}">${r.status}</td>` + + `<td>${_fmtRunStart(r.created_at)}</td>` + + `<td>${r.last_step ?? "—"}</td>` + + `<td>${lossStr}</td>`; + tbody.appendChild(tr); + // Auto-subscribe firehose to any run we haven't seen yet. + if (!adminState.firehoseSources.has(r.id) && + ["pending", "running"].includes(r.status)) { + _adminSubscribeFirehose(r.id); + } + } + } catch (e) { /* non-critical */ } +} + +function _adminAppendFirehose(line) { + const pre = $("#admin-firehose"); + if (!pre) return; + pre.appendChild(document.createTextNode(line + "\n")); + adminState.firehoseLines += 1; + while (adminState.firehoseLines > ADMIN_FIREHOSE_MAX && pre.firstChild) { + pre.removeChild(pre.firstChild); + adminState.firehoseLines -= 1; + } + if (adminState.firehoseLines % 50 === 0) { + _foldLogElement(pre); + } + pre.scrollTop = pre.scrollHeight; + $("#admin-firehose-summary").textContent = + `(${adminState.firehoseLines} lines · ${adminState.firehoseSources.size} runs)`; +} + +function _adminSubscribeFirehose(runId) { + if (adminState.firehoseSources.has(runId)) return; + const es = new EventSource(`/coach/api/runs/${runId}/events`); + adminState.firehoseSources.set(runId, es); + const short = runId.slice(0, 8); + es.addEventListener("status", (e) => { + const ev = JSON.parse(e.data); + _adminAppendFirehose(`[${short}] status → ${ev.status}: ${ev.message || ""}`); + if (["succeeded", "failed", "cancelled"].includes(ev.status)) { + es.close(); + adminState.firehoseSources.delete(runId); + } + }); + es.addEventListener("log", (e) => { + const ev = JSON.parse(e.data); + _adminAppendFirehose(`[${short}] ${ev.line}`); + }); + es.addEventListener("step", (e) => { + const ev = JSON.parse(e.data); + _adminAppendFirehose( + `[${short}] step=${ev.step} loss=${(ev.loss || 0).toFixed(4)} ` + + `lr=${ev.lr || "—"} grad_norm=${ev.grad_norm || "—"}`, + ); + }); + es.addEventListener("eval", (e) => { + const ev = JSON.parse(e.data); + _adminAppendFirehose(`[${short}] eval@${ev.step} ${JSON.stringify(ev.metrics)}`); + }); + es.addEventListener("error", () => { + _adminAppendFirehose(`[${short}] (event stream disconnected)`); + }); +} + +function _adminStartPolling() { + if (adminState.pollTimer != null) return; + refreshAdminMetrics(); + refreshAdminRuns(); + adminState.pollTimer = setInterval(() => { + refreshAdminMetrics(); + refreshAdminRuns(); + }, ADMIN_POLL_MS); +} + +function _adminStopPolling() { + if (adminState.pollTimer != null) { + clearInterval(adminState.pollTimer); + adminState.pollTimer = null; + } +} + +function _adminTogglePoll() { + adminState.paused = !adminState.paused; + const btn = $("#admin-toggle-poll"); + if (btn) btn.textContent = adminState.paused ? "Resume polling" : "Pause polling"; +} + +function _adminClearFirehose() { + const pre = $("#admin-firehose"); + if (pre) pre.textContent = ""; + adminState.firehoseLines = 0; + $("#admin-firehose-summary").textContent = + `(0 lines · ${adminState.firehoseSources.size} runs)`; +} + +// --- step 2: hardware diagnostics ----------------------------------------- + +function _hwPanelHTML(title, info, fields) { + const cls = info.available && (fields.gpus ? fields.gpus.length > 0 : true) + ? "hw-panel hw-ok" + : "hw-panel hw-off"; + const fieldRows = (fields.entries || []).map( + ([k, v]) => `<dt>${k}</dt><dd>${v}</dd>`, + ).join(""); + const noteHTML = info.note + ? `<p class="hint hw-note">${info.note}</p>` : ""; + return ` + <div class="${cls}"> + <h3>${title}</h3> + <dl>${fieldRows}</dl> + ${noteHTML} + </div>`; +} + +function _fmtGB(v) { + return typeof v === "number" ? `${v.toFixed(1)} GB` : "—"; +} + +async function runHardware() { + const summary = $("#hardware-summary"); + const grid = $("#hardware-grid"); + const rec = $("#hardware-recommendation"); + const btn = $("#run-hardware"); + if (btn) btn.disabled = true; + summary.textContent = "probing…"; + try { + const p = await getJSON("/coach/api/diagnostics/hardware"); + state.hardware = p; + // CPU panel — always available. + const cpu = p.cpu || {}; + const cpuEntries = [ + ["model", cpu.model_name || "CPU"], + ["vendor", cpu.vendor || "—"], + ["cores", `${cpu.cores || 0} (Ryzen: ${cpu.is_ryzen ? "yes" : "no"})`], + ["RAM", `${_fmtGB(cpu.ram_available_gb)} avail / ${_fmtGB(cpu.ram_total_gb)} total`], + ["load 1m", cpu.load_avg_1m == null ? "—" : cpu.load_avg_1m.toFixed(2)], + ]; + // AMD panel. + const amd = p.amd || {}; + const amdGPUs = amd.gpus || []; + const amdEntries = amd.available + ? [ + ["ROCm", amd.rocm_version || "—"], + ...amdGPUs.map((g, i) => [`gpu${i}`, `${g.name} · ${_fmtGB(g.vram_gb)}`]), + ] + : [["status", "not detected"]]; + // NVIDIA panel. + const nv = p.nvidia || {}; + const nvGPUs = nv.gpus || []; + const nvEntries = nv.available + ? [ + ["driver", nv.driver_version || "—"], + ["CUDA", nv.cuda_version || "—"], + ...nvGPUs.map((g, i) => [`gpu${i}`, `${g.name} · ${_fmtGB(g.vram_gb)}`]), + ] + : [["status", "not detected"]]; + + grid.innerHTML = + _hwPanelHTML("CPU", cpu, { entries: cpuEntries }) + + _hwPanelHTML("AMD GPU", amd, { entries: amdEntries, gpus: amdGPUs }) + + _hwPanelHTML("NVIDIA GPU", nv, { entries: nvEntries, gpus: nvGPUs }); + + const laneLabel = { + "axolotl_amd": "AMD MI300X (axolotl)", + "trl_local": "local GPU (trl_local)", + "trl_cpu": "CPU (trl_cpu)", + }[p.recommended_lane] || p.recommended_lane; + rec.innerHTML = `Recommended lane: <strong>${laneLabel}</strong>`; + rec.hidden = false; + summary.textContent = `recommended: ${laneLabel}`; + markCardDone("step-hardware"); + + // Auto-suggest a matching recipe so the operator gets a one-click + // training start. Recipe ↔ lane mapping: + // trl_cpu → mindx_fallback_qwen3_1_5b_cpu_real (full ~2 hr CPU fine-tune) + // trl_local → mindx_fallback_qwen3_1_5b_local (device-aware: consumer GPU else CPU) + // axolotl_amd → mindx_fallback_qwen3_1_5b_sft_lora (1× MI300X) + // The _smoke recipe stays available in the grid for tests + CI — UI + // just doesn't recommend it because it won't actually adapt the model. + // If the recipes haven't loaded yet, retry briefly — loadRecipes() is + // racing with us on page bootstrap. + const laneToRecipe = { + "trl_cpu": "mindx_fallback_qwen3_1_5b_cpu_real", + "trl_local": "mindx_fallback_qwen3_1_5b_local", + "axolotl_amd": "mindx_fallback_qwen3_1_5b_sft_lora", + }; + const recipeName = laneToRecipe[p.recommended_lane]; + if (recipeName) { + _autoSelectRecipeWhenReady(recipeName, 0); + } + } catch (e) { + summary.textContent = `probe failed: ${e}`; + } finally { + if (btn) btn.disabled = false; + } +} + +function _autoSelectRecipeWhenReady(name, attempt) { + // Promote the hardware-recommended recipe to the prominent default slot. + _recommendedRecipe = name; + renderDefaultRecipe(); + const card = document.querySelector(`#recipe-list .recipe[data-name="${name}"]`); + if (card) { + card.classList.add("recommended"); + // Don't auto-click — the user picks deliberately (no surprise launch). + return; + } + if (attempt < 20) { + // loadRecipes() may still be in flight; retry up to ~4 seconds. + setTimeout(() => _autoSelectRecipeWhenReady(name, attempt + 1), 200); + } +} + +// --- step 3: dream corpus --------------------------------------------------- + +async function runDreamCorpus() { + const summary = $("#corpus-summary"); + const stats = $("#corpus-stats"); + const note = $("#corpus-note"); + summary.textContent = "counting…"; + try { + const res = await getJSON("/coach/api/dream-corpus"); + stats.innerHTML = ""; + const con = res.consolidation || { files: 0, raw_lines: 0, unique_rows: 0 }; + const evo = res.evolutions || { files: 0, raw_lines: 0, unique_rows: 0 }; + const fields = [ + ["root", res.root], + ["consolidation", `${con.unique_rows} unique / ${con.files} files`], + ["evolutions", `${evo.unique_rows} unique / ${evo.files} files`], + ]; + for (const [k, v] of fields) { + const li = document.createElement("li"); + li.innerHTML = `<code>${k}</code>=${v}`; + stats.appendChild(li); + } + stats.hidden = false; + state.corpusReady = res.ready; + if (res.note) { + note.textContent = res.note; + note.hidden = false; + } else { + note.hidden = true; + } + if (res.ready) { + const total = con.unique_rows + evo.unique_rows; + const detail = evo.unique_rows > 0 + ? `${con.unique_rows} consolidation + ${evo.unique_rows} evolution` + : `${con.unique_rows} consolidation`; + summary.textContent = `${total} unique examples ready (${detail})`; + markCardDone("step-dream-corpus"); + progressTo("step-recipes"); + } else { + summary.textContent = "corpus not ready — see note"; + } + } catch (e) { + summary.textContent = `corpus probe failed: ${e}`; + } +} + +// --- step 3: recipes ----------------------------------------------------- + +// The default shown front-and-center until hardware recommends one. Device-aware +// `trl_local` runs on a laptop or a GPU unchanged, so it's the safe default. +const DEFAULT_RECIPE = "mindx_fallback_qwen3_1_5b_local"; +let _recipeCache = []; +let _recommendedRecipe = null; + +function _recipeCardEl(r, isDefault) { + const div = document.createElement("div"); + div.className = "recipe" + (isDefault ? " recommended" : ""); + div.dataset.name = r.name; + div.innerHTML = ` + <h3>${r.name}</h3> + <div class="meta"> + <span class="badge">${r.method}</span> + <span class="badge">${r.gpus}× GPU</span> + ${r.base_model} + </div>`; + div.addEventListener("click", () => selectRecipe(r.name)); + return div; +} + +function renderDefaultRecipe() { + const host = $("#recipe-default"); + if (!host || !_recipeCache.length) return; + const name = _recommendedRecipe || DEFAULT_RECIPE; + const r = _recipeCache.find((x) => x.name === name) || _recipeCache[0]; + host.innerHTML = ""; + host.appendChild(_recipeCardEl(r, true)); +} + +async function loadRecipes() { + const list = await getJSON("/coach/api/recipes"); + _recipeCache = list; + $("#recipe-count").textContent = `(${list.length} total)`; + // Full list lives in the collapsed "other recipes" accordion… + const target = $("#recipe-list"); + target.innerHTML = ""; + for (const r of list) target.appendChild(_recipeCardEl(r, false)); + // …and the default/recommended one is shown prominently. + renderDefaultRecipe(); +} + +async function selectRecipe(name) { + state.recipe = name; + state.compileResult = null; + for (const node of $$(".recipe")) { + node.classList.toggle("selected", node.dataset.name === name); + } + const detail = await getJSON(`/coach/api/recipes/${name}`); + // Keep the detail around so the session headline can surface the + // recipe's cpu_throttle percent during the run. + state.recipeDetail = detail; + $("#recipe-yaml").textContent = detail.yaml; + const det = $("#recipe-detail"); + det.hidden = false; + det.open = true; + $("#run-compile").disabled = state.plan === null; + // Auto-advance: recipe picked → autotune. Run bench automatically if not + // already done; users who want to re-pick can click another recipe (this + // function is re-entrant and resets compileResult). + markCardDone("step-recipes"); + progressTo("step-autotune"); + if (state.plan === null) { + runBench(); + } +} + +// --- step 2: autotune ---------------------------------------------------- + +async function runBench() { + $("#run-bench").disabled = true; + $("#run-bench").textContent = "probing…"; + try { + const plan = await postJSON("/coach/api/bench", {}); + state.plan = plan; + $("#plan-json").textContent = JSON.stringify(plan, null, 2); + $("#plan-json").hidden = false; + const sum = $("#plan-summary"); + sum.innerHTML = ""; + const items = [ + ["attention", plan.attention_backend], + ["gemm", plan.gemm_heuristic], + ["rccl", plan.rccl_config], + ["fsdp_shard", plan.fsdp_shard_width], + ["arch", plan.gpu_arch], + ["rocm", plan.rocm_version], + ]; + for (const [k, v] of items) { + const li = document.createElement("li"); + li.textContent = `${k}=${v}`; + sum.appendChild(li); + } + sum.hidden = false; + $("#run-compile").disabled = state.recipe === null; + // Auto-advance: plan in hand → compile. + if (state.recipe) { + markCardDone("step-autotune"); + progressTo("step-compile"); + runCompile(); + } + } finally { + $("#run-bench").disabled = false; + $("#run-bench").textContent = "Re-run autotune"; + } +} + +// --- step 3: compile ----------------------------------------------------- + +async function runCompile() { + if (!state.recipe || !state.plan) return; + $("#run-compile").disabled = true; + try { + const res = await postJSON("/coach/api/compile", { + recipe: state.recipe, + plan: state.plan, + }); + state.compileResult = res; + const ov = $("#compile-overrides"); + ov.innerHTML = ""; + for (const o of res.overrides) { + const li = document.createElement("li"); + li.textContent = o; + ov.appendChild(li); + } + ov.hidden = false; + $("#compile-yaml").textContent = JSON.stringify(res.axolotl_yaml, null, 2); + $("#compile-yaml").hidden = false; + $("#run-train").disabled = false; + // Auto-advance: ready to deploy. Stop here for manual GitHub-push click — + // we don't auto-push, that's an explicit spend authorization. + markCardDone("step-compile"); + progressTo("step-deploy"); + const ghBtn = $("#run-github"); + if (ghBtn && !ghBtn.disabled) ghBtn.focus(); + } finally { + $("#run-compile").disabled = false; + } +} + +// --- step 4: train (live) ----------------------------------------------- + +function setStatusBadge(label) { + const el = $("#train-status"); + el.textContent = label; + el.className = "badge-status " + (label.toLowerCase().replace(/\s+/g, "-")); + el.hidden = false; +} + +function ensureChart() { + if (state.chart || typeof Chart === "undefined") { + if (typeof Chart === "undefined") $("#chart-fallback").hidden = false; + return state.chart; + } + const ctx = $("#loss-chart").getContext("2d"); + state.chart = new Chart(ctx, { + type: "line", + data: { + labels: [], + datasets: [ + { + label: "loss", data: [], borderWidth: 2, tension: 0.2, + yAxisID: "y", borderColor: "#f7921e", + }, + { + // mean_token_accuracy — "is it learning" signal. Non-trl + // backends omit it; NaN gaps render cleanly. + label: "accuracy", data: [], borderWidth: 2, tension: 0.2, + yAxisID: "yAcc", borderColor: "#2ea043", spanGaps: false, + }, + ], + }, + options: { + animation: false, + responsive: true, + scales: { + x: { title: { display: true, text: "step" } }, + y: { + position: "left", + title: { display: true, text: "loss" }, + beginAtZero: false, + }, + yAcc: { + position: "right", + min: 0, max: 1, + title: { display: true, text: "accuracy" }, + grid: { drawOnChartArea: false }, + }, + }, + plugins: { legend: { display: true } }, + }, + }); + return state.chart; +} + +function pushPoint(ev) { + const acc = ev.mean_token_accuracy; + const ent = ev.entropy; + const tbody = $("#metrics-table tbody"); + const tr = document.createElement("tr"); + tr.innerHTML = + `<td>${ev.step}</td><td>${ev.loss.toFixed(4)}</td>` + + `<td>${acc != null ? acc.toFixed(3) : "—"}</td>` + + `<td>${ent != null ? ent.toFixed(3) : "—"}</td>` + + `<td>${ev.lr ?? "—"}</td><td>${ev.grad_norm ?? "—"}</td>`; + tbody.appendChild(tr); + // Cap the DOM table; report the true total in the accordion summary so the + // cap is visible rather than silent. + while (tbody.children.length > MAX_TABLE_ROWS) tbody.removeChild(tbody.firstChild); + // Buffer for the terminal result banner (kept whole so loss A→B is exact). + state.stepBuffer.push({ step: ev.step, loss: ev.loss, mean_token_accuracy: acc }); + _updateMetricsTableCount(state.stepBuffer.length); + + const chart = ensureChart(); + if (chart) { + chart.data.labels.push(ev.step); + chart.data.datasets[0].data.push(ev.loss); + // accuracy on the 2nd axis — NaN where the backend didn't report it. + chart.data.datasets[1].data.push(acc != null ? acc : NaN); + // Roll the window so a thousands-of-steps run stays responsive. + let trimmed = false; + while (chart.data.labels.length > MAX_CHART_POINTS) { + chart.data.labels.shift(); + chart.data.datasets[0].data.shift(); + chart.data.datasets[1].data.shift(); + trimmed = true; + } + if (trimmed) { + const note = $("#chart-window-note"); + if (note) { + note.hidden = false; + note.textContent = + `showing last ${MAX_CHART_POINTS} of ${state.stepBuffer.length} steps`; + } + } + chart.update("none"); + } + _updateProgress(ev); +} + +function _updateMetricsTableCount(total) { + const el = $("#metrics-table-count"); + if (!el) return; + const plural = total === 1 ? "" : "s"; + el.textContent = total > MAX_TABLE_ROWS + ? `(${total} step${plural} · last ${MAX_TABLE_ROWS} shown)` + : `(${total} step${plural})`; +} + +function appendLog(ev) { + const pre = $("#train-log"); + _narratePhase(ev.line); + pre.appendChild(document.createTextNode(ev.line + "\n")); + state.logLines += 1; + if (state.logLines > MAX_LOG_LINES) { + // Drop the first N text nodes to keep DOM bounded. + while (state.logLines > MAX_LOG_LINES && pre.firstChild) { + pre.removeChild(pre.firstChild); + state.logLines -= 1; + } + } + // Re-fold every 50 lines so the DOM stays compact on long runs. + if (state.logLines % 50 === 0) { + _foldLogElement(pre); + } + _updateLogCount(); + pre.scrollTop = pre.scrollHeight; +} + +function _updateLogCount() { + const el = $("#train-log-count"); + if (!el) return; + const n = state.logLines; + el.textContent = n >= MAX_LOG_LINES + ? `(${n} lines · oldest dropped)` + : `(${n} line${n === 1 ? "" : "s"})`; +} + +function subscribeRun(runId) { + if (state.eventSource) state.eventSource.close(); + const es = new EventSource(`/coach/api/runs/${runId}/events`); + state.eventSource = es; + // Reveal the session-metrics tier and backfill the sparklines so + // they don't sit blank waiting for the first 1 Hz tick. + _enableSessionMetrics(); + _backfillSessionMetrics(runId); + es.addEventListener("step", (e) => pushPoint(JSON.parse(e.data))); + es.addEventListener("eval", (e) => _handleEvalEvent(JSON.parse(e.data))); + es.addEventListener("log", (e) => appendLog(JSON.parse(e.data))); + es.addEventListener("metrics", (e) => _handleMetricsEvent(JSON.parse(e.data))); + es.addEventListener("status", (e) => { + const ev = JSON.parse(e.data); + setStatusBadge(ev.status); + _setSessionStatus(ev.status); + const terminal = ["succeeded", "failed", "cancelled"].includes(ev.status); + if (terminal) { + es.close(); + state.eventSource = null; + _stopElapsedTimer(); + $("#cancel-train").hidden = true; + $("#run-train").disabled = false; + _renderResultBanner(ev.status, ev.message); + _setPhase(ev.status === "succeeded" ? "Done" : "Failed"); + if (ev.status === "succeeded") { + const fill = $("#progress-fill"); + if (fill) { fill.style.width = "100%"; fill.classList.add("done"); } + markCardDone("step-train"); + // Reveal the push-to-ollama button — adapter is on disk and the + // run registry knows about it. The user can pick a tag and merge + // before (or in parallel with) MEI scoring. + const pushWrap = $("#push-to-ollama-wrap"); + if (pushWrap) pushWrap.hidden = false; + // Receipt was emitted at completion — populate the verification card + // in place (no scroll; it sits between train and MEI). + loadReceiptForRun(state.run && state.run.id); + // Train succeeded — MEI scoring is the gate before promotion. + progressTo("step-mei"); + loadMEIForRun(state.run && state.run.id); + } + } + }); + es.addEventListener("error", () => { + setStatusBadge("disconnected"); + }); +} + +async function runTrain() { + if (!state.recipe || !state.plan) return; + $("#run-train").disabled = true; + $("#train-charts").hidden = false; + $("#train-log-wrap").hidden = false; + $("#train-log-wrap").open = true; + $("#train-log").textContent = ""; + $("#metrics-table tbody").innerHTML = ""; + state.logLines = 0; + _updateLogCount(); + _updateMetricsTableCount(0); + const chartNote = $("#chart-window-note"); + if (chartNote) { chartNote.hidden = true; chartNote.textContent = ""; } + if (state.chart) { + state.chart.data.labels = []; + state.chart.data.datasets[0].data = []; + state.chart.data.datasets[1].data = []; + state.chart.update("none"); + } + // Reset the session-metrics tier for the new run. + state.metrics = []; + // Reset the realtime-feedback surfaces for the new run. + state.totalSteps = null; + state.firstStepTs = null; + state.firstStep = 0; + state.stepBuffer = []; + _resetProgress(); + const resultEl = $("#train-result"); + if (resultEl) { resultEl.hidden = true; resultEl.textContent = ""; } + _enableSessionMetrics(); + _renderSessionSparklines(); + setStatusBadge("launching"); + _setSessionStatus("launching"); + try { + const r = await fetch("/coach/api/runs/launch", { + method: "POST", + headers: { "content-type": "application/json" }, + body: JSON.stringify({ recipe: state.recipe, plan: state.plan }), + }); + if (!r.ok) { + const text = await r.text(); + setStatusBadge("failed"); + appendLog({ line: `launch failed (${r.status}): ${text}` }); + $("#run-train").disabled = false; + return; + } + const run = await r.json(); + state.run = run; + $("#train-id").textContent = `run ${run.id}`; + $("#cancel-train").hidden = false; + subscribeRun(run.id); + } catch (e) { + setStatusBadge("failed"); + appendLog({ line: `launch error: ${e}` }); + $("#run-train").disabled = false; + } +} + +// --- realtime training feedback: phase, progress, result ---------------- +// +// Three surfaces that make a live CPU run legible: a plain-language +// phase line, a step X/N progress bar with an ETA, and a terminal +// outcome banner. Driven by the existing step/log/status SSE events. + +// Maps trl_cpu's well-prefixed log lines to friendly phase labels. +const TRL_PHASES = [ + ["materializing dataset", "Preparing dataset…"], + ["dataset size=", "Preparing dataset…"], + ["split:", "Splitting train / eval…"], + ["loading base model", "Loading base model…"], + ["est_max_steps=", "Planning training run…"], + ["starting trainer.train", "Training…"], + ["training complete", "Saving checkpoint…"], + ["checkpoint at", "Checkpoint written"], +]; + +function _setPhase(text) { + const el = $("#train-phase"); + if (!el) return; + el.hidden = false; + el.textContent = text; +} + +function _narratePhase(line) { + // Surface a friendly phase from a raw trl_cpu log line, if it matches. + if (typeof line !== "string" || !line.includes("[trl_cpu]")) return; + for (const [needle, label] of TRL_PHASES) { + if (line.includes(needle)) { _setPhase(label); return; } + } +} + +function _resetProgress() { + const fill = $("#progress-fill"); + if (fill) { fill.style.width = "0%"; fill.classList.remove("done"); } + const label = $("#progress-label"); + if (label) label.textContent = "step 0 / ? · 0% · ETA --:--"; + const wrap = $("#train-progress"); + if (wrap) wrap.hidden = false; + _setPhase("Waiting to start…"); +} + +function _updateProgress(ev) { + // Called per StepEvent. Fills the bar, computes an ETA from the + // observed per-step cadence, and narrates the live step count. + if (ev.total_steps) state.totalSteps = ev.total_steps; + const wrap = $("#train-progress"); + if (wrap) wrap.hidden = false; + if (state.firstStepTs == null) { + state.firstStepTs = Date.now(); + state.firstStep = ev.step; + } + const total = state.totalSteps; + const pct = total ? Math.min(100, Math.round((ev.step / total) * 100)) : 0; + const fill = $("#progress-fill"); + if (fill && total) fill.style.width = pct + "%"; + let eta = "--:--"; + const stepsDone = ev.step - state.firstStep; + if (total && stepsDone > 0) { + const perStepMs = (Date.now() - state.firstStepTs) / stepsDone; + const remainS = Math.max(0, Math.round((perStepMs * (total - ev.step)) / 1000)); + eta = _formatHMS(remainS).slice(3); // HH:MM:SS → MM:SS + } + const label = $("#progress-label"); + if (label) { + label.textContent = total + ? `step ${ev.step} / ${total} · ${pct}% · ETA ${eta}` + : `step ${ev.step} · ETA --:--`; + } + _setPhase(total + ? `Training — step ${ev.step} of ${total}` + : `Training — step ${ev.step}`); +} + +function _renderResultBanner(status, message) { + // Terminal outcome — assembled from the buffered step data so the + // user reads the result without parsing the raw log. + const el = $("#train-result"); + if (!el) return; + const buf = state.stepBuffer || []; + const first = buf[0]; + const last = buf[buf.length - 1]; + let elapsed = "—"; + if (state.run && state.run.created_at) { + const ms = Date.now() - Date.parse(state.run.created_at); + if (!Number.isNaN(ms)) elapsed = _formatHMS(Math.floor(ms / 1000)); + } + const parts = []; + if (status === "succeeded") { + el.className = "train-result"; + parts.push(`✓ ${buf.length} step${buf.length === 1 ? "" : "s"}`, elapsed); + if (first && last) { + parts.push(`loss ${first.loss.toFixed(2)}→${last.loss.toFixed(2)}`); + } + if (last && last.mean_token_accuracy != null) { + parts.push(`acc ${last.mean_token_accuracy.toFixed(2)}`); + } + if (/checkpoint/i.test(message || "")) parts.push("checkpoint written"); + } else { + el.className = "train-result failed"; + parts.push(`✗ ${status}`); + if (message) parts.push(message); + } + el.textContent = parts.filter(Boolean).join(" · "); + el.hidden = false; +} + +async function discoverActiveRun() { + // Hands-free CPU lane: when the operator autostarts a run + // (MINDXTRAIN_AUTOSTART) the UI must attach to it on page load with no + // button push. Picks the most-recent pending/running run, opens the + // Train card, and subscribes — the SSE ring buffer replays the steps + // and metrics already emitted so nothing is missed. + try { + const runs = await getJSON("/coach/api/runs"); + const active = (runs || []) + .filter((r) => r.status === "running" || r.status === "pending") + .sort((a, b) => String(b.created_at).localeCompare(String(a.created_at)))[0]; + if (!active) return; + state.run = active; + if (!state.recipe) state.recipe = active.recipe; + // Pull the recipe detail so the headline shows the cpu_throttle %. + try { + state.recipeDetail = await getJSON( + `/coach/api/recipes/${encodeURIComponent(active.recipe)}`, + ); + } catch (_e) { /* throttle headline falls back to — */ } + progressTo("step-train"); + $("#train-id").textContent = `run ${active.id} (autostarted · ${active.recipe})`; + $("#train-charts").hidden = false; + $("#train-log-wrap").hidden = false; + $("#cancel-train").hidden = false; + $("#run-train").disabled = true; + setStatusBadge(active.status); + appendLog({ line: `attached to autostarted run ${active.id}` }); + subscribeRun(active.id); + } catch (_e) { + /* no operator runs yet — the normal case on a fresh manual boot */ + } +} + +async function refreshSEADecision() { + // Surface the mindX SEA agent's go/no-go on autonomous training. The + // operator only auto-launches a run when this gate is open; the user + // can always start one by hand with the Run training button. + const wrap = $("#sea-status"); + const pill = $("#sea-status-pill"); + const text = $("#sea-status-text"); + if (!wrap || !pill || !text) return; + try { + const d = await getJSON("/coach/api/sea-decision"); + wrap.hidden = false; + if (!d.autostart_enabled) { + pill.className = "badge-status tier-unknown"; + pill.textContent = "SEA"; + text.textContent = + "Autonomous mode off — start a session with Run training below."; + return; + } + if (d.open) { + pill.className = "badge-status tier-correlated"; + pill.textContent = "SEA · go"; + } else { + pill.className = "badge-status tier-drifted"; + pill.textContent = "SEA · hold"; + } + text.textContent = d.reason || ""; + } catch (_e) { + wrap.hidden = false; + pill.className = "badge-status tier-unknown"; + pill.textContent = "SEA"; + text.textContent = "SEA decision unavailable."; + } +} + +async function cancelTrain() { + if (!state.run) return; + $("#cancel-train").disabled = true; + try { + await fetch(`/coach/api/runs/${state.run.id}/cancel`, { method: "POST" }); + } finally { + $("#cancel-train").disabled = false; + } +} + +function openModelfileBuilder() { + // Open the standalone Modelfile builder in a separate window, pre-filled for + // the current run when available (adapter = its checkpoint, tag = recipe). + const params = new URLSearchParams(); + const run = state.run; + if (run) { + if (run.out_dir) params.set("adapter", `${run.out_dir}/checkpoint`); + const tag = ($("#ollama-push-tag") && $("#ollama-push-tag").value.trim()) || run.recipe || ""; + if (tag) params.set("tag", tag); + } + if (state.chatBackendModel) params.set("from", state.chatBackendModel); + const qs = params.toString(); + window.open(`/coach/modelfile${qs ? "?" + qs : ""}`, "mindxtrain-modelfile", + "width=960,height=900,scrollbars=yes,resizable=yes"); +} + +async function pushTrainedRunToOllama() { + if (!state.run) return; + const btn = $("#push-to-ollama-btn"); + const status = $("#push-to-ollama-status"); + const tag = ($("#ollama-push-tag").value || "").trim(); + btn.disabled = true; + status.textContent = "merging + creating…"; + status.className = "hint"; + // Push log lines stream into the train-log SSE channel — surface them + // so the user isn't watching a frozen button for ~30-60s of merge time. + const logWrap = $("#train-log-wrap"); + if (logWrap) { + logWrap.hidden = false; + logWrap.open = true; + } + try { + // The log lines fire through the train SSE channel — the user is + // already watching #train-log, so the merge progress shows up there. + const registerFallback = ($("#register-fallback") || {}).checked === true; + const body = {}; + if (tag) body.tag = tag; + if (registerFallback) body.register_with_mindx = true; + const r = await fetch( + `/coach/api/runs/${encodeURIComponent(state.run.id)}/push-to-ollama`, + { + method: "POST", + headers: { "content-type": "application/json" }, + body: JSON.stringify(body), + }, + ); + const data = await r.json().catch(() => ({})); + if (!r.ok) { + status.textContent = `failed (${r.status}): ${data.detail || "unknown error"}`; + status.className = "hint bad"; + return; + } + let msg = `pushed: ${data.tag} (${data.merged_dir})`; + if (data.mindx_fallback_swapped && data.mindx_fallback_swap) { + const prev = data.mindx_fallback_swap.previous || "?"; + const cur = data.mindx_fallback_swap.current || "?"; + msg += ` · mindX fallback: ${prev} → ${cur}`; + } else if (registerFallback) { + msg += " · mindX swap failed (see log)"; + } + status.textContent = msg; + status.className = "hint ready"; + // Re-probe so the chat card flips to the freshly pushed model on its + // next status read. + probeChat(); + } catch (e) { + status.textContent = `error: ${e}`; + status.className = "hint bad"; + } finally { + btn.disabled = false; + } +} + +// --- step 4: cost -------------------------------------------------------- + +async function runCost() { + const gpus = parseInt($("#cost-gpus").value, 10); + const hours = parseFloat($("#cost-hours").value); + const res = await postJSON("/coach/api/cost", { gpus, hours, safety_margin: 1.15 }); + const tbody = $("#cost-table tbody"); + tbody.innerHTML = ""; + for (const row of [res.mi300x, res.h100, res.h200]) { + const tr = document.createElement("tr"); + const fits = row.fits_qwen3_8b_bf16_bs8_seq4096; + tr.innerHTML = ` + <td>${row.name}</td> + <td>$${row.rate_usdc_per_hour.toFixed(2)}</td> + <td>${row.gpus}</td> + <td>${res.hours}</td> + <td><strong>$${row.cost_usdc.toFixed(2)}</strong></td> + <td class="fits-${fits ? "yes" : "no"}">${fits ? "✓ " : "✗ "}${row.note}</td> + `; + tbody.appendChild(tr); + } + $("#cost-table").hidden = false; + $("#cost-headline").hidden = false; + $("#cost-headline").innerHTML = + `MI300X is <strong>${res.speedup_vs_h100_x.toFixed(2)}×</strong> cheaper than the H100 baseline for this workload.`; +} + +// --- step 6: deploy (github push + droplet provision/sync) ------------- + +// Generic SSE attachment used by all three deploy cards. Returns the EventSource +// so the caller can keep a reference for cancellation. +function attachDeployStream(runId, opts) { + const { logEl, badgeEl, cancelBtn, runBtn, onTerminal } = opts; + const es = new EventSource(`/coach/api/runs/${runId}/events`); + const setBadge = (label) => { + badgeEl.textContent = label; + badgeEl.className = "badge-status " + label.toLowerCase().replace(/\s+/g, "-"); + badgeEl.hidden = false; + }; + setBadge("running"); + logEl.hidden = false; + logEl.textContent = ""; + let lineCount = 0; + const append = (line) => { + logEl.appendChild(document.createTextNode(line + "\n")); + lineCount += 1; + if (lineCount > MAX_LOG_LINES) { + while (lineCount > MAX_LOG_LINES && logEl.firstChild) { + logEl.removeChild(logEl.firstChild); + lineCount -= 1; + } + } + if (lineCount % 50 === 0) { + _foldLogElement(logEl); + } + logEl.scrollTop = logEl.scrollHeight; + }; + es.addEventListener("log", (e) => append(JSON.parse(e.data).line)); + es.addEventListener("status", (e) => { + const ev = JSON.parse(e.data); + setBadge(ev.status); + const terminal = ["succeeded", "failed", "cancelled"].includes(ev.status); + if (terminal) { + es.close(); + if (cancelBtn) cancelBtn.hidden = true; + if (runBtn) runBtn.disabled = false; + if (onTerminal) onTerminal(ev); + } + }); + es.addEventListener("error", () => setBadge("disconnected")); + return es; +} + +const deploy = { + github: { es: null, runId: null }, + provision: { es: null, runId: null }, + sync: { es: null, runId: null }, +}; + +function fmtMissing(missing) { + if (!missing || !missing.length) return ""; + return `set ${missing.join(", ")} in .env`; +} + +async function refreshGithubStatus() { + try { + const s = await getJSON("/coach/api/github/status"); + const target = $("#github-target"); + const status = $("#github-status"); + const button = $("#run-github"); + if (s.configured) { + status.textContent = "ready"; + status.className = "hint deploy-status ready"; + target.textContent = `→ github.com/${s.target}`; + button.disabled = false; + } else { + status.textContent = fmtMissing(s.missing) || "not configured"; + status.className = "hint deploy-status notready"; + target.textContent = ""; + button.disabled = true; + } + } catch (e) { + $("#github-status").textContent = `status probe failed: ${e}`; + } +} + +async function refreshDropletStatus() { + try { + const s = await getJSON("/coach/api/droplet/status"); + // Provision card. + const pStatus = $("#provision-status"), pTarget = $("#provision-target"), pBtn = $("#run-provision"); + if (s.provision.configured) { + pStatus.textContent = "ready"; + pStatus.className = "hint deploy-status ready"; + pTarget.textContent = `→ ${s.provision.target}`; + pBtn.disabled = false; + } else { + pStatus.textContent = fmtMissing(s.provision.missing) || "not configured"; + pStatus.className = "hint deploy-status notready"; + pTarget.textContent = ""; + pBtn.disabled = true; + } + // Sync card. + const sStatus = $("#sync-status"), sTarget = $("#sync-target"), sBtn = $("#run-sync"); + if (s.sync.configured) { + sStatus.textContent = "ready"; + sStatus.className = "hint deploy-status ready"; + sTarget.textContent = `→ ${s.sync.target}`; + sBtn.disabled = false; + } else { + sStatus.textContent = fmtMissing(s.sync.missing) || "not configured"; + sStatus.className = "hint deploy-status notready"; + sTarget.textContent = ""; + sBtn.disabled = true; + } + } catch (e) { + $("#provision-status").textContent = `status probe failed: ${e}`; + $("#sync-status").textContent = `status probe failed: ${e}`; + } +} + +async function runDeploy({ url, body, slot, runBtn, cancelBtn, logEl, badgeEl, onSuccess, onStart }) { + runBtn.disabled = true; + cancelBtn.hidden = false; + try { + const r = await fetch(url, { + method: "POST", + headers: { "content-type": "application/json" }, + body: JSON.stringify(body || {}), + }); + if (!r.ok) { + const text = await r.text(); + badgeEl.textContent = "failed"; + badgeEl.className = "badge-status failed"; + badgeEl.hidden = false; + logEl.hidden = false; + logEl.textContent = `${r.status}: ${text}`; + runBtn.disabled = false; + cancelBtn.hidden = true; + return; + } + const run = await r.json(); + deploy[slot].runId = run.id; + deploy[slot].es = attachDeployStream(run.id, { + logEl, badgeEl, cancelBtn, runBtn, + onTerminal: (ev) => { + deploy[slot].es = null; + if (ev.status === "succeeded" && typeof onSuccess === "function") { + onSuccess(); + } + }, + }); + // onStart fires after the run-id is known. Used by provision-with-recipe + // to bind the Train card's SSE to the same run before training begins + // streaming events, so the loss chart populates in real time. + if (typeof onStart === "function") { + try { onStart(); } catch (_) { /* non-fatal */ } + } + } catch (e) { + badgeEl.textContent = "failed"; + badgeEl.className = "badge-status failed"; + badgeEl.hidden = false; + logEl.hidden = false; + logEl.textContent = String(e); + runBtn.disabled = false; + cancelBtn.hidden = true; + } +} + +async function cancelDeploy(slot, cancelBtn) { + const runId = deploy[slot].runId; + if (!runId) return; + cancelBtn.disabled = true; + try { + await fetch(`/coach/api/runs/${runId}/cancel`, { method: "POST" }); + } finally { + cancelBtn.disabled = false; + } +} + +function runGithubPush() { + return runDeploy({ + url: "/coach/api/github/push", + body: { force: $("#github-force").checked }, + slot: "github", + runBtn: $("#run-github"), + cancelBtn: $("#cancel-github"), + logEl: $("#github-log"), + badgeEl: $("#github-badge"), + onSuccess: () => { + // Focus the next deploy action — provision is the production path on + // a fresh MI300X; sync is the alternative if the user already has one. + const pBtn = $("#run-provision"); + if (pBtn && !pBtn.disabled) pBtn.focus(); + }, + }); +} + +function runDropletProvision() { + // Pass the picked recipe through so cloud-init runs `mindxtrain train` + // and the orchestrator bridges its log into this run's SSE stream. If + // the user somehow reaches Provision without picking a recipe, we still + // provision (bench-only) — the API treats recipe as optional. + const body = state.recipe ? { recipe: state.recipe } : {}; + return runDeploy({ + url: "/coach/api/droplet/provision", + body, + slot: "provision", + runBtn: $("#run-provision"), + cancelBtn: $("#cancel-provision"), + logEl: $("#provision-log"), + badgeEl: $("#provision-badge"), + onStart: () => { + // The provision run-id is also where training events will land, so + // open the Train card immediately and bind its SSE to this run. The + // loss chart will populate as soon as the droplet starts training. + const runId = deploy.provision.runId; + if (!runId) return; + markCardDone("step-deploy"); + progressTo("step-train"); + $("#train-id").textContent = `run ${runId} (remote MI300X)`; + $("#train-charts").hidden = false; + $("#train-log-wrap").hidden = false; + subscribeRun(runId); + }, + }); +} + +function runDropletSync() { + const body = state.recipe ? { recipe: state.recipe } : {}; + return runDeploy({ + url: "/coach/api/droplet/sync", + body, + slot: "sync", + runBtn: $("#run-sync"), + cancelBtn: $("#cancel-sync"), + logEl: $("#sync-log"), + badgeEl: $("#sync-badge"), + onSuccess: () => { + markCardDone("step-deploy"); + progressTo("step-train"); + }, + }); +} + +// --- step 7: chat (gated on backend health) ------------------------------ + +async function probeChat() { + // Backend badge + ollama status + the model list that drives the chat. + try { + const h = await getJSON("/coach/api/health"); + state.chatBackendModel = h.chat_backend_model || ""; + _updateBackendBadge(h); + } catch (e) { + _updateBackendBadge({ chat_backend_ready: false, chat_backend_name: "" }); + } + await refreshOllamaStatus(); + await loadChatModels(); +} + +async function refreshOllamaStatus() { + const el = $("#ollama-status"); + if (!el) return; + try { + const s = await getJSON("/coach/api/ollama/status"); + state.ollamaReachable = s.reachable; + el.textContent = s.reachable + ? `ollama: running (${(s.serve_pids || []).length || 1} proc)` + : (s.has_ollama_bin ? "ollama: stopped" : "ollama: not installed"); + el.className = "hint " + (s.reachable ? "ready" : "notready"); + const startBtn = $("#ollama-start"); + const stopBtn = $("#ollama-stop"); + if (startBtn) startBtn.hidden = s.reachable; + if (stopBtn) stopBtn.hidden = !s.reachable; + } catch (e) { + el.textContent = "ollama: ?"; + } +} + +async function loadChatModels() { + const sel = $("#chat-model"); + const status = $("#chat-status"); + if (!sel) return; + let models = []; + try { models = (await getJSON("/coach/api/models")).models || []; } catch (e) { /* offline */ } + // Preserve the user's current choice across the 30 s re-probe. + const previous = sel.value; + sel.innerHTML = ""; + if (!models.length) { + if ($("#chat-disabled-msg")) $("#chat-disabled-msg").hidden = false; + if ($("#chat-send")) $("#chat-send").disabled = true; + if (status) { status.textContent = "no model"; status.className = "hint notready"; } + return; + } + for (const id of models) { + const o = document.createElement("option"); + o.value = id; o.textContent = id; + sel.appendChild(o); + } + // Keep the user's selection if still present; else the detected backend model; + // else the first (local-first, sorted server-side). + if (previous && models.includes(previous)) { + sel.value = previous; + } else if (state.chatBackendModel && models.includes(state.chatBackendModel)) { + sel.value = state.chatBackendModel; + } + if ($("#chat-disabled-msg")) $("#chat-disabled-msg").hidden = true; + if ($("#chat-send")) $("#chat-send").disabled = false; + if (status) { status.textContent = `${models.length} model(s) ready`; status.className = "hint ready"; } +} + +async function startOllama() { + const el = $("#ollama-status"); + if (el) el.textContent = "ollama: starting…"; + try { await postJSON("/coach/api/ollama/start", {}); } catch (e) { /* report via status */ } + setTimeout(probeChat, 1500); +} + +async function stopOllama() { + const el = $("#ollama-status"); + if (el) el.textContent = "ollama: stopping…"; + try { await postJSON("/coach/api/ollama/stop", {}); } catch (e) { /* report via status */ } + setTimeout(probeChat, 800); +} + +function _updateBackendBadge(h) { + const badge = $("#backend-badge"); + if (!badge) return; + if (h && h.chat_backend_ready) { + const tail = h.chat_backend_model ? ` (${h.chat_backend_model})` : ""; + badge.textContent = `${h.chat_backend_name}${tail} live`; + badge.className = "backend-badge ready"; + } else { + const name = (h && h.chat_backend_name) || "no backend"; + badge.textContent = `${name} cold`; + badge.className = "backend-badge notready"; + } +} + +function _startChatBackendPolling() { + // Re-probe every 30s so the chat card flips live if the user starts + // ollama or vLLM after page-load. Also re-probe on tab focus — the + // user often switches to a terminal, starts the daemon, then flips + // back expecting the UI to know. + setInterval(() => { probeChat(); }, 30000); + window.addEventListener("focus", () => { probeChat(); }); +} + +let _chatHistory = []; +let _chatAbort = null; + +function _appendChatMsg(role, text) { + const t = $("#chat-transcript"); + t.hidden = false; + const div = document.createElement("div"); + div.className = "chat-msg chat-" + role; + div.innerHTML = `<span class="chat-role">${role}</span><span class="chat-text"></span>`; + div.querySelector(".chat-text").textContent = text; + t.appendChild(div); + t.scrollTop = t.scrollHeight; + return div.querySelector(".chat-text"); +} + +async function sendChat() { + const inputEl = $("#chat-input"); + const input = inputEl.value.trim(); + if (!input) return; + const model = $("#chat-model") ? $("#chat-model").value : ""; + if (!model) { $("#chat-status").textContent = "pick a model first"; return; } + + inputEl.value = ""; + _appendChatMsg("user", input); + _chatHistory.push({ role: "user", content: input }); + const out = _appendChatMsg("assistant", "thinking…"); + $("#chat-send").disabled = true; + $("#chat-stop").hidden = false; + _chatAbort = new AbortController(); + let acc = ""; + try { + const resp = await fetch("/coach/api/chat/stream", { + method: "POST", + headers: { "content-type": "application/json" }, + body: JSON.stringify({ model, messages: _chatHistory, max_tokens: 384 }), + signal: _chatAbort.signal, + }); + // Consume the SSE text stream (AI-SDK textStream pattern) token-by-token. + const reader = resp.body.getReader(); + const dec = new TextDecoder(); + let buf = ""; + let stop = false; + while (!stop) { + const { done, value } = await reader.read(); + if (done) break; + buf += dec.decode(value, { stream: true }); + let nl; + while ((nl = buf.indexOf("\n\n")) >= 0) { + const frame = buf.slice(0, nl); + buf = buf.slice(nl + 2); + let isErr = false; + for (const line of frame.split("\n")) { + if (line.startsWith("event: error")) isErr = true; + if (!line.startsWith("data:")) continue; + const data = line.slice(5).trim(); + if (data === "[DONE]") { stop = true; break; } + try { + const piece = JSON.parse(data); + if (isErr) { out.textContent = "⚠ " + piece; } + else { acc += piece; out.textContent = acc; } + } catch (e) { /* skip unparseable frame */ } + } + $("#chat-transcript").scrollTop = $("#chat-transcript").scrollHeight; + } + } + if (acc) _chatHistory.push({ role: "assistant", content: acc }); + else if (out.textContent === "…") out.textContent = "(no content — try a larger model)"; + } catch (e) { + if (e.name !== "AbortError") out.textContent = `⚠ ${e}`; + } finally { + $("#chat-send").disabled = false; + $("#chat-stop").hidden = true; + _chatAbort = null; + } +} + +function stopChat() { + if (_chatAbort) _chatAbort.abort(); +} + +// --- step 7: MEI card ----------------------------------------------------- + +const MEI_AXIS_LABELS = ["Q", "Dt", "Pp", "M", "E"]; +const MEI_AXIS_FULL = [ + "Quality", "Decode throughput", "Prefill throughput", "Memory", "Energy", +]; + +function _ensureMEIRadar(axes) { + if (state.meiChart || typeof Chart === "undefined") return state.meiChart; + const ctx = $("#mei-radar").getContext("2d"); + state.meiChart = new Chart(ctx, { + type: "radar", + data: { + labels: MEI_AXIS_FULL, + datasets: [{ + label: "MEI sub-indices", + data: axes, + borderColor: "#f7921e", + backgroundColor: "rgba(247, 146, 30, 0.18)", + borderWidth: 2, + pointBackgroundColor: "#f7921e", + }], + }, + options: { + responsive: true, + animation: false, + scales: { + r: { + min: 0, + max: 1, + ticks: { stepSize: 0.2, color: "#8b949e", backdropColor: "transparent" }, + grid: { color: "#30363d" }, + angleLines: { color: "#30363d" }, + pointLabels: { color: "#e6edf3", font: { size: 11 } }, + }, + }, + plugins: { legend: { display: false } }, + }, + }); + return state.meiChart; +} + +function _renderMEISubindices(view) { + const ul = $("#mei-subindex-list"); + ul.innerHTML = ""; + const fields = [ + ["quality", view.quality], + ["decode_throughput", view.decode_throughput], + ["prefill_throughput", view.prefill_throughput], + ["memory", view.memory], + ["energy", view.energy], + ]; + for (const [k, v] of fields) { + const li = document.createElement("li"); + li.innerHTML = `<code>${k}</code>${v.toFixed(3)}`; + ul.appendChild(li); + } +} + +function _renderMEIPromotion(view) { + const promoteBtn = $("#mei-promote"); + const reasonsList = $("#mei-reasons"); + promoteBtn.disabled = !view.promotable; + if (view.promotable) { + reasonsList.hidden = true; + reasonsList.innerHTML = ""; + } else { + reasonsList.hidden = false; + reasonsList.innerHTML = ""; + for (const r of view.promotion_reasons) { + const li = document.createElement("li"); + li.textContent = r; + reasonsList.appendChild(li); + } + } +} + +function _renderMEINotes(view) { + const ul = $("#mei-notes"); + ul.innerHTML = ""; + if (!view.notes || !view.notes.length) { + ul.hidden = true; + return; + } + ul.hidden = false; + for (const n of view.notes) { + const li = document.createElement("li"); + li.textContent = n; + ul.appendChild(li); + } +} + +async function loadMEIForRun(runId) { + const summary = $("#mei-summary"); + const body = $("#mei-card-body"); + if (!runId) { + summary.textContent = "no active run"; + return; + } + summary.textContent = `loading score for run ${runId}…`; + try { + const view = await getJSON(`/coach/api/mei/score/${encodeURIComponent(runId)}`); + state.mei = view; + body.hidden = false; + $("#mei-composite").textContent = view.composite.toFixed(3); + $("#mei-provisional-flag").hidden = !view.mab_provisional; + summary.textContent = view.promotable + ? "promotable to AgenticPlace" + : "below promotion gate — see reasons"; + const axes = [ + view.quality, view.decode_throughput, view.prefill_throughput, + view.memory, view.energy, + ]; + const chart = _ensureMEIRadar(axes); + if (chart) { + chart.data.datasets[0].data = axes; + chart.update("none"); + } + _renderMEISubindices(view); + _renderMEINotes(view); + _renderMEIPromotion(view); + refreshMEIHistory(); + } catch (e) { + summary.textContent = `no MEI score for ${runId} yet — score the run via \`mindxtrain mei score\``; + body.hidden = true; + } +} + +// Short BLAKE3 view: first 8 + last 8 hex chars, or a dash when absent. +function _shortHash(h) { + if (!h) return "—"; + return h.length > 20 ? `${h.slice(0, 8)}…${h.slice(-8)}` : h; +} + +const RECEIPT_HASH_LABELS = { + config_yaml: "config", + checkpoint: "checkpoint", + autotune_plan: "autotune plan", + dataset: "dataset", + eval_json: "eval", +}; + +async function loadReceiptForRun(runId) { + const empty = $("#receipt-empty"); + const body = $("#receipt-card-body"); + const badge = $("#receipt-badge"); + const list = $("#receipt-hashes"); + if (!runId) { + if (empty) empty.textContent = "no active run"; + return; + } + try { + const view = await getJSON(`/coach/api/receipt/${encodeURIComponent(runId)}`); + state.receipt = view; + if (empty) empty.hidden = true; + if (body) body.hidden = false; + if (badge) { + badge.hidden = false; + badge.textContent = view.verified ? "verified ✓" : "unverified"; + badge.className = "badge-status " + (view.verified ? "succeeded" : "failed"); + } + if (list) { + list.innerHTML = ""; + for (const [field, value] of Object.entries(view.hashes)) { + const ok = view.checks[field]; + const li = document.createElement("li"); + const label = RECEIPT_HASH_LABELS[field] || field; + const mark = value ? (ok ? "✓" : "✗") : "·"; + li.innerHTML = + `<code>${mark} ${label}</code>` + + `<span class="mono">${_shortHash(value)}</span>`; + list.appendChild(li); + } + } + } catch (e) { + // 409 = run finished but no manifest (rare best-effort emit failure); + // 404 = unknown run. Either way, leave the card in its waiting state. + if (empty) { + empty.hidden = false; + empty.textContent = "no receipt for this run yet"; + } + if (body) body.hidden = true; + } +} + +// --- create script (dataset authoring) ----------------------------------- + +function _parseExchanges(text) { + const out = []; + for (const raw of (text || "").split("\n")) { + const line = raw.trim(); + if (!line) continue; + const i = line.indexOf(":::"); + if (i < 0) continue; + const user = line.slice(0, i).trim(); + const assistant = line.slice(i + 3).trim(); + if (user && assistant) out.push({ user, assistant }); + } + return out; +} + +function _linesToList(text) { + return (text || "").split("\n").map((s) => s.trim()).filter(Boolean); +} + +async function seedFromPersona() { + try { + const p = await getJSON("/coach/api/persona"); + if (p.name) $("#ds-persona-name").value = p.name; + if (p.system_prompt) $("#ds-system").value = p.system_prompt; + if (Array.isArray(p.voice_examples)) $("#ds-voice").value = p.voice_examples.join("\n"); + $("#ds-status").textContent = `seeded from persona "${p.name}"`; + } catch (e) { + $("#ds-status").textContent = "no persona available (set MINDXTRAIN_PERSONA_PATH)"; + } +} + +async function loadPersonasAndSkills() { + const sel = $("#ds-persona"); + const skillsHost = $("#ds-skills"); + let body = { personas: [], skills: [] }; + try { body = await getJSON("/coach/api/personas"); } catch (e) { /* offline */ } + if (sel) { + sel.innerHTML = '<option value="">(custom — use fields below)</option>'; + for (const p of body.personas) { + const o = document.createElement("option"); + o.value = p.name; o.textContent = p.label; + sel.appendChild(o); + } + } + if (skillsHost) { + skillsHost.innerHTML = ""; + for (const s of body.skills) { + const lab = document.createElement("label"); + lab.innerHTML = `<input type="checkbox" class="ds-skill" value="${s.name}"> ${s.label}`; + lab.title = s.addendum; + skillsHost.appendChild(lab); + } + } +} + +function _selectedSkills() { + return Array.from(document.querySelectorAll(".ds-skill:checked")).map((c) => c.value); +} + +async function saveScript() { + const status = $("#ds-status"); + const skills = _selectedSkills(); + const persona = ($("#ds-persona") && $("#ds-persona").value) || ""; + const body = { + name: $("#ds-name").value.trim() || "script", + persona, + persona_name: $("#ds-persona-name").value.trim() || "actor", + system_prompt: $("#ds-system").value.trim(), + voice_examples: _linesToList($("#ds-voice").value), + exchanges: _parseExchanges($("#ds-exchanges").value), + skills, + seed_voice: $("#ds-seed-voice").checked, + }; + if (!persona && !skills.length && !body.exchanges.length && + !(body.seed_voice && body.voice_examples.length)) { + status.textContent = "pick a persona/skill, add an exchange, or a voice example"; + return; + } + status.textContent = "saving…"; + try { + const info = await postJSON("/coach/api/datasets", body); + const tp = info.train_params || {}; + status.textContent = `✓ ${info.rows} rows → ${info.path}` + + (tp.epochs ? ` · suggested: ${tp.epochs} epochs, grad_accum ${tp.grad_accum}` : ""); + refreshDatasets(); + } catch (e) { + status.textContent = `save failed: ${e}`; + } +} + +async function refreshDatasets() { + const list = $("#ds-list"); + if (!list) return; + try { + const rows = await getJSON("/coach/api/datasets"); + list.innerHTML = ""; + if (!rows.length) { list.hidden = true; return; } + for (const s of rows) { + const li = document.createElement("li"); + li.innerHTML = `<code>${s.name}</code><span class="mono">${s.rows} rows · ${s.path}</span>`; + list.appendChild(li); + } + list.hidden = false; + } catch (e) { + list.hidden = true; + } +} + +function wireCreateDataset() { + const save = $("#ds-save"); + const seed = $("#ds-seed-persona"); + if (save) save.addEventListener("click", saveScript); + if (seed) seed.addEventListener("click", seedFromPersona); + loadPersonasAndSkills(); + refreshDatasets(); +} + +// --- governance: boardroom (any-N) + dojo (prime-N) ---------------------- + +let _lastBoardSize = 0; + +async function loadBoardroomPresets() { + const sel = $("#br-preset"); + if (!sel) return; + try { + const presets = await getJSON("/coach/api/boardroom/presets"); + sel.innerHTML = ""; + for (const name of Object.keys(presets)) { + const o = document.createElement("option"); + o.value = name; + o.textContent = `${name} (${presets[name].length})`; + o.dataset.roles = JSON.stringify(presets[name]); + sel.appendChild(o); + } + } catch (e) { /* presets unavailable; leave empty */ } +} + +async function loadBoardroomModels() { + const sel = $("#br-model"); + if (!sel) return; + let models = []; + try { + const body = await getJSON("/coach/api/models"); + models = body.models || []; + } catch (e) { /* backend unreachable */ } + sel.innerHTML = ""; + if (!models.length) { + const o = document.createElement("option"); + o.value = ""; o.textContent = "(no backend models — start ollama)"; + sel.appendChild(o); + return; + } + // Prefer a small local (non-cloud) model first. + models.sort((a, b) => (a.includes(":cloud") ? 1 : 0) - (b.includes(":cloud") ? 1 : 0)); + for (const id of models) { + const o = document.createElement("option"); + o.value = id; o.textContent = id; + sel.appendChild(o); + } +} + +function _boardMembers() { + const sel = $("#br-preset"); + const opt = sel && sel.selectedOptions[0]; + const roles = opt ? JSON.parse(opt.dataset.roles || "[]") : ["advocate", "critic", "analyst"]; + const model = $("#br-model").value.trim() || "llama3.2"; + return roles.map((r, i) => ({ id: `${r}-${i}`, role: r, model })); +} + +function _defaultMotion() { + return ($("#br-motion").value.trim()) + || (state.run ? `promote actor ${state.run.id}` : "promote the actor"); +} + +async function conveneBoardroom() { + const status = $("#br-status"); + const members = _boardMembers(); + _lastBoardSize = members.length; + status.textContent = "convening — members deliberating…"; + $("#br-dojo").hidden = true; + try { + const body = await postJSON("/coach/api/boardroom/convene", { + motion: _defaultMotion(), members, + model: $("#br-model").value.trim() || "llama3.2", use_models: true, + }); + const d = body.decision; + const outcome = $("#br-outcome"); + outcome.hidden = false; + outcome.textContent = `${d.outcome.toUpperCase()} — ${d.rationale}`; + const list = $("#br-votes"); + list.hidden = false; + list.innerHTML = ""; + for (const del of body.deliberations) { + const li = document.createElement("li"); + li.innerHTML = `<code>${del.vote} · ${del.role}</code>` + + `<span class="mono">${(del.rationale || del.error || "").slice(0, 140)}</span>`; + list.appendChild(li); + } + status.textContent = `decided: ${d.outcome}`; + if (d.disputed) { + $("#br-dojo").hidden = false; + $("#br-verdict").textContent = "disputed — settle in a prime dojo"; + } + } catch (e) { + status.textContent = `convene failed: ${e} (is a chat backend running?)`; + } +} + +async function settleDojo() { + const v = $("#br-verdict"); + v.textContent = "dojo deliberating…"; + try { + const verdict = await postJSON("/coach/api/dojo/settle", { + motion: _defaultMotion(), size: _lastBoardSize || 3, + model: $("#br-model").value.trim() || "llama3.2", use_models: true, + }); + v.textContent = `dojo (${verdict.judges.length} judges) → ` + + `${verdict.winner.toUpperCase()} (${verdict.approvals}-${verdict.rejections})`; + } catch (e) { + v.textContent = `settle failed: ${e}`; + } +} + +function wireBoardroom() { + const convene = $("#br-convene"); + const settle = $("#br-settle"); + if (convene) convene.addEventListener("click", conveneBoardroom); + if (settle) settle.addEventListener("click", settleDojo); + loadBoardroomPresets(); + loadBoardroomModels(); +} + +async function refreshMEIHistory() { + const wrap = $("#mei-history-wrap"); + const tbody = $("#mei-history-table tbody"); + try { + const rows = await getJSON("/coach/api/mei/history?last=10"); + tbody.innerHTML = ""; + if (!rows.length) { + wrap.hidden = true; + return; + } + wrap.hidden = false; + for (const r of rows) { + const tr = document.createElement("tr"); + const mark = r.promoted + ? '<span class="promoted-mark">★</span>' + : "·"; + tr.innerHTML = ` + <td>${new Date(r.timestamp).toLocaleString()}</td> + <td class="mono">${r.run_id}</td> + <td>${r.model_id}</td> + <td><strong>${r.composite.toFixed(3)}</strong></td> + <td>${mark}</td> + `; + tbody.appendChild(tr); + } + } catch (e) { + wrap.hidden = true; + } +} + +async function promoteCurrentMEI() { + if (!state.mei) return; + const promoteBtn = $("#mei-promote"); + const badge = $("#mei-promote-badge"); + promoteBtn.disabled = true; + badge.hidden = false; + badge.textContent = "promoting…"; + badge.className = "badge-status running"; + try { + const r = await fetch( + `/coach/api/mei/promote/${encodeURIComponent(state.mei.run_id)}`, + { method: "POST" }, + ); + if (!r.ok) { + const text = await r.text(); + badge.textContent = `failed (${r.status})`; + badge.className = "badge-status failed"; + console.error("promote failed:", text); + promoteBtn.disabled = false; + return; + } + const body = await r.json(); + if (body.promoted) { + badge.textContent = "promoted"; + badge.className = "badge-status succeeded"; + promoteBtn.disabled = true; // already promoted; refresh below + refreshMEIHistory(); + } else { + badge.textContent = "blocked"; + badge.className = "badge-status failed"; + const reasonsList = $("#mei-reasons"); + reasonsList.hidden = false; + reasonsList.innerHTML = ""; + for (const reason of body.reasons || []) { + const li = document.createElement("li"); + li.textContent = reason; + reasonsList.appendChild(li); + } + promoteBtn.disabled = false; + } + } catch (e) { + badge.textContent = String(e); + badge.className = "badge-status failed"; + promoteBtn.disabled = false; + } +} + +// --- bootstrap ----------------------------------------------------------- + +window.addEventListener("DOMContentLoaded", () => { + $("#run-preflight").addEventListener("click", runPreflight); + $("#run-hardware").addEventListener("click", runHardware); + + // Admin card: poll only when expanded; pause when collapsed. This + // keeps the operator process idle when nobody's looking at the panel. + const adminCard = $("#step-admin"); + if (adminCard) { + adminCard.addEventListener("toggle", () => { + if (adminCard.open) _adminStartPolling(); + else _adminStopPolling(); + }); + $("#admin-toggle-poll").addEventListener("click", _adminTogglePoll); + $("#admin-clear-firehose").addEventListener("click", _adminClearFirehose); + } + $("#run-bench").addEventListener("click", runBench); + $("#run-compile").addEventListener("click", runCompile); + $("#run-train").addEventListener("click", runTrain); + $("#cancel-train").addEventListener("click", cancelTrain); + const pushBtn = $("#push-to-ollama-btn"); + if (pushBtn) pushBtn.addEventListener("click", pushTrainedRunToOllama); + const mfBtn = $("#open-modelfile-btn"); + if (mfBtn) mfBtn.addEventListener("click", openModelfileBuilder); + // Cost card is hidden for now; keep the handler wired if present. + const costBtn = $("#run-cost"); + if (costBtn) costBtn.addEventListener("click", runCost); + $("#chat-send").addEventListener("click", sendChat); + const chatRecheck = $("#chat-recheck"); + if (chatRecheck) { + chatRecheck.addEventListener("click", () => { + $("#chat-status").textContent = "re-probing…"; + probeChat(); + }); + } + // Streaming chat + ollama controls. + const chatStop = $("#chat-stop"); + if (chatStop) chatStop.addEventListener("click", stopChat); + const chatInput = $("#chat-input"); + if (chatInput) chatInput.addEventListener("keydown", (e) => { + if (e.key === "Enter" && (e.ctrlKey || e.metaKey)) { e.preventDefault(); sendChat(); } + }); + const ollamaStart = $("#ollama-start"); + if (ollamaStart) ollamaStart.addEventListener("click", startOllama); + const ollamaStop = $("#ollama-stop"); + if (ollamaStop) ollamaStop.addEventListener("click", stopOllama); + const ollamaModels = $("#ollama-refresh-models"); + if (ollamaModels) ollamaModels.addEventListener("click", loadChatModels); + $("#mei-refresh").addEventListener("click", () => { + loadMEIForRun(state.run && state.run.id); + }); + $("#mei-promote").addEventListener("click", promoteCurrentMEI); + + // Create script (dataset authoring). + wireCreateDataset(); + + // Governance: boardroom + dojo. + wireBoardroom(); + + // Deploy section. + $("#run-github").addEventListener("click", runGithubPush); + $("#cancel-github").addEventListener("click", () => cancelDeploy("github", $("#cancel-github"))); + $("#run-provision").addEventListener("click", runDropletProvision); + $("#cancel-provision").addEventListener("click", () => cancelDeploy("provision", $("#cancel-provision"))); + $("#run-sync").addEventListener("click", runDropletSync); + $("#cancel-sync").addEventListener("click", () => cancelDeploy("sync", $("#cancel-sync"))); + + // Start the auto-advance chain at the top. Each step's success handler + // calls progressTo(...) for the next step. loadRecipes runs eagerly so + // the recipe list is rendered even if preflight/corpus fail — the user + // can still see what's available. + syncPipelineHeader("step-preflight"); + loadRecipes(); + probeChat(); + _startChatBackendPolling(); + refreshGithubStatus(); + refreshDropletStatus(); + refreshMEIHistory(); + runHardware(); + runPreflight(); + refreshChronos(); + _startChronosPolling(); + // Hands-free: attach to any run the operator autostarted at boot so + // the Train card is live without a button push. + discoverActiveRun(); + // SEA gate — show the mindX agent's autonomous-training verdict and + // re-poll every 30s so a fresh decision file flips the banner live. + refreshSEADecision(); + setInterval(refreshSEADecision, 30000); +}); + + +// --- session metrics (per-training-run system load) --------------------- +// +// Five d3 sparklines + a five-cell mono headline live inside #step-train. +// Data shape: array of MetricsEvent dicts (see runs.py:MetricsEvent), each +// carrying ts/cpu_pct/ram_pct/load_1m/proc_rss_mb/proc_cpu_seconds. + +function _enableSessionMetrics() { + const headline = $("#session-headline"); + const metrics = $("#session-metrics"); + if (headline) headline.hidden = false; + if (metrics) metrics.hidden = false; + _renderSessionThrottle(); + _startElapsedTimer(); +} + +function _setSessionStatus(status) { + const pill = $("#session-status"); + if (!pill) return; + pill.textContent = status; + // Map run status → tier class so the pill picks up the same colour + // tokens defined for the chronos card (correlated/degraded/drifted). + const tier = { + running: "tier-correlated", + succeeded: "tier-correlated", + pending: "tier-unknown", + launching: "tier-unknown", + failed: "tier-drifted", + cancelled: "tier-degraded", + }[status] || "tier-unknown"; + pill.className = `badge-status ${tier}`; +} + +function _renderSessionThrottle() { + // Surface the recipe's cpu_throttle % and, when the host core count is + // known, the resolved "N of M cores" with a row of core pips — the + // same floor((cores*pct)/100) math the trl_cpu backend applies. + const el = $("#session-throttle"); + if (!el) return; + const yaml = (state.recipeDetail || {}).yaml || ""; + const m = yaml.match(/cpu_throttle:[\s\S]{0,200}?percent:\s*(\d+)/); + if (!m) { el.textContent = "—"; return; } + const pct = parseInt(m[1], 10); + const cores = (state.hardware && state.hardware.cpu && state.hardware.cpu.cores) || 0; + el.innerHTML = ""; + const label = document.createElement("span"); + if (cores > 0) { + const threads = Math.max(1, Math.floor((cores * pct) / 100)); + label.textContent = `${pct}% · ${threads} of ${cores} cores `; + el.appendChild(label); + for (let i = 0; i < cores; i += 1) { + const pip = document.createElement("span"); + pip.className = "core-pip" + (i < threads ? " on" : ""); + el.appendChild(pip); + } + } else { + label.textContent = `${pct}%`; + el.appendChild(label); + } +} + +function _startElapsedTimer() { + if (state.metricsTimer) return; + // Wall + cpu-time counter ticks at 1 Hz even before the first sample. + state.metricsTimer = setInterval(() => { + if (!state.run) return; + const startedMs = Date.parse(state.run.created_at || ""); + if (!Number.isNaN(startedMs)) { + const elapsedS = Math.max(0, Math.floor((Date.now() - startedMs) / 1000)); + const wall = $("#session-wall"); + if (wall) wall.textContent = _formatHMS(elapsedS); + } + const last = state.metrics[state.metrics.length - 1]; + if (last) { + const cpu = $("#session-cputime"); + if (cpu) cpu.textContent = _formatHMS(Math.floor(last.proc_cpu_seconds)); + } + }, 1000); +} + +function _stopElapsedTimer() { + if (state.metricsTimer) { + clearInterval(state.metricsTimer); + state.metricsTimer = null; + } +} + +function _formatHMS(seconds) { + const s = Math.max(0, Math.floor(seconds)); + const h = Math.floor(s / 3600); + const m = Math.floor((s % 3600) / 60); + const r = s % 60; + const pad = (n) => String(n).padStart(2, "0"); + return `${pad(h)}:${pad(m)}:${pad(r)}`; +} + +async function _backfillSessionMetrics(runId) { + try { + const body = await getJSON( + `/coach/api/runs/${encodeURIComponent(runId)}/metrics`, + ); + const samples = body.samples || []; + if (samples.length) { + state.metrics = samples.slice(-METRICS_BUFFER_CAP); + _renderSessionSparklines(); + // Update last-loss + cpu-time headline with the freshest sample. + _updateHeadlineFromSample(samples[samples.length - 1]); + } + } catch (_e) { /* graceful — first launch has no samples */ } +} + +function _handleMetricsEvent(ev) { + state.metrics.push(ev); + if (state.metrics.length > METRICS_BUFFER_CAP) { + state.metrics.splice(0, state.metrics.length - METRICS_BUFFER_CAP); + } + _updateHeadlineFromSample(ev); + _renderSessionSparklines(); +} + +function _updateHeadlineFromSample(ev) { + const cpu = $("#session-cputime"); + if (cpu) cpu.textContent = _formatHMS(Math.floor(ev.proc_cpu_seconds)); + // Last-loss comes from the existing step chart (Chart.js), not the + // metrics stream — sync it here so the headline always reflects the + // most recent loss value. + const loss = state.chart && state.chart.data && state.chart.data.datasets[0]; + if (loss && loss.data && loss.data.length) { + const v = loss.data[loss.data.length - 1]; + const lossEl = $("#session-last-loss"); + if (lossEl) lossEl.textContent = (typeof v === "number") ? v.toFixed(4) : "—"; + } +} + +function _handleEvalEvent(ev) { + // Every eval checkpoint streams to the raw log... + const metrics = ev.metrics || {}; + appendLog({ line: `[eval@${ev.step}] ${JSON.stringify(metrics)}` }); + // ...and the headline surfaces the freshest eval signal so model + // quality is visible without scrolling the log. + const el = $("#session-eval"); + if (!el) return; + let label = "loss"; + let val = metrics.eval_loss; + if (typeof val !== "number") { + const entry = Object.entries(metrics).find(([, v]) => typeof v === "number"); + if (entry) { [label, val] = entry; } + } + if (typeof val === "number") { + el.textContent = `${val.toFixed(4)} ${label} @${ev.step}`; + } +} + +function _renderSessionSparklines() { + if (typeof d3 === "undefined") return; + const samples = state.metrics; + _renderSparkline("#spark-cpu", "#spark-cpu-val", samples, + s => s.cpu_pct, v => `${v.toFixed(1)}%`, "#f7921e"); + _renderSparkline("#spark-ram", "#spark-ram-val", samples, + s => s.ram_pct, v => `${v.toFixed(1)}%`, "#2f81f7"); + _renderSparkline("#spark-load", "#spark-load-val", samples, + s => s.load_1m, v => v.toFixed(2), "#2ea043"); + _renderSparkline("#spark-rss", "#spark-rss-val", samples, + s => s.proc_rss_mb, v => `${v.toFixed(0)} MB`, "#f7921e"); + // cpu-s/s: derive a delta-per-second from consecutive samples. + const rates = []; + for (let i = 1; i < samples.length; i += 1) { + const dt = samples[i].ts - samples[i - 1].ts; + const dCpu = samples[i].proc_cpu_seconds - samples[i - 1].proc_cpu_seconds; + rates.push({ + ts: samples[i].ts, + value: dt > 0 ? dCpu / dt : 0, + }); + } + _renderSparkline("#spark-cpurate", "#spark-cpurate-val", rates, + s => s.value, v => v.toFixed(2), "#d29922"); +} + +function _renderSparkline(svgSel, valSel, series, getY, formatVal, color) { + const svg = d3.select(svgSel); + if (svg.empty()) return; + svg.selectAll("*").remove(); + const w = +svg.attr("width") || 240; + const h = +svg.attr("height") || 40; + const pad = 3; + const valEl = document.querySelector(valSel); + if (!series.length) { + if (valEl) valEl.textContent = "—"; + return; + } + const ys = series.map(getY); + const yMin = Math.min(...ys); + const yMax = Math.max(...ys); + const yScale = d3.scaleLinear() + .domain([yMin, Math.max(yMax, yMin + 0.0001)]) + .range([h - pad, pad]); + const xScale = d3.scaleLinear() + .domain([0, Math.max(1, series.length - 1)]) + .range([pad, w - pad]); + const line = d3.line() + .x((_, i) => xScale(i)) + .y((_, i) => yScale(ys[i])) + .curve(d3.curveMonotoneX); + svg.append("path") + .datum(series) + .attr("d", line) + .attr("fill", "none") + .attr("stroke", color) + .attr("stroke-width", 1.5); + if (valEl) valEl.textContent = formatVal(ys[ys.length - 1]); +} + +// --- chronos card -------------------------------------------------------- + +async function refreshChronos() { + const summary = document.getElementById("chronos-summary"); + let body; + try { + body = await getJSON("/coach/api/diagnostics/chronos"); + } catch (e) { + if (summary) summary.textContent = `probe failed: ${e}`; + _renderChronosHeadline({ consensus: "unavailable", utc: "", confidence_ms: 0 }); + return; + } + const pt = body.promised_time || {}; + _renderChronosHeadline(pt); + if (summary) { + const anchors = body.anchor_count != null ? `${body.anchor_count} anchors` : "no anchors"; + summary.textContent = `${pt.consensus || "?"} · ${anchors}`; + } + // Drift sparkline + density bars (d3) — degrades to no-op when d3 absent. + if (typeof d3 !== "undefined") { + _renderChronosDrift(body.drift_history || { buckets: [] }); + _renderChronosDensity(body.anchors || []); + } + // Measurement-confidence cross-check. + try { + const mc = await getJSON("/coach/api/diagnostics/measurement-confidence"); + _renderMeasurementConfidence(mc); + } catch (e) { + /* graceful: chip stays "unknown" */ + } +} + +function _renderChronosHeadline(pt) { + const utcEl = document.getElementById("chronos-utc"); + const consEl = document.getElementById("chronos-consensus"); + const confEl = document.getElementById("chronos-confidence"); + if (utcEl) utcEl.textContent = pt.utc || "—"; + if (consEl) { + consEl.textContent = pt.consensus || "unknown"; + consEl.className = `badge-status tier-${pt.consensus || "unknown"}`; + } + if (confEl) { + const ms = typeof pt.confidence_ms === "number" ? pt.confidence_ms : null; + confEl.textContent = ms !== null ? `± ${ms.toFixed(1)} ms` : "± ? ms"; + } +} + +function _renderChronosDrift(hist) { + const svg = d3.select("#chronos-drift"); + svg.selectAll("*").remove(); + const buckets = hist.buckets || []; + const w = +svg.attr("width") || 320; + const h = +svg.attr("height") || 60; + const pad = 4; + if (buckets.length === 0) { + svg.append("text").attr("x", w / 2).attr("y", h / 2) + .attr("text-anchor", "middle").attr("fill", "#8b949e") + .style("font", "11px ui-monospace, monospace") + .text("no anchors in last 24h"); + return; + } + const xs = d3.scaleLinear() + .domain([0, Math.max(1, buckets.length - 1)]) + .range([pad, w - pad]); + const drifts = buckets.map(b => b.drift_mean_ms); + const yMin = Math.min(0, ...drifts); + const yMax = Math.max(0, ...drifts); + const ys = d3.scaleLinear() + .domain([yMin, yMax]) + .range([h - pad, pad]); + // Zero line. + svg.append("line") + .attr("x1", pad).attr("x2", w - pad) + .attr("y1", ys(0)).attr("y2", ys(0)) + .attr("stroke", "#30363d").attr("stroke-dasharray", "2 3"); + // Drift line. + const line = d3.line() + .x((_, i) => xs(i)).y(d => ys(d.drift_mean_ms)) + .curve(d3.curveMonotoneX); + svg.append("path") + .datum(buckets) + .attr("d", line) + .attr("fill", "none") + .attr("stroke", "#f7921e") + .attr("stroke-width", 1.5); + // Hover dots. + svg.selectAll("circle").data(buckets).enter() + .append("circle") + .attr("cx", (_, i) => xs(i)) + .attr("cy", d => ys(d.drift_mean_ms)) + .attr("r", 1.8) + .attr("fill", "#f7921e"); +} + +function _renderChronosDensity(anchors) { + const svg = d3.select("#chronos-density"); + svg.selectAll("*").remove(); + const w = +svg.attr("width") || 320; + const h = +svg.attr("height") || 60; + const pad = 4; + // Bucket anchors by hour (epoch-hour). Last 24 buckets. + const nowH = Math.floor(Date.now() / 3600000); + const counts = new Array(24).fill(0); + for (const a of anchors) { + const tsMs = (a.captured_at_ns || 0) / 1e6; + if (!tsMs) continue; + const hr = Math.floor(tsMs / 3600000); + const offset = nowH - hr; + if (offset >= 0 && offset < 24) counts[23 - offset] += 1; + } + const maxC = Math.max(1, ...counts); + const barW = (w - pad * 2) / 24; + svg.selectAll("rect").data(counts).enter() + .append("rect") + .attr("x", (_, i) => pad + i * barW) + .attr("y", c => h - pad - (h - 2 * pad) * (c / maxC)) + .attr("width", Math.max(1, barW - 1)) + .attr("height", c => (h - 2 * pad) * (c / maxC)) + .attr("fill", "#2f81f7"); +} + +function _renderMeasurementConfidence(mc) { + const chip = document.getElementById("chronos-mc"); + const detail = document.getElementById("chronos-mc-detail"); + if (!chip) return; + const band = mc.confidence_band || "unknown"; + chip.textContent = band; + chip.className = `badge-status tier-${band}`; + if (detail && mc.ok) { + detail.textContent = + `Δcpu=${mc.cpu_delta_pp}pp Δrss=${mc.rss_delta_mb}MB ` + + `(psutil cpu=${mc.psutil_cpu_pct}% ps cpu=${mc.ps_cpu_pct}%)`; + } else if (detail) { + detail.textContent = ""; + } +} + +function _startChronosPolling() { + // 5s refresh — UI feels live without thrashing the mindX backend. + setInterval(() => { refreshChronos(); }, 5000); +} diff --git a/mindxtrain/operator/coach/static/dcoach.html b/mindxtrain/operator/coach/static/dcoach.html new file mode 100644 index 0000000000000000000000000000000000000000..6538def82f52dec86d39247289c0c96b52e2804a --- /dev/null +++ b/mindxtrain/operator/coach/static/dcoach.html @@ -0,0 +1,143 @@ +<!doctype html> +<html lang="en"> +<head> + <meta charset="utf-8" /> + <meta name="viewport" content="width=device-width, initial-scale=1" /> + <title>dcoach — prove a CPU model recalls its training · mindXtrain + + + + +
+
+ ← Coach + Prompt tools + Modelfile builder +
+

dcoach — prove the training stuck

+

+ Imprint a persona onto a tiny model on CPU, then prove the model + recalls it: the classroom measures recall before vs after + training, the boardroom rules success or failure, and the verdict + feeds the autotune feedback loop that tunes the next run. This is + mindXtrain's first-run proof — and the on-ramp to decentralized training. +

+
+ +
+
+

1 · Imprint & Prove

+
+ + +
+
+ +
+
+ +
+ Advanced parameters +
+
+ + +
+
+ + +
+
+ + +
+
+ + +
+
+
+ + + +
+ + + + + +
+

How mindXtrain fits decentralized training (2026)

+

+ + + +
PrimitivemindXtrainMaps to
+

The networks

+
+

+ Read-only. mindXtrain does not mine on these networks — every one is CUDA-first / + hardware-gated. It exposes a verifiable, payable training surface compatible with + their verification primitives. Source: docs/decentralized-training-deep-dive-2026.md. +

+
+
+ + + + diff --git a/mindxtrain/operator/coach/static/dcoach.js b/mindxtrain/operator/coach/static/dcoach.js new file mode 100644 index 0000000000000000000000000000000000000000..882b34275c358576b17e0be246306f6a6d3e572b --- /dev/null +++ b/mindxtrain/operator/coach/static/dcoach.js @@ -0,0 +1,175 @@ +// dcoach — drive the proof loop (imprint → classroom → boardroom → feedback) and +// render the read-only decentralized-network panel. Plain ES, no build step. +"use strict"; + +const $ = (id) => document.getElementById(id); + +async function loadPersonas() { + try { + const r = await fetch("/coach/api/personas"); + if (!r.ok) return; + const data = await r.json(); + const sel = $("dc-persona"); + sel.innerHTML = ""; + (data.personas || []).forEach((p) => { + const opt = document.createElement("option"); + opt.value = p.name; + opt.textContent = p.label || p.name; + if (p.name === "codephreak") opt.selected = true; + sel.appendChild(opt); + }); + const skills = $("dc-skills"); + skills.innerHTML = ""; + (data.skills || []).forEach((s) => { + const lbl = document.createElement("label"); + const cb = document.createElement("input"); + cb.type = "checkbox"; + cb.value = s.name; + cb.className = "dc-skill"; + lbl.appendChild(cb); + lbl.appendChild(document.createTextNode(" " + (s.label || s.name))); + skills.appendChild(lbl); + }); + } catch (_e) { /* personas optional */ } +} + +async function loadDecentralized() { + try { + const r = await fetch("/coach/api/decentralized"); + if (!r.ok) return; + const data = await r.json(); + $("dc-thesis").textContent = data.thesis || ""; + const fit = $("dc-fit-body"); + fit.innerHTML = ""; + (data.fit || []).forEach((f) => { + const tr = document.createElement("tr"); + tr.innerHTML = + `${esc(f.primitive)}` + + `${esc(f.mindxtrain)}${esc(f.maps_to)}`; + fit.appendChild(tr); + }); + const net = $("dc-net"); + net.innerHTML = ""; + (data.networks || []).forEach((n) => { + const card = document.createElement("div"); + card.className = "dc-net-card"; + card.innerHTML = + `

${esc(n.name)}

` + + `

${esc(n.what)}

` + + `

Hardware: ${esc(n.hardware)}

` + + `

Token: ${esc(n.token)}

` + + `

${esc(n.fit)}

`; + net.appendChild(card); + }); + } catch (_e) { /* panel optional */ } +} + +function esc(s) { + return String(s == null ? "" : s) + .replace(/&/g, "&").replace(//g, ">"); +} + +function addPhase(tag, msg) { + const row = document.createElement("div"); + row.className = "dc-phase"; + row.innerHTML = `${esc(tag)}${esc(msg)}`; + $("dc-phases").appendChild(row); + $("dc-phases").scrollTop = $("dc-phases").scrollHeight; +} + +function fmt(x) { return (typeof x === "number") ? x.toFixed(4) : String(x); } + +function renderResult(res) { + const c = res.classroom || {}; + $("r-before").textContent = fmt(c.before_recall); + $("r-after").textContent = fmt(c.recall); + const delta = c.imprint_delta; + const de = $("r-delta"); + de.textContent = (delta >= 0 ? "+" : "") + fmt(delta); + de.className = "v " + (delta > 0 ? "pass" : "fail"); + const pm = $("r-persona"); + pm.textContent = c.persona_maintained ? "yes" : "no"; + pm.className = "v " + (c.persona_maintained ? "pass" : "fail"); + $("r-board").textContent = res.boardroom_outcome || "–"; + const passed = !!res.passed; + const pe = $("r-passed"); + pe.textContent = passed ? "PASS" : "FAIL"; + pe.className = "v " + (passed ? "pass" : "fail"); + $("r-rationale").textContent = res.boardroom_rationale || ""; + const np = res.next_params || {}; + $("r-next").textContent = + `Autotune feedback → next run: epochs=${np.epochs}, grad_accum=${np.grad_accum}` + + (passed ? " (held — the imprint took)." : " (trains harder — the imprint was weak)."); + $("dc-result").classList.remove("hidden"); +} + +async function run() { + const btn = $("dc-run"); + btn.disabled = true; + $("dc-status").textContent = "training on CPU — this takes a few minutes…"; + $("dc-phases").innerHTML = ""; + $("dc-live").classList.remove("hidden"); + $("dc-result").classList.add("hidden"); + + const skills = Array.from(document.querySelectorAll(".dc-skill")) + .filter((c) => c.checked).map((c) => c.value); + const body = { + persona: $("dc-persona").value, + skills, + base_model: $("dc-base").value.trim() || "HuggingFaceTB/SmolLM2-135M", + board_preset: $("dc-board").value, + board_model: $("dc-board-model").value.trim() || null, + max_new_tokens: parseInt($("dc-tokens").value, 10) || 48, + }; + + try { + const resp = await fetch("/coach/api/dcoach/run", { + method: "POST", + headers: { "Content-Type": "application/json" }, + body: JSON.stringify(body), + }); + if (!resp.ok || !resp.body) { + addPhase("error", "could not start: HTTP " + resp.status); + return; + } + const reader = resp.body.getReader(); + const dec = new TextDecoder(); + let buf = ""; + for (;;) { + const { value, done } = await reader.read(); + if (done) break; + buf += dec.decode(value, { stream: true }); + const lines = buf.split("\n"); + buf = lines.pop(); + for (const line of lines) { + if (!line.startsWith("data:")) continue; + const payload = line.slice(5).trim(); + if (payload === "[DONE]") continue; + let evt; + try { evt = JSON.parse(payload); } catch (_e) { continue; } + if (evt.phase === "result") { + renderResult(evt.result); + $("dc-status").textContent = "done."; + } else if (evt.phase === "error") { + addPhase("error", evt.msg); + $("dc-status").textContent = "failed."; + } else if (evt.phase === "start") { + addPhase("start", "run " + evt.run_id); + } else { + addPhase(evt.phase, evt.msg); + } + } + } + } catch (e) { + addPhase("error", String(e)); + $("dc-status").textContent = "failed."; + } finally { + btn.disabled = false; + } +} + +document.addEventListener("DOMContentLoaded", () => { + loadPersonas(); + loadDecentralized(); + $("dc-run").addEventListener("click", run); +}); diff --git a/mindxtrain/operator/coach/static/index.html b/mindxtrain/operator/coach/static/index.html new file mode 100644 index 0000000000000000000000000000000000000000..fd051fc6ddc42d26dd486064e735bdac7aa24ff6 --- /dev/null +++ b/mindxtrain/operator/coach/static/index.html @@ -0,0 +1,579 @@ + + + + + + mindXtrain Coach + + + +
+

+ mindXtrain Coach + probing… +

+
+
automindXtrain
runtime
+
→
+
mindXtrain
training
+
→
+
custmodel
artifact
+
+

+ Walk the AMD MI300X fine-tuning pipeline end-to-end. + Pick a recipe, watch the 60-second AOT autotune pick kernels, + see the Axolotl YAML compile, then cost it against H100. +

+ +
+ +
+
+ +

⚙ Advanced admin

+ click to expand · live metrics + event firehose + +

+ Live system pressure (load, RAM, disk), the operator process's + own RSS + thread count, the current run roster, and a + cross-run event firehose. Auto-polls while expanded; pauses + when collapsed. +

+
+

Active runs

+ + + + + +
idrecipestatusstartedlast steploss
+

Event firehose (idle)

+ 

+      

+        
+        
+      

+    

+
+    

+      

+        
1 Preflight env

+        checking…
+      

+      

+        Required: AMD Dev Cloud credentials (provision the MI300X droplet),
+        Hugging Face token + username (push the trained checkpoint).
+        Optional: mindX API key (auto-swap fallback), Lighthouse key (IPFS pin).
+        Values are not exposed — the endpoint only reports presence.
+      

+      
    
+      
    
+        
+        
+      
    
+    
    
+
+    
    
+      
    
+        
    ⏱ Promised time (mindX chronos)
    
+        probing…
+      
    
+      
    
+        Every mindX artefact — training manifest, MEI receipt, evolution
+        proposal — is stamped with chronos.agent's promised time:
+        a UTC moment plus a measured confidence interval. The interval
+        comes from the standard deviation of recent transaction
+        anchors (the chain confirmed our event at Tblock
+        while our local clock saw Tlocal). When the spread
+        widens, the consensus tier drops from correlated
+        to degraded to drifted.
+      
    
+      
    
+        
    
+          —
+          probing
+          ± ? ms
+        
    
+        
    
+          
    
+            
    drift over 24h (d3 sparkline)
    
+            
+          
    
+          
    
+            
    anchor density (per hour)
    
+            
+          
    
+        
    
+        
    
+          measurement confidence:
+          unknown
+          
+        
    
+      
    
+    
    
+
+    
    
+      
    
+        
    2 Hardware available
    
+        probing…
+      
    
+      
    
+        Which training lanes are reachable from this host. CPU is always
+        available (the trl_cpu lane); AMD GPUs surface when
+        rocm-smi is on PATH; NVIDIA GPUs surface when
+        nvidia-smi is on PATH. The "recommended lane" is the
+        most capable lane detected and matches the recipe's
+        train.backend field.
+      
    
+      
    
+      
+      
    
+        
+      
    
+    
    
+
+    
    
+      
    
+        
    3 Dream corpus
    
+        waiting for preflight…
+      
    
+      
    
+        The training data is mindX's dream-cycle JSONL output. This step
+        confirms the corpus path is reachable and has examples to train on.
+        Set MINDXTRAIN_MINDX_HOME to point at your mindX checkout.
+      
    
+      
+      
+    
    
+
+    
    
+      
    
+        
    + Create script
    
+        a model is an actor · persona + script → imprint
+      
    
+      
    
+        Author a small script (the training examples) for an
+        actor (the model) in its persona voice.
+        Saved as source: local JSONL under
+        out/datasets/<name>/script.jsonl — point the
+        mindx_persona_imprint_local recipe’s data.path at it,
+        train, then measure the imprint (recall before vs after).
+      
    
+      
    
+        
+        
+        
+        
+        
    
+        
+        
+        
+        
+        
    
+          
+          
+          
+        
    
+        
+      
    
+    
    
+
+    
    
+      
    
+        
    4 Pick a recipe
    
+        default for your hardware
+      
    
+      
    
+      
    
+        other recipes 
+        
    
+      
    
+      
+    
    
+
+    
    
+      
    
+        
    5 Run the autotune probe
    
+        60 s on MI300X · CPU dry-run here
+      
    
+      
    
+        The differentiator. A short on-device micro-benchmark picks
+        Composable Kernel vs Triton SDPA, hipBLASLt heuristics, and the
+        RCCL collective config — static plan, no JIT autotune in
+        production.
+      
    
+      
+      
+      
+    
    
+
+    
    
+      
    
+        
    6 Compile to Axolotl YAML
    
+        requires: recipe + plan
+      
    
+      
    
+        Translate the canonical mindXtrain config plus the autotune plan
+        into the backend-specific YAML the trainer subprocess consumes.
+      
    
+      
+      
+      
+    
    
+
+    
    
+      
    
+        
    7 Train (live)
    
+        requires: compile complete
+      
    
+      
    
+        Spawn the trainer subprocess and watch loss, learning rate, and
+        the raw train.log stream in over Server-Sent Events.
+        On a CPU-only laptop the launch returns a 503 with an install hint;
+        on the MI300X box the loss curve fills in as accelerate launch
+        progresses.
+      
    
+      
    
+        
+        
+        
+        
+      
    
+      
+      
+      
+      
+      
+      
+      
+      
+      
+    
    
+
+    
    
+      
    
+        
    ✓ Verifiable receipt
    
+        
+      
    
+      
    
+        The AOT-only discipline is a verification primitive: binding the frozen
+        AutotunePlan hash to the checkpoint hash proves which compiled
+        backend, GEMM heuristic and RCCL config produced these weights —
+        the bitwise-reproducibility that decentralized-training verifiers
+        (Verde/RepOps) require. This re-hashes the run’s
+        manifest.json against the artifacts on disk.
+      
    
+      
+      
    waiting for a completed run…
    
+    
    
+
+    
    
+      
    
+        
    ⚖ Boardroom
    
+        any-N consensus · dojo settles disputes (prime)
+      
    
+      
    
+        The model is an actor; the classroom graduates it; the
+        boardroom (any number of role-based members) decides whether to
+        promote it. A tie escalates to a dojo — a panel of a
+        prime number of judges, so the dispute always settles with no tie.
+        Members deliberate with real models via the configured chat backend.
+      
    
+      
    
+        
+        
+        
+        
    
+          
+          
+        
    
+        
+        
+        
+      
    
+    
    
+
+    
    
+      
    
+        
    8 mindX Efficiency Index
    
+        waiting for a scored run…
+      
    
+      
    
+        Composite score over five sub-indices — quality, decode
+        throughput, prefill throughput, memory footprint, and energy per
+        useful token — combined via weighted geometric mean
+        (spec §5). A checkpoint must clear MEI ≥ 0.55 with every
+        sub-index ≥ 0.30 to be promotable to AgenticPlace.
+      
    
+      
+      
+    
    
+
+    
+    
+
+    
    
+      
    
+        
    10 Deploy
    
+        push to GitHub · provision MI300X · sync to existing droplet
+      
    
+      
    
+        One-click bridge from this laptop to github.com/professor-codephreak/mindXtrain
+        and onto an AMD Dev Cloud MI300X droplet. GitHub first so the cloud-init
+        bootstrap clones from a deterministic commit; then either provision a
+        fresh droplet (cloud-init runs mindxtrain bench as it boots)
+        or rsync to one you already own. All output streams over SSE.
+      
    
+      
    
+
+        
    
+          
    ① Push to GitHub
    
+          
    checking…
    
+          
    
+          
    
+            
+            
+            
+            
+          
    
+          
+        
    
+
+        
    
+          
    ② Provision MI300X droplet
    
+          
    checking…
    
+          
    
+          
    
+            
+            
+            
+          
    
+          
+        
    
+
+        
    
+          
    ② Sync to existing droplet
    
+          
    checking…
    
+          
    
+          
    
+            
+            
+            
+          
    
+          
+        
    
+
+      
    
+    
    
+
+    
    
+      
    
+        
    11 Try the model
    
+        probing…
+        
+      
    
+      
    
+        Chat with a local model — responses stream token-by-token (the AI-SDK
+        text-stream pattern). Pick a model, or start the ollama server if it isn’t
+        running.
+      
    
+      
    
+        ollama: …
+        
+        
+        
+        
+      
    
+      
+      
+      
    
+        
+        
+        
+      
    
+    
    
+  
    
+
+  
    
+    
    
+      PYTHAI ·
+      © BANKON — all rights preserved ·
+      repo ·
+      OpenAPI ·
+      mindX cognitive runtime × AMD MI300X training
+    
    
+  
    
+
+  
+  
+  
+
+
diff --git a/mindxtrain/operator/coach/static/modelfile.html b/mindxtrain/operator/coach/static/modelfile.html
new file mode 100644
index 0000000000000000000000000000000000000000..95e5e378ad22507bbf8cbf6a8f097017e02eeac3
--- /dev/null
+++ b/mindxtrain/operator/coach/static/modelfile.html
@@ -0,0 +1,73 @@
+
+
+
+  
+  
+  Ollama Modelfile builder — mindXtrain
+  
+  
+
+
+  
    Ollama Modelfile builder
    
+  
    Toggle the instructions + parameters you want, fill the fields, and build a valid
+    Modelfile (spec).
+    Opened from the Coach — pre-filled when launched for a trained checkpoint.
    
+
+  
    
+    
    FROM (required)
    
+    
    
+  
    
+
+  
    
+    
    Instructions
    
+    
    
+  
    
+
+  
    
+    
    PARAMETER (toggle on to include)
    
+    
    
+  
    
+
+  
    
+    
    stop sequences (one per line)
    
+    
    
+  
    
+
+  
    
+    
    MESSAGE examples (one per line: role: content — role = system/user/assistant)
    
+    
    
+  
    
+
+  
    
+    
+    
+    
+  
    
+  
+
+  
    
+    
    Create in ollama
    
+    
    
+      
+      
+      
+    
    
+  
    
+
+  
+
+
diff --git a/mindxtrain/operator/coach/static/modelfile.js b/mindxtrain/operator/coach/static/modelfile.js
new file mode 100644
index 0000000000000000000000000000000000000000..6b4f994ad349dab351fa91bbc56780a9c461cdf5
--- /dev/null
+++ b/mindxtrain/operator/coach/static/modelfile.js
@@ -0,0 +1,152 @@
+"use strict";
+// Standalone Ollama Modelfile builder. Fetches the PARAMETER catalogue, renders a
+// toggle + input per parameter, and POSTs the assembled spec to /coach/api/modelfile/build.
+
+const $ = (s) => document.querySelector(s);
+
+async function getJSON(url) {
+  const r = await fetch(url);
+  if (!r.ok) throw new Error(`${url} → ${r.status}`);
+  return r.json();
+}
+async function postJSON(url, body) {
+  const r = await fetch(url, {
+    method: "POST", headers: { "Content-Type": "application/json" }, body: JSON.stringify(body),
+  });
+  if (!r.ok) throw new Error(`${url} → ${r.status}`);
+  return r.json();
+}
+
+// Toggleable text/area instructions: SYSTEM, TEMPLATE, ADAPTER, LICENSE, REQUIRES.
+const INSTRUCTIONS = [
+  { key: "system", label: "SYSTEM", area: true, ph: "You are Codephreak, augmentic intelligence orchestrator." },
+  { key: "template", label: "TEMPLATE", area: true, ph: "{{ .System }}\n{{ .Prompt }}" },
+  { key: "adapter", label: "ADAPTER", area: false, ph: "./out/runs//checkpoint" },
+  { key: "license", label: "LICENSE", area: true, ph: "Apache-2.0" },
+  { key: "requires", label: "REQUIRES", area: false, ph: "0.5.0" },
+];
+
+function renderInstructions(prefill) {
+  const host = $("#mf-instructions");
+  for (const ins of INSTRUCTIONS) {
+    const wrap = document.createElement("div");
+    wrap.className = "mf-row";
+    const pre = (prefill[ins.key] || "");
+    const field = ins.area
+      ? ``
+      : ``;
+    wrap.innerHTML =
+      `` +
+      `
    ${field}
    `;
+    host.appendChild(wrap);
+  }
+  host.querySelectorAll(".mf-ins-toggle").forEach((cb) => {
+    cb.addEventListener("change", () => {
+      $(`#mf-field-${cb.dataset.key}`).hidden = !cb.checked;
+    });
+  });
+}
+
+async function renderParams() {
+  const host = $("#mf-params");
+  let params = [];
+  try { params = (await getJSON("/coach/api/modelfile/params")).parameters; }
+  catch (e) { host.textContent = "could not load parameters"; return; }
+  for (const p of params) {
+    const div = document.createElement("div");
+    div.className = "mf-param";
+    const inputType = (p.type === "int" || p.type === "float") ? "number" : "text";
+    const step = p.type === "float" ? "0.05" : "1";
+    div.innerHTML =
+      `` +
+      `${p.name}` +
+      `` +
+      `${p.description}`;
+    host.appendChild(div);
+  }
+}
+
+function buildSpec() {
+  const spec = { from_model: $("#mf-from").value.trim(), parameters: {} };
+  // Toggled instructions.
+  for (const ins of INSTRUCTIONS) {
+    const cb = document.querySelector(`.mf-ins-toggle[data-key="${ins.key}"]`);
+    if (cb && cb.checked) {
+      const v = $(`#mf-${ins.key}`).value.trim();
+      if (v) spec[ins.key] = v;
+    }
+  }
+  // Toggled parameters.
+  document.querySelectorAll(".mf-param-toggle").forEach((cb) => {
+    if (!cb.checked) return;
+    const name = cb.dataset.name;
+    const raw = document.querySelector(`.mf-param-val[data-name="${name}"]`).value.trim();
+    if (raw === "") return;
+    spec.parameters[name] = (cb.dataset.type === "int") ? parseInt(raw, 10)
+      : (cb.dataset.type === "float") ? parseFloat(raw) : raw;
+  });
+  // Stop sequences.
+  const stop = $("#mf-stop").value.split("\n").map((s) => s.trim()).filter(Boolean);
+  if (stop.length) spec.stop = stop;
+  // Messages: "role: content".
+  const messages = [];
+  for (const line of $("#mf-messages").value.split("\n")) {
+    const i = line.indexOf(":");
+    if (i < 0) continue;
+    const role = line.slice(0, i).trim().toLowerCase();
+    const content = line.slice(i + 1).trim();
+    if (["system", "user", "assistant"].includes(role) && content) messages.push({ role, content });
+  }
+  if (messages.length) spec.messages = messages;
+  return spec;
+}
+
+async function buildModelfile() {
+  const status = $("#mf-status");
+  const spec = buildSpec();
+  if (!spec.from_model) { status.textContent = "FROM is required"; return; }
+  status.textContent = "building…";
+  try {
+    const body = await postJSON("/coach/api/modelfile/build", spec);
+    $("#mf-output").hidden = false;
+    $("#mf-output").textContent = body.modelfile;
+    status.textContent = "built ✓";
+  } catch (e) { status.textContent = `build failed: ${e}`; }
+}
+
+async function createModel() {
+  const s = $("#mf-create-status");
+  const tag = $("#mf-tag").value.trim();
+  if (!tag) { s.textContent = "tag required"; return; }
+  const spec = buildSpec();
+  if (!spec.from_model) { s.textContent = "FROM is required"; return; }
+  s.textContent = `running ollama create ${tag}…`;
+  try {
+    const res = await postJSON("/coach/api/modelfile/create", { tag, spec });
+    s.textContent = `${res.status}: ${(res.output || "").slice(-200)}`;
+  } catch (e) { s.textContent = `create failed: ${e}`; }
+}
+
+function prefillFromQuery() {
+  const q = new URLSearchParams(location.search);
+  const out = {};
+  for (const k of ["system", "template", "adapter", "license", "requires"]) {
+    if (q.get(k)) out[k] = q.get(k);
+  }
+  if (q.get("from")) $("#mf-from").value = q.get("from");
+  if (q.get("tag")) $("#mf-tag").value = q.get("tag");
+  return out;
+}
+
+window.addEventListener("DOMContentLoaded", async () => {
+  const prefill = prefillFromQuery();
+  renderInstructions(prefill);
+  await renderParams();
+  $("#mf-build").addEventListener("click", buildModelfile);
+  $("#mf-create").addEventListener("click", createModel);
+  $("#mf-copy").addEventListener("click", () => {
+    const t = $("#mf-output").textContent;
+    if (t) navigator.clipboard && navigator.clipboard.writeText(t);
+  });
+});
diff --git a/mindxtrain/operator/coach/static/prompts.html b/mindxtrain/operator/coach/static/prompts.html
new file mode 100644
index 0000000000000000000000000000000000000000..4c6605aca06ad79c2b96c45baef9d7b3d9d2ff95
--- /dev/null
+++ b/mindxtrain/operator/coach/static/prompts.html
@@ -0,0 +1,127 @@
+
+
+
+  
+  
+  Prompt tools — non-permanent training tests · mindXtrain
+  
+  
+
+
+  
    
+    
    
+      ← Coach
+      dcoach
+      Modelfile builder
+    
    
+    
    Prompt tools — test cheap, promote if it wins
    
+    
    
+      Prompting is the cheapest pseudo-training: craft a system prompt + a few
+      demonstrations, run them against a base model (no training), evaluate
+      the outcome with the clean-room eval tools, and only if the result is advantageous
+      make it permanent by baking it into an Ollama Modelfile.
+    
    
+  
    
+
+  
    
+    
    
+      
    1 · Craft the prompt
    
+      
    
+        
+        
+      
    
+      
    
+        
+        
+      
    
+      
    
+        
+        
    
+        
+      
    
+    
    
+
+    
    
+      
    2 · Run a test
    
+      
    
+        
+        
+      
    
+      
    
+        
+        
+      
    
+      
    
+        
+        
    
+      
    
+    
    
+
+    
    
+      
    3 · Evaluate the outcome
    
+      
    
+        
+        
+      
    
+      
    
+        
+        
+      
    
+      
    
+        
+        
+      
    
+      
+      
    
+    
    
+
+    
    
+      
    4 · Make it permanent
    
+      
    If the prompt wins, bake the system prompt + demonstrations into an
+        Ollama Modelfile so the behaviour ships without re-prompting.
    
+      
    
+        
+        
+      
    
+      
    
+        
+        
+      
    
+      
    
+        
+        
    
+      
    
+    
    
+  
    
+
+  
+
+
diff --git a/mindxtrain/operator/coach/static/prompts.js b/mindxtrain/operator/coach/static/prompts.js
new file mode 100644
index 0000000000000000000000000000000000000000..299cdba851ba3bee374225f356866fdd8ff9c80d
--- /dev/null
+++ b/mindxtrain/operator/coach/static/prompts.js
@@ -0,0 +1,206 @@
+// prompt-tools — cheap non-permanent test → evaluate → promote to a Modelfile.
+"use strict";
+
+const $ = (id) => document.getElementById(id);
+let lastResponse = "";
+
+function esc(s) {
+  return String(s == null ? "" : s)
+    .replace(/&/g, "&").replace(//g, ">");
+}
+
+async function loadModels() {
+  try {
+    const r = await fetch("/coach/api/models");
+    if (!r.ok) return;
+    const data = await r.json();
+    const sel = $("pt-model");
+    const models = data.models || data || [];
+    if (!models.length) return;
+    sel.innerHTML = "";
+    models.forEach((m) => {
+      const opt = document.createElement("option");
+      opt.value = m; opt.textContent = m;
+      sel.appendChild(opt);
+    });
+  } catch (_e) { /* keep default */ }
+}
+
+function addShot(user = "", assistant = "") {
+  const row = document.createElement("div");
+  row.className = "pt-shot";
+  row.innerHTML =
+    `` +
+    `` +
+    ``;
+  row.querySelector(".pt-shot-u").value = user;
+  row.querySelector(".pt-shot-a").value = assistant;
+  row.querySelector(".pt-shot-x").addEventListener("click", () => row.remove());
+  $("pt-shots").appendChild(row);
+}
+
+function collectMessages() {
+  const msgs = [];
+  const system = $("pt-system").value.trim();
+  if (system) msgs.push({ role: "system", content: system });
+  document.querySelectorAll(".pt-shot").forEach((row) => {
+    const u = row.querySelector(".pt-shot-u").value.trim();
+    const a = row.querySelector(".pt-shot-a").value.trim();
+    if (u) msgs.push({ role: "user", content: u });
+    if (a) msgs.push({ role: "assistant", content: a });
+  });
+  return msgs;
+}
+
+async function runTest() {
+  const btn = $("pt-run");
+  btn.disabled = true;
+  $("pt-status").textContent = "running…";
+  $("pt-response").textContent = "";
+  lastResponse = "";
+
+  const messages = collectMessages();
+  messages.push({ role: "user", content: $("pt-user").value.trim() });
+  const body = { model: $("pt-model").value, messages, max_tokens: 384, temperature: 0.7 };
+
+  try {
+    const resp = await fetch("/coach/api/chat/stream", {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify(body),
+    });
+    if (!resp.ok || !resp.body) {
+      $("pt-response").textContent = "error: HTTP " + resp.status;
+      return;
+    }
+    const reader = resp.body.getReader();
+    const dec = new TextDecoder();
+    let buf = "";
+    for (;;) {
+      const { value, done } = await reader.read();
+      if (done) break;
+      buf += dec.decode(value, { stream: true });
+      const lines = buf.split("\n");
+      buf = lines.pop();
+      for (const line of lines) {
+        if (!line.startsWith("data:")) continue;
+        const payload = line.slice(5).trim();
+        if (payload === "[DONE]") continue;
+        let tok;
+        try { tok = JSON.parse(payload); } catch (_e) { continue; }
+        if (typeof tok === "string") {
+          lastResponse += tok;
+          $("pt-response").textContent = lastResponse;
+        }
+      }
+    }
+    $("pt-status").textContent = "done.";
+  } catch (e) {
+    $("pt-response").textContent = "error: " + String(e);
+    $("pt-status").textContent = "failed.";
+  } finally {
+    btn.disabled = false;
+  }
+}
+
+function metric(key, val) {
+  const cls = val >= 0.6 ? "pass" : "fail";
+  return `
    ${val.toFixed(3)}
    ` +
+         `
    ${esc(key)}
    `;
+}
+
+async function evaluate() {
+  if (!lastResponse.trim()) { $("pt-verdict").textContent = "Run a test first."; return; }
+  const btn = $("pt-eval");
+  btn.disabled = true;
+  $("pt-verdict").textContent = "scoring…";
+  const body = {
+    query: $("pt-user").value.trim(),
+    response: lastResponse,
+    reference: $("pt-reference").value.trim(),
+    use_judge: $("pt-judge").checked,
+    model: $("pt-model").value,
+    guidelines: $("pt-guidelines").value.trim(),
+  };
+  try {
+    const r = await fetch("/coach/api/eval/prompt", {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify(body),
+    });
+    if (!r.ok) { $("pt-verdict").textContent = "eval error: HTTP " + r.status; return; }
+    const data = await r.json();
+    const box = $("pt-scores");
+    box.innerHTML = "";
+    Object.entries(data.scores || {}).forEach(([k, s]) => {
+      box.insertAdjacentHTML("beforeend", metric(k, s.score));
+    });
+    box.insertAdjacentHTML("beforeend", metric("overall", data.overall));
+    box.classList.remove("hidden");
+    $("pt-verdict").innerHTML = data.advantageous
+      ? `Advantageous (${data.overall}). Worth making permanent.`
+      : `Not advantageous yet (${data.overall}). Tune the prompt and retest.`;
+  } catch (e) {
+    $("pt-verdict").textContent = "eval error: " + String(e);
+  } finally {
+    btn.disabled = false;
+  }
+}
+
+function buildSpec() {
+  const messages = collectMessages().filter((m) => m.role !== "system");
+  return {
+    from_model: $("pt-model").value,
+    system: $("pt-system").value.trim(),
+    messages,
+  };
+}
+
+async function previewModelfile() {
+  try {
+    const r = await fetch("/coach/api/modelfile/build", {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify(buildSpec()),
+    });
+    if (r.ok) { $("pt-modelfile").textContent = (await r.json()).modelfile || ""; }
+  } catch (_e) { /* preview optional */ }
+}
+
+async function promote() {
+  const tag = $("pt-tag").value.trim();
+  if (!tag) { $("pt-promote-status").textContent = "a tag is required."; return; }
+  const btn = $("pt-promote");
+  btn.disabled = true;
+  $("pt-promote-status").textContent = "ollama create…";
+  try {
+    const r = await fetch("/coach/api/modelfile/create", {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify({ tag, spec: buildSpec() }),
+    });
+    const data = await r.json();
+    if (!r.ok) {
+      $("pt-promote-status").textContent = "error: " + (data.detail || r.status);
+    } else {
+      $("pt-promote-status").innerHTML = data.status === "ok"
+        ? `created ${esc(tag)}.`
+        : `${esc(data.status || "error")}: ${esc(data.output || "")}`;
+    }
+  } catch (e) {
+    $("pt-promote-status").textContent = "error: " + String(e);
+  } finally {
+    btn.disabled = false;
+  }
+}
+
+document.addEventListener("DOMContentLoaded", () => {
+  loadModels();
+  addShot();
+  $("pt-add-shot").addEventListener("click", () => addShot());
+  $("pt-run").addEventListener("click", runTest);
+  $("pt-eval").addEventListener("click", evaluate);
+  $("pt-promote").addEventListener("click", () => { previewModelfile(); promote(); });
+  ["pt-system", "pt-model"].forEach((id) =>
+    $(id).addEventListener("change", previewModelfile));
+});
diff --git a/mindxtrain/operator/coach/static/style.css b/mindxtrain/operator/coach/static/style.css
new file mode 100644
index 0000000000000000000000000000000000000000..f224c356973a629bbdd4aa8fe410c197cdffac40
--- /dev/null
+++ b/mindxtrain/operator/coach/static/style.css
@@ -0,0 +1,899 @@
+/* mindXtrain Coach — minimal, dark-friendly. No external CSS framework. */
+
+:root {
+  color-scheme: light dark;
+  --bg: #0d1117;
+  --fg: #e6edf3;
+  --muted: #8b949e;
+  --accent: #f7921e;        /* AMD orange */
+  --accent-2: #2f81f7;      /* link blue */
+  --card: #161b22;
+  --border: #30363d;
+  --code-bg: #010409;
+  --ok: #2ea043;
+  --warn: #d29922;
+  --bad: #f85149;
+}
+
+* { box-sizing: border-box; }
+
+body {
+  margin: 0;
+  font-family: ui-sans-serif, system-ui, -apple-system, sans-serif;
+  background: var(--bg);
+  color: var(--fg);
+  line-height: 1.5;
+}
+
+header { padding: 2rem 1.5rem 1rem; max-width: 1100px; margin: 0 auto; }
+
+h1 { margin: 0 0 0.75rem; font-size: 2rem; }
+h1 .muted { color: var(--accent); font-weight: 400; }
+h2 { margin: 0; font-size: 1.15rem; }
+
+.muted { color: var(--muted); }
+
+.pipeline {
+  display: flex; align-items: center; gap: 0.5rem;
+  margin: 0.75rem 0;
+  font-size: 0.9rem;
+}
+.stage {
+  border: 1px solid var(--border);
+  border-radius: 6px;
+  padding: 0.5rem 0.75rem;
+  background: var(--card);
+  text-align: center;
+  min-width: 8rem;
+}
+.stage.active {
+  border-color: var(--accent);
+  box-shadow: 0 0 0 1px var(--accent) inset;
+}
+.arrow { color: var(--muted); font-size: 1.25rem; }
+
+.lede { color: var(--muted); max-width: 70ch; margin-top: 0.5rem; }
+.coach-nav { margin-top: 0.9rem; display: flex; gap: 1.2rem; flex-wrap: wrap; }
+.coach-nav a { color: var(--accent-2); font-size: 0.88rem; text-decoration: none; }
+.coach-nav a:first-child { color: var(--accent); font-weight: 600; }
+.coach-nav a:hover { text-decoration: underline; }
+
+main {
+  max-width: 1100px; margin: 0 auto; padding: 0 1.5rem 3rem;
+  display: flex; flex-direction: column; gap: 1.25rem;
+}
+
+.card {
+  background: var(--card);
+  border: 1px solid var(--border);
+  border-radius: 8px;
+  padding: 1.25rem 1.5rem;
+  transition: border-color 200ms ease, box-shadow 200ms ease, opacity 200ms ease;
+  scroll-margin-top: 1rem;
+}
+
+/* Inline "Check now"-style re-probe trigger in a card header. */
+.link-button {
+  background: none;
+  border: none;
+  color: var(--accent-2);
+  cursor: pointer;
+  font: inherit;
+  padding: 0;
+  text-decoration: underline;
+}
+.link-button:hover { color: var(--accent); }
+
+/* Auto-advance step states. .active = the step the user is on; .done = a
+   completed step (kept visible for context); neither = pending. */
+.card.active {
+  border-color: var(--accent);
+  box-shadow: 0 0 0 1px var(--accent) inset, 0 4px 18px rgba(247, 146, 30, 0.12);
+}
+
+.card.done {
+  opacity: 0.78;
+  border-color: var(--ok);
+}
+
+.card.done > header h2 .step {
+  background: var(--ok);
+  color: #fff;
+}
+
+.card.done > header h2 .step::before {
+  content: "✓ ";
+  font-weight: 700;
+}
+
+.card:not(.active):not(.done) {
+  opacity: 0.72;
+}
+
+.card:not(.active):not(.done):hover {
+  opacity: 0.95;
+}
+
+.card > header {
+  display: flex; justify-content: space-between; align-items: baseline;
+  padding: 0; margin: 0 0 0.75rem; max-width: none;
+}
+
+.step {
+  display: inline-flex;
+  width: 1.5rem; height: 1.5rem;
+  border-radius: 50%;
+  background: var(--accent);
+  color: #000;
+  align-items: center; justify-content: center;
+  font-size: 0.85rem;
+  font-weight: 600;
+  margin-right: 0.5rem;
+}
+
+.hint { color: var(--muted); font-size: 0.85rem; }
+
+.prose { color: var(--muted); max-width: 75ch; }
+
+button {
+  font: inherit;
+  background: var(--accent);
+  color: #000;
+  border: 0;
+  padding: 0.5rem 1rem;
+  border-radius: 4px;
+  cursor: pointer;
+  font-weight: 600;
+}
+button:disabled {
+  background: var(--border);
+  color: var(--muted);
+  cursor: not-allowed;
+}
+button:hover:not(:disabled) { filter: brightness(1.1); }
+
+input, textarea {
+  font: inherit;
+  background: var(--code-bg);
+  color: var(--fg);
+  border: 1px solid var(--border);
+  border-radius: 4px;
+  padding: 0.4rem 0.6rem;
+}
+input[type="number"] { width: 5rem; }
+textarea { width: 100%; resize: vertical; }
+
+.code {
+  background: var(--code-bg);
+  border: 1px solid var(--border);
+  padding: 0.75rem 1rem;
+  border-radius: 4px;
+  overflow-x: auto;
+  font-family: ui-monospace, SFMono-Regular, Menlo, monospace;
+  font-size: 0.85rem;
+  margin: 0.75rem 0 0;
+  max-height: 24rem;
+}
+
+.grid {
+  display: grid;
+  grid-template-columns: repeat(auto-fill, minmax(280px, 1fr));
+  gap: 0.75rem;
+  margin-bottom: 0.75rem;
+}
+
+.recipe {
+  border: 1px solid var(--border);
+  border-radius: 6px;
+  padding: 0.75rem 0.9rem;
+  background: var(--bg);
+  cursor: pointer;
+  transition: border-color 120ms ease;
+}
+.recipe:hover { border-color: var(--accent-2); }
+.recipe.selected {
+  border-color: var(--accent);
+  background: rgba(247, 146, 30, 0.05);
+}
+/* Training session — tiered metrics panel inside #step-train */
+.sea-status {
+  display: flex;
+  align-items: center;
+  gap: 0.6rem;
+  margin: 0.5rem 0;
+  padding: 0.5rem 0.7rem;
+  border: 1px solid var(--border);
+  border-radius: 6px;
+  background: var(--code-bg);
+  font-size: 0.85rem;
+}
+.sea-status .hint { text-transform: none; letter-spacing: 0; }
+
+.session-headline {
+  display: flex;
+  align-items: center;
+  gap: 0.75rem;
+  flex-wrap: wrap;
+  margin: 0.5rem 0 0.75rem;
+  font-family: ui-monospace, SFMono-Regular, Menlo, monospace;
+  font-size: 0.9rem;
+}
+.session-headline .hint {
+  font-size: 0.7rem;
+  text-transform: uppercase;
+  letter-spacing: 0.05em;
+}
+.session-headline .mono { color: var(--fg); }
+
+/* --- realtime training feedback: phase line, progress bar, result --- */
+.train-phase {
+  margin: 0.35rem 0 0.25rem;
+  font-size: 0.95rem;
+  font-weight: 500;
+  color: var(--fg);
+}
+.train-progress {
+  margin: 0.25rem 0 0.75rem;
+  display: flex;
+  align-items: center;
+  gap: 0.6rem;
+}
+.progress-track {
+  flex: 1;
+  height: 8px;
+  background: var(--border);
+  border-radius: 4px;
+  overflow: hidden;
+}
+.progress-fill {
+  height: 100%;
+  width: 0%;
+  background: var(--accent);
+  transition: width 0.4s ease;
+}
+.progress-fill.done { background: var(--ok); }
+.train-result {
+  margin: 0.75rem 0;
+  padding: 0.6rem 0.9rem;
+  border-radius: 6px;
+  border-left: 4px solid var(--ok);
+  background: var(--code-bg);
+  font-family: ui-monospace, SFMono-Regular, Menlo, monospace;
+  font-size: 0.9rem;
+  color: var(--fg);
+}
+.train-result.failed { border-left-color: var(--bad); }
+.core-pip {
+  display: inline-block;
+  width: 7px;
+  height: 7px;
+  margin-left: 2px;
+  border-radius: 2px;
+  background: var(--border);
+  vertical-align: middle;
+}
+.core-pip.on { background: var(--accent); }
+
+#session-metrics > summary,
+#metrics-table-wrap > summary,
+#train-log-wrap > summary {
+  cursor: pointer;
+  user-select: none;
+  font-size: 0.85rem;
+  color: var(--muted);
+  margin-bottom: 0.5rem;
+}
+#session-metrics > summary:hover,
+#metrics-table-wrap > summary:hover,
+#train-log-wrap > summary:hover { color: var(--fg); }
+#metrics-table-wrap { margin-top: 0.75rem; }
+#chart-window-note { margin: 0.25rem 0 0; }
+
+/* Create-script (dataset authoring) form. */
+.dataset-form { display: flex; flex-direction: column; gap: 0.6rem; }
+.dataset-form label { display: flex; flex-direction: column; gap: 0.25rem; font-size: 0.9rem; }
+.dataset-form textarea {
+  width: 100%;
+  background: var(--bg);
+  color: var(--fg);
+  border: 1px solid var(--border);
+  border-radius: 6px;
+  padding: 0.4rem 0.6rem;
+  font-family: inherit;
+}
+.dataset-form .train-controls { flex-direction: row; align-items: center; flex-wrap: wrap; }
+
+/* Streaming chat transcript. */
+.chat-transcript {
+  max-height: 360px;
+  overflow-y: auto;
+  border: 1px solid var(--border);
+  border-radius: 8px;
+  padding: 0.5rem 0.75rem;
+  margin: 0.5rem 0;
+  display: flex;
+  flex-direction: column;
+  gap: 0.5rem;
+}
+.chat-msg { display: flex; flex-direction: column; gap: 0.15rem; }
+.chat-role {
+  font-size: 0.7rem;
+  text-transform: uppercase;
+  letter-spacing: 0.04em;
+  color: var(--muted);
+}
+.chat-text { white-space: pre-wrap; }
+.chat-msg.chat-user .chat-text { color: var(--accent-2); }
+.chat-msg.chat-assistant .chat-text { color: var(--fg); }
+#step-chat textarea { flex: 1; min-width: 16rem; }
+.metrics-grid {
+  display: grid;
+  grid-template-columns: repeat(auto-fit, minmax(260px, 1fr));
+  gap: 0.75rem;
+  margin: 0.25rem 0 0.75rem;
+}
+.spark-cell h4 {
+  margin: 0 0 0.25rem;
+  font-size: 0.7rem;
+  letter-spacing: 0.05em;
+  text-transform: uppercase;
+  color: var(--muted);
+  font-weight: 600;
+}
+.spark-cell svg {
+  display: block;
+  background: var(--code-bg);
+  border: 1px solid var(--border);
+  border-radius: 4px;
+  width: 100%;
+  max-width: 320px;
+}
+.spark-cell .mono {
+  display: inline-block;
+  margin-top: 0.2rem;
+  font-size: 0.85rem;
+  color: var(--fg);
+}
+
+.chronos-step { background: var(--accent-2); color: #fff; font-size: 0.95rem; }
+.chronos-headline {
+  display: flex;
+  align-items: center;
+  gap: 0.75rem;
+  font-size: 1.1rem;
+  margin: 0.5rem 0 1rem;
+}
+.chronos-headline .mono {
+  font-family: ui-monospace, SFMono-Regular, Menlo, monospace;
+  font-size: 0.95rem;
+}
+.chronos-charts {
+  display: grid;
+  grid-template-columns: repeat(auto-fit, minmax(280px, 1fr));
+  gap: 1rem;
+  margin: 0.75rem 0;
+}
+.chronos-chart-wrap h3 {
+  margin: 0 0 0.25rem;
+  font-size: 0.85rem;
+  text-transform: uppercase;
+  letter-spacing: 0.05em;
+  color: var(--muted);
+  font-weight: 600;
+}
+.chronos-chart-wrap svg {
+  display: block;
+  width: 100%;
+  max-width: 320px;
+  background: var(--code-bg);
+  border: 1px solid var(--border);
+  border-radius: 4px;
+}
+.chronos-confidence-row {
+  display: flex;
+  align-items: center;
+  gap: 0.5rem;
+  margin-top: 0.5rem;
+}
+/* Consensus tier colors — reused for the headline pill */
+.badge-status.tier-correlated, .badge-status.tier-tight {
+  background: rgba(46, 160, 67, 0.18); color: var(--ok);
+}
+.badge-status.tier-degraded, .badge-status.tier-loose {
+  background: rgba(210, 153, 34, 0.18); color: var(--warn);
+}
+.badge-status.tier-drifted, .badge-status.tier-divergent,
+.badge-status.tier-unavailable, .badge-status.tier-offline {
+  background: rgba(248, 81, 73, 0.18); color: var(--bad);
+}
+.badge-status.tier-unknown { background: var(--border); color: var(--muted); }
+
+.backend-badge {
+  display: inline-flex;
+  align-items: center;
+  gap: 0.3rem;
+  font-size: 0.7rem;
+  font-family: ui-monospace, SFMono-Regular, Menlo, monospace;
+  font-weight: 500;
+  letter-spacing: 0.05em;
+  text-transform: uppercase;
+  padding: 0.2rem 0.55rem;
+  border-radius: 999px;
+  vertical-align: middle;
+  margin-left: 0.6rem;
+  border: 1px solid var(--border);
+}
+.backend-badge::before {
+  content: "";
+  display: inline-block;
+  width: 0.5rem; height: 0.5rem;
+  border-radius: 50%;
+  background: var(--muted);
+}
+.backend-badge.ready {
+  color: var(--ok);
+  border-color: rgba(46, 160, 67, 0.4);
+  background: rgba(46, 160, 67, 0.08);
+}
+.backend-badge.ready::before { background: var(--ok); }
+.backend-badge.notready {
+  color: var(--muted);
+  border-color: var(--border);
+  background: rgba(139, 148, 158, 0.08);
+}
+.backend-badge.notready::before { background: var(--warn); }
+
+.recipe.recommended {
+  border-color: var(--accent-2);
+  box-shadow: 0 0 0 2px rgba(247, 146, 30, 0.25), 0 0 12px rgba(247, 146, 30, 0.15);
+  position: relative;
+}
+.recipe.recommended::after {
+  content: "recommended";
+  position: absolute;
+  top: 0.4rem;
+  right: 0.5rem;
+  font-size: 0.65rem;
+  text-transform: uppercase;
+  letter-spacing: 0.05em;
+  color: var(--accent);
+  font-family: ui-monospace, monospace;
+}
+.recipe h3 { margin: 0 0 0.25rem; font-size: 1rem; font-family: ui-monospace, monospace; }
+.recipe .meta { color: var(--muted); font-size: 0.8rem; }
+.recipe .badge {
+  display: inline-block;
+  padding: 0.05rem 0.4rem;
+  border-radius: 3px;
+  background: var(--border);
+  color: var(--fg);
+  font-size: 0.7rem;
+  margin-right: 0.25rem;
+}
+
+.summary {
+  list-style: none;
+  padding: 0;
+  margin: 0.75rem 0 0;
+  display: flex;
+  flex-wrap: wrap;
+  gap: 0.5rem;
+}
+.summary li {
+  background: var(--code-bg);
+  border: 1px solid var(--border);
+  padding: 0.3rem 0.6rem;
+  border-radius: 3px;
+  font-family: ui-monospace, monospace;
+  font-size: 0.8rem;
+}
+
+.cost-controls {
+  display: flex; gap: 1rem; align-items: center; flex-wrap: wrap;
+  margin-bottom: 0.75rem;
+}
+.cost-controls label { display: flex; flex-direction: column; gap: 0.25rem; font-size: 0.85rem; color: var(--muted); }
+
+table {
+  width: 100%;
+  border-collapse: collapse;
+  margin-top: 0.5rem;
+}
+th, td {
+  text-align: left;
+  padding: 0.5rem 0.75rem;
+  border-bottom: 1px solid var(--border);
+  font-size: 0.9rem;
+}
+th { color: var(--muted); font-weight: 500; }
+td.fits-yes { color: var(--ok); }
+td.fits-no  { color: var(--bad); }
+
+.headline {
+  font-size: 1.15rem;
+  margin-top: 1rem;
+  padding: 0.75rem 1rem;
+  border-left: 3px solid var(--accent);
+  background: var(--code-bg);
+}
+
+#step-chat .hint.ready    { color: var(--ok); }
+#step-chat .hint.notready { color: var(--warn); }
+
+/* --- step 4: live training -------------------------------------------- */
+
+.train-controls {
+  display: flex; gap: 0.75rem; align-items: center; flex-wrap: wrap;
+  margin-bottom: 0.75rem;
+}
+.train-controls .mono { font-family: ui-monospace, monospace; font-size: 0.8rem; }
+
+.badge-status {
+  display: inline-block;
+  padding: 0.15rem 0.6rem;
+  border-radius: 3px;
+  font-size: 0.75rem;
+  font-family: ui-monospace, monospace;
+  border: 1px solid var(--border);
+  background: var(--code-bg);
+  color: var(--muted);
+  text-transform: lowercase;
+  letter-spacing: 0.04em;
+}
+.badge-status.running    { color: var(--accent-2); border-color: var(--accent-2); }
+.badge-status.pending,
+.badge-status.launching  { color: var(--warn); border-color: var(--warn); }
+.badge-status.succeeded  { color: var(--ok); border-color: var(--ok); }
+.badge-status.failed,
+.badge-status.cancelled,
+.badge-status.disconnected { color: var(--bad); border-color: var(--bad); }
+
+#cancel-train {
+  background: transparent;
+  color: var(--bad);
+  border: 1px solid var(--bad);
+}
+#cancel-train:hover:not(:disabled) {
+  background: var(--bad);
+  color: #000;
+  filter: none;
+}
+
+.chart-wrap {
+  background: var(--code-bg);
+  border: 1px solid var(--border);
+  border-radius: 4px;
+  padding: 0.75rem;
+  margin: 0.5rem 0;
+}
+.chart-wrap canvas { max-width: 100%; }
+
+#metrics-table {
+  font-family: ui-monospace, monospace;
+  font-size: 0.8rem;
+}
+#metrics-table th, #metrics-table td { padding: 0.25rem 0.6rem; }
+
+.log-tail {
+  max-height: 18rem;
+  overflow-y: auto;
+  white-space: pre;
+}
+
+/* --- step 6: deploy --------------------------------------------------- */
+
+.deploy-grid {
+  display: grid;
+  grid-template-columns: repeat(auto-fit, minmax(310px, 1fr));
+  gap: 0.85rem;
+  margin-top: 0.75rem;
+}
+
+.deploy-card {
+  border: 1px solid var(--border);
+  border-radius: 6px;
+  padding: 0.85rem 1rem;
+  background: var(--bg);
+  display: flex; flex-direction: column;
+}
+.deploy-card h3 {
+  margin: 0 0 0.4rem;
+  font-size: 1rem;
+  font-family: ui-monospace, monospace;
+}
+.deploy-card .deploy-status { margin: 0; }
+.deploy-card .deploy-status.ready    { color: var(--ok); }
+.deploy-card .deploy-status.notready { color: var(--warn); }
+
+.deploy-target {
+  margin: 0.4rem 0 0.6rem;
+  color: var(--muted);
+  font-size: 0.8rem;
+  font-family: ui-monospace, monospace;
+  min-height: 1em;
+  word-break: break-all;
+}
+.deploy-target.mono { font-family: ui-monospace, monospace; }
+
+.deploy-controls {
+  display: flex;
+  align-items: center;
+  gap: 0.5rem;
+  flex-wrap: wrap;
+}
+.deploy-controls .deploy-opt {
+  font-size: 0.75rem;
+  color: var(--muted);
+  display: inline-flex; align-items: center; gap: 0.25rem;
+}
+
+.deploy-card .log-tail {
+  margin-top: 0.6rem;
+  max-height: 14rem;
+}
+
+[id^="cancel-github"], [id^="cancel-provision"], [id^="cancel-sync"] {
+  background: transparent;
+  color: var(--bad);
+  border: 1px solid var(--bad);
+  padding: 0.3rem 0.7rem;
+  font-size: 0.85rem;
+}
+[id^="cancel-github"]:hover:not(:disabled),
+[id^="cancel-provision"]:hover:not(:disabled),
+[id^="cancel-sync"]:hover:not(:disabled) {
+  background: var(--bad);
+  color: #000;
+  filter: none;
+}
+
+/* Advanced admin card -------------------------------------------------- */
+
+.admin-card {
+  border-left: 4px solid var(--accent-2);
+  background: linear-gradient(180deg,
+    rgba(47, 129, 247, 0.06) 0%, var(--card) 30%);
+}
+.admin-card > summary {
+  cursor: pointer;
+  list-style: none;
+  display: flex;
+  justify-content: space-between;
+  align-items: baseline;
+}
+.admin-card > summary::-webkit-details-marker {
+  display: none;
+}
+.admin-card > summary h2 {
+  display: inline-block;
+}
+.admin-step {
+  background: var(--accent-2);
+  color: #fff;
+}
+.admin-metrics-grid {
+  display: grid;
+  grid-template-columns: repeat(auto-fit, minmax(140px, 1fr));
+  gap: 0.5rem;
+  margin: 0.75rem 0;
+}
+.admin-metric {
+  background: var(--bg);
+  border: 1px solid var(--border);
+  border-radius: 6px;
+  padding: 0.5rem 0.75rem;
+}
+.admin-metric-label {
+  font-size: 0.7rem;
+  text-transform: uppercase;
+  letter-spacing: 0.05em;
+  color: var(--muted);
+}
+.admin-metric-value {
+  font-size: 1.2rem;
+  font-weight: 600;
+  font-variant-numeric: tabular-nums;
+}
+.admin-metric.warn { border-color: var(--warn); }
+.admin-metric.warn .admin-metric-value { color: var(--warn); }
+.admin-metric.bad { border-color: var(--bad); }
+.admin-metric.bad .admin-metric-value { color: var(--bad); }
+
+#admin-runs-table {
+  width: 100%;
+  border-collapse: collapse;
+  margin: 0.5rem 0;
+  font-size: 0.85rem;
+}
+#admin-runs-table th, #admin-runs-table td {
+  text-align: left;
+  padding: 0.25rem 0.5rem;
+  border-bottom: 1px solid var(--border);
+}
+#admin-runs-table .status-running { color: var(--accent); }
+#admin-runs-table .status-succeeded { color: var(--ok); }
+#admin-runs-table .status-failed,
+#admin-runs-table .status-cancelled { color: var(--bad); }
+#admin-runs-table .status-pending { color: var(--muted); }
+
+#admin-firehose {
+  font-size: 0.75rem;
+  background: var(--code-bg);
+}
+
+/* Collapsible log fold (.log-tail > details) --------------------------- */
+
+.log-tail details {
+  margin-bottom: 0.5rem;
+  background: rgba(255, 255, 255, 0.03);
+  border-left: 2px solid var(--muted);
+  padding: 0.25rem 0.5rem;
+  border-radius: 4px;
+}
+.log-tail details summary {
+  cursor: pointer;
+  color: var(--muted);
+  font-size: 0.75rem;
+  padding: 0.15rem 0;
+  user-select: none;
+}
+.log-tail details summary:hover {
+  color: var(--fg);
+}
+.log-tail details[open] summary::before {
+  content: "▾ ";
+}
+.log-tail details:not([open]) summary::before {
+  content: "▸ ";
+}
+.log-tail pre.folded {
+  margin: 0.25rem 0 0;
+  padding: 0;
+  font-size: 0.75rem;
+  opacity: 0.85;
+  background: transparent;
+  border: none;
+  white-space: pre-wrap;
+  overflow: visible;
+  max-height: none;
+}
+
+/* Hardware diagnostics card -------------------------------------------- */
+
+.hw-grid {
+  display: grid;
+  grid-template-columns: repeat(auto-fit, minmax(220px, 1fr));
+  gap: 0.75rem;
+  margin: 0.5rem 0 1rem;
+}
+.hw-panel {
+  background: var(--bg);
+  border: 1px solid var(--border);
+  border-radius: 6px;
+  padding: 0.75rem 1rem;
+}
+.hw-panel h3 {
+  margin: 0 0 0.5rem;
+  font-size: 1rem;
+  display: flex;
+  justify-content: space-between;
+  align-items: baseline;
+}
+.hw-panel h3::after {
+  content: attr(data-status);
+  font-size: 0.75rem;
+  text-transform: uppercase;
+  letter-spacing: 0.05em;
+}
+.hw-panel dl {
+  margin: 0;
+  display: grid;
+  grid-template-columns: max-content 1fr;
+  gap: 0.15rem 0.5rem;
+  font-size: 0.85rem;
+}
+.hw-panel dt {
+  color: var(--muted);
+}
+.hw-panel dd {
+  margin: 0;
+  font-family: ui-monospace, "Roboto Mono", monospace;
+  font-size: 0.85rem;
+}
+.hw-panel.hw-ok {
+  border-color: var(--ok);
+}
+.hw-panel.hw-off {
+  opacity: 0.6;
+}
+.hw-note {
+  margin: 0.5rem 0 0;
+  font-style: italic;
+  font-size: 0.8rem;
+}
+
+/* MEI card -------------------------------------------------------------- */
+
+.mei-headline {
+  display: flex;
+  align-items: baseline;
+  gap: 0.75rem;
+  margin: 0.5rem 0 1rem;
+}
+.mei-composite-label {
+  font-size: 0.85rem;
+  text-transform: uppercase;
+  letter-spacing: 0.1em;
+  color: var(--muted);
+}
+.mei-composite-value {
+  font-size: 2rem;
+  font-weight: 700;
+  color: var(--accent);
+  font-variant-numeric: tabular-nums;
+}
+.mei-flag {
+  font-size: 0.75rem;
+  padding: 0.15rem 0.5rem;
+  border-radius: 999px;
+  background: rgba(210, 153, 34, 0.18);
+  color: var(--warn);
+  border: 1px solid var(--warn);
+}
+
+.mei-chart-wrap {
+  max-width: 380px;
+  margin: 0 auto 1rem;
+}
+
+#mei-subindex-list code {
+  display: inline-block;
+  min-width: 5rem;
+  color: var(--muted);
+}
+
+.mei-controls {
+  display: flex;
+  gap: 0.5rem;
+  align-items: center;
+  margin: 0.75rem 0;
+}
+#mei-promote:disabled {
+  opacity: 0.45;
+  cursor: not-allowed;
+}
+#mei-promote:not(:disabled) {
+  background: var(--ok);
+  color: #000;
+  border-color: var(--ok);
+}
+
+#mei-reasons li {
+  color: var(--bad);
+}
+
+#mei-history-table {
+  width: 100%;
+  border-collapse: collapse;
+  margin-top: 0.5rem;
+  font-size: 0.85rem;
+}
+#mei-history-table th,
+#mei-history-table td {
+  text-align: left;
+  padding: 0.3rem 0.5rem;
+  border-bottom: 1px solid var(--border);
+}
+#mei-history-table .promoted-mark {
+  color: var(--ok);
+  font-weight: 700;
+}
+
+footer {
+  max-width: 1100px;
+  margin: 2rem auto;
+  padding: 0 1.5rem;
+  color: var(--muted);
+  font-size: 0.85rem;
+}
+footer a { color: var(--accent-2); }
diff --git a/mindxtrain/operator/coach/static/vendor/VERSIONS.md b/mindxtrain/operator/coach/static/vendor/VERSIONS.md
new file mode 100644
index 0000000000000000000000000000000000000000..419e237f6a0cba8d1ae61690f73b463016a1b659
--- /dev/null
+++ b/mindxtrain/operator/coach/static/vendor/VERSIONS.md
@@ -0,0 +1,68 @@
+# Vendored frontend assets
+
+Pinned third-party JS the Coach UI loads directly from
+`/coach/static/vendor/`. No build step, no npm — these files are checked
+in as-is and served to the browser.
+
+## chart.umd.min.js
+
+| Field | Value |
+|---|---|
+| Library | Chart.js |
+| Version | 4.4.0 |
+| Source | https://cdn.jsdelivr.net/npm/chart.js@4.4.0/dist/chart.umd.min.js |
+| License | MIT |
+| Used by | `coach/static/index.html` (renders the live loss curve in step 4) |
+| Fallback | If the file is missing or fails to load, `coach.js` checks `typeof Chart === "undefined"` and degrades to a metrics-only table — the page does not break. |
+
+The repo treats this file as a vendored binary artifact: do not edit it
+in place. To upgrade, replace the file from the upstream CDN URL above
+and update this row plus the SHA256 below.
+
+### SHA256
+
+| File | Hash |
+|---|---|
+| `chart.umd.min.js` | `0e2326c6868072bec1592760c6729043caeea2960a2b46cee6a2192aac6abff0` |
+| Size | 201 KB |
+| Vendored on | 2026-05-08 |
+
+Verify locally:
+
+```sh
+sha256sum mindxtrain/operator/coach/static/vendor/chart.umd.min.js
+# → 0e2326c6868072bec1592760c6729043caeea2960a2b46cee6a2192aac6abff0  …
+```
+
+To upgrade, replace the file from the upstream CDN URL above and update
+this row.
+
+## d3.v7.min.js
+
+| Field | Value |
+|---|---|
+| Library | D3.js |
+| Version | 7.9.0 |
+| Source | https://cdn.jsdelivr.net/npm/d3@7.9.0/dist/d3.min.js |
+| License | ISC |
+| Used by | `coach/static/index.html` (chronos drift sparkline + 24h anchor density bar in `#step-chronos`) |
+| Fallback | If d3 isn't loaded, `coach.js` checks `typeof d3 === "undefined"` and the chronos card renders text-only (UTC + consensus tier + confidence band) — the page does not break. |
+
+### SHA256
+
+| File | Hash |
+|---|---|
+| `d3.v7.min.js` | `f2094bbf6141b359722c4fe454eb6c4b0f0e42cc10cc7af921fc158fceb86539` |
+| Size | 274 KB |
+| Vendored on | 2026-05-19 |
+
+Verify locally:
+
+```sh
+sha256sum mindxtrain/operator/coach/static/vendor/d3.v7.min.js
+# → f2094bbf6141b359722c4fe454eb6c4b0f0e42cc10cc7af921fc158fceb86539  …
+```
+
+three.js intentionally **not** vendored. The time-drift story is 2D
+(sparkline + density bars) and d3 covers it cleanly. A future
+agent-topology view would justify three.js (~600 KB) on its own merits.
diff --git a/mindxtrain/operator/coach/static/vendor/chart.umd.min.js b/mindxtrain/operator/coach/static/vendor/chart.umd.min.js
new file mode 100644
index 0000000000000000000000000000000000000000..9a07c2f45def422fa70b91aa8ae4f0601935246c
--- /dev/null
+++ b/mindxtrain/operator/coach/static/vendor/chart.umd.min.js
@@ -0,0 +1,20 @@
+/**
+ * Skipped minification because the original files appears to be already minified.
+ * Original file: /npm/chart.js@4.4.0/dist/chart.umd.js
+ *
+ * Do NOT use SRI with dynamically generated files! More information: https://www.jsdelivr.com/using-sri-with-dynamic-files
+ */
+/*!
+ * Chart.js v4.4.0
+ * https://www.chartjs.org
+ * (c) 2023 Chart.js Contributors
+ * Released under the MIT License
+ */
+!function(t,e){"object"==typeof exports&&"undefined"!=typeof module?module.exports=e():"function"==typeof define&&define.amd?define(e):(t="undefined"!=typeof globalThis?globalThis:t||self).Chart=e()}(this,(function(){"use strict";var t=Object.freeze({__proto__:null,get Colors(){return Go},get Decimation(){return Qo},get Filler(){return ma},get Legend(){return ya},get SubTitle(){return ka},get Title(){return Ma},get Tooltip(){return Ba}});function e(){}const i=(()=>{let t=0;return()=>t++})();function s(t){return null==t}function n(t){if(Array.isArray&&Array.isArray(t))return!0;const e=Object.prototype.toString.call(t);return"[object"===e.slice(0,7)&&"Array]"===e.slice(-6)}function o(t){return null!==t&&"[object Object]"===Object.prototype.toString.call(t)}function a(t){return("number"==typeof t||t instanceof Number)&&isFinite(+t)}function r(t,e){return a(t)?t:e}function l(t,e){return void 0===t?e:t}const h=(t,e)=>"string"==typeof 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+ * https://github.com/kurkle/color#readme
+ * (c) 2023 Jukka Kurkela
+ * Released under the MIT License
+ */function _t(t){return t+.5|0}const yt=(t,e,i)=>Math.max(Math.min(t,i),e);function vt(t){return yt(_t(2.55*t),0,255)}function Mt(t){return yt(_t(255*t),0,255)}function wt(t){return yt(_t(t/2.55)/100,0,1)}function kt(t){return yt(_t(100*t),0,100)}const St={0:0,1:1,2:2,3:3,4:4,5:5,6:6,7:7,8:8,9:9,A:10,B:11,C:12,D:13,E:14,F:15,a:10,b:11,c:12,d:13,e:14,f:15},Pt=[..."0123456789ABCDEF"],Dt=t=>Pt[15&t],Ct=t=>Pt[(240&t)>>4]+Pt[15&t],Ot=t=>(240&t)>>4==(15&t);function At(t){var e=(t=>Ot(t.r)&&Ot(t.g)&&Ot(t.b)&&Ot(t.a))(t)?Dt:Ct;return t?"#"+e(t.r)+e(t.g)+e(t.b)+((t,e)=>t<255?e(t):"")(t.a,e):void 0}const Tt=/^(hsla?|hwb|hsv)\(\s*([-+.e\d]+)(?:deg)?[\s,]+([-+.e\d]+)%[\s,]+([-+.e\d]+)%(?:[\s,]+([-+.e\d]+)(%)?)?\s*\)$/;function Lt(t,e,i){const s=e*Math.min(i,1-i),n=(e,n=(e+t/30)%12)=>i-s*Math.max(Math.min(n-3,9-n,1),-1);return[n(0),n(8),n(4)]}function Et(t,e,i){const s=(s,n=(s+t/60)%6)=>i-i*e*Math.max(Math.min(n,4-n,1),0);return[s(5),s(3),s(1)]}function Rt(t,e,i){const s=Lt(t,1,.5);let 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window.devicePixelRatio}getMaximumSize(t,e,i,s){return we(t,e,i,s)}isAttached(t){const e=ge(t);return!(!e||!e.isConnected)}}function ks(t){return!fe()||"undefined"!=typeof OffscreenCanvas&&t instanceof OffscreenCanvas?ls:ws}var Ss=Object.freeze({__proto__:null,BasePlatform:rs,BasicPlatform:ls,DomPlatform:ws,_detectPlatform:ks});const Ps="transparent",Ds={boolean:(t,e,i)=>i>.5?e:t,color(t,e,i){const s=Qt(t||Ps),n=s.valid&&Qt(e||Ps);return n&&n.valid?n.mix(s,i).hexString():e},number:(t,e,i)=>t+(e-t)*i};class Cs{constructor(t,e,i,s){const n=e[i];s=Pi([t.to,s,n,t.from]);const o=Pi([t.from,n,s]);this._active=!0,this._fn=t.fn||Ds[t.type||typeof o],this._easing=fi[t.easing]||fi.linear,this._start=Math.floor(Date.now()+(t.delay||0)),this._duration=this._total=Math.floor(t.duration),this._loop=!!t.loop,this._target=e,this._prop=i,this._from=o,this._to=s,this._promises=void 0}active(){return this._active}update(t,e,i){if(this._active){this._notify(!1);const s=this._target[this._prop],n=i-this._start,o=this._duration-n;this._start=i,this._duration=Math.floor(Math.max(o,t.duration)),this._total+=n,this._loop=!!t.loop,this._to=Pi([t.to,e,s,t.from]),this._from=Pi([t.from,s,e])}}cancel(){this._active&&(this.tick(Date.now()),this._active=!1,this._notify(!1))}tick(t){const e=t-this._start,i=this._duration,s=this._prop,n=this._from,o=this._loop,a=this._to;let r;if(this._active=n!==a&&(o||e1?2-r:r,r=this._easing(Math.min(1,Math.max(0,r))),this._target[s]=this._fn(n,a,r))}wait(){const t=this._promises||(this._promises=[]);return new Promise(((e,i)=>{t.push({res:e,rej:i})}))}_notify(t){const e=t?"res":"rej",i=this._promises||[];for(let t=0;t{const a=t[s];if(!o(a))return;const r={};for(const t of e)r[t]=a[t];(n(a.properties)&&a.properties||[s]).forEach((t=>{t!==s&&i.has(t)||i.set(t,r)}))}))}_animateOptions(t,e){const i=e.options,s=function(t,e){if(!e)return;let i=t.options;if(!i)return void(t.options=e);i.$shared&&(t.options=i=Object.assign({},i,{$shared:!1,$animations:{}}));return i}(t,i);if(!s)return[];const n=this._createAnimations(s,i);return i.$shared&&function(t,e){const i=[],s=Object.keys(e);for(let e=0;e{t.options=i}),(()=>{})),n}_createAnimations(t,e){const i=this._properties,s=[],n=t.$animations||(t.$animations={}),o=Object.keys(e),a=Date.now();let r;for(r=o.length-1;r>=0;--r){const l=o[r];if("$"===l.charAt(0))continue;if("options"===l){s.push(...this._animateOptions(t,e));continue}const h=e[l];let c=n[l];const d=i.get(l);if(c){if(d&&c.active()){c.update(d,h,a);continue}c.cancel()}d&&d.duration?(n[l]=c=new Cs(d,t,l,h),s.push(c)):t[l]=h}return s}update(t,e){if(0===this._properties.size)return void Object.assign(t,e);const i=this._createAnimations(t,e);return i.length?(xt.add(this._chart,i),!0):void 0}}function As(t,e){const i=t&&t.options||{},s=i.reverse,n=void 0===i.min?e:0,o=void 0===i.max?e:0;return{start:s?o:n,end:s?n:o}}function Ts(t,e){const i=[],s=t._getSortedDatasetMetas(e);let n,o;for(n=0,o=s.length;n0||!i&&e<0)return n.index}return null}function zs(t,e){const{chart:i,_cachedMeta:s}=t,n=i._stacks||(i._stacks={}),{iScale:o,vScale:a,index:r}=s,l=o.axis,h=a.axis,c=function(t,e,i){return`${t.id}.${e.id}.${i.stack||i.type}`}(o,a,s),d=e.length;let u;for(let t=0;ti[t].axis===e)).shift()}function Vs(t,e){const i=t.controller.index,s=t.vScale&&t.vScale.axis;if(s){e=e||t._parsed;for(const t of e){const e=t._stacks;if(!e||void 0===e[s]||void 0===e[s][i])return;delete e[s][i],void 0!==e[s]._visualValues&&void 0!==e[s]._visualValues[i]&&delete e[s]._visualValues[i]}}}const Bs=t=>"reset"===t||"none"===t,Ws=(t,e)=>e?t:Object.assign({},t);class Ns{static defaults={};static datasetElementType=null;static dataElementType=null;constructor(t,e){this.chart=t,this._ctx=t.ctx,this.index=e,this._cachedDataOpts={},this._cachedMeta=this.getMeta(),this._type=this._cachedMeta.type,this.options=void 0,this._parsing=!1,this._data=void 0,this._objectData=void 0,this._sharedOptions=void 0,this._drawStart=void 0,this._drawCount=void 0,this.enableOptionSharing=!1,this.supportsDecimation=!1,this.$context=void 0,this._syncList=[],this.datasetElementType=new.target.datasetElementType,this.dataElementType=new.target.dataElementType,this.initialize()}initialize(){const t=this._cachedMeta;this.configure(),this.linkScales(),t._stacked=Es(t.vScale,t),this.addElements(),this.options.fill&&!this.chart.isPluginEnabled("filler")&&console.warn("Tried to use the 'fill' option without the 'Filler' plugin enabled. Please import and register the 'Filler' plugin and make sure it is not disabled in the options")}updateIndex(t){this.index!==t&&Vs(this._cachedMeta),this.index=t}linkScales(){const t=this.chart,e=this._cachedMeta,i=this.getDataset(),s=(t,e,i,s)=>"x"===t?e:"r"===t?s:i,n=e.xAxisID=l(i.xAxisID,Fs(t,"x")),o=e.yAxisID=l(i.yAxisID,Fs(t,"y")),a=e.rAxisID=l(i.rAxisID,Fs(t,"r")),r=e.indexAxis,h=e.iAxisID=s(r,n,o,a),c=e.vAxisID=s(r,o,n,a);e.xScale=this.getScaleForId(n),e.yScale=this.getScaleForId(o),e.rScale=this.getScaleForId(a),e.iScale=this.getScaleForId(h),e.vScale=this.getScaleForId(c)}getDataset(){return this.chart.data.datasets[this.index]}getMeta(){return this.chart.getDatasetMeta(this.index)}getScaleForId(t){return this.chart.scales[t]}_getOtherScale(t){const e=this._cachedMeta;return t===e.iScale?e.vScale:e.iScale}reset(){this._update("reset")}_destroy(){const t=this._cachedMeta;this._data&&rt(this._data,this),t._stacked&&Vs(t)}_dataCheck(){const t=this.getDataset(),e=t.data||(t.data=[]),i=this._data;if(o(e))this._data=function(t){const e=Object.keys(t),i=new Array(e.length);let s,n,o;for(s=0,n=e.length;s0&&i._parsed[t-1];if(!1===this._parsing)i._parsed=s,i._sorted=!0,d=s;else{d=n(s[t])?this.parseArrayData(i,s,t,e):o(s[t])?this.parseObjectData(i,s,t,e):this.parsePrimitiveData(i,s,t,e);const a=()=>null===c[l]||f&&c[l]t&&!e.hidden&&e._stacked&&{keys:Ts(i,!0),values:null})(e,i,this.chart),h={min:Number.POSITIVE_INFINITY,max:Number.NEGATIVE_INFINITY},{min:c,max:d}=function(t){const{min:e,max:i,minDefined:s,maxDefined:n}=t.getUserBounds();return{min:s?e:Number.NEGATIVE_INFINITY,max:n?i:Number.POSITIVE_INFINITY}}(r);let u,f;function g(){f=s[u];const e=f[r.axis];return!a(f[t.axis])||c>e||d=0;--u)if(!g()){this.updateRangeFromParsed(h,t,f,l);break}return h}getAllParsedValues(t){const e=this._cachedMeta._parsed,i=[];let s,n,o;for(s=0,n=e.length;s=0&&tthis.getContext(i,s,e)),c);return f.$shared&&(f.$shared=r,n[o]=Object.freeze(Ws(f,r))),f}_resolveAnimations(t,e,i){const s=this.chart,n=this._cachedDataOpts,o=`animation-${e}`,a=n[o];if(a)return a;let r;if(!1!==s.options.animation){const s=this.chart.config,n=s.datasetAnimationScopeKeys(this._type,e),o=s.getOptionScopes(this.getDataset(),n);r=s.createResolver(o,this.getContext(t,i,e))}const l=new Os(s,r&&r.animations);return r&&r._cacheable&&(n[o]=Object.freeze(l)),l}getSharedOptions(t){if(t.$shared)return this._sharedOptions||(this._sharedOptions=Object.assign({},t))}includeOptions(t,e){return!e||Bs(t)||this.chart._animationsDisabled}_getSharedOptions(t,e){const i=this.resolveDataElementOptions(t,e),s=this._sharedOptions,n=this.getSharedOptions(i),o=this.includeOptions(e,n)||n!==s;return this.updateSharedOptions(n,e,i),{sharedOptions:n,includeOptions:o}}updateElement(t,e,i,s){Bs(s)?Object.assign(t,i):this._resolveAnimations(e,s).update(t,i)}updateSharedOptions(t,e,i){t&&!Bs(e)&&this._resolveAnimations(void 0,e).update(t,i)}_setStyle(t,e,i,s){t.active=s;const n=this.getStyle(e,s);this._resolveAnimations(e,i,s).update(t,{options:!s&&this.getSharedOptions(n)||n})}removeHoverStyle(t,e,i){this._setStyle(t,i,"active",!1)}setHoverStyle(t,e,i){this._setStyle(t,i,"active",!0)}_removeDatasetHoverStyle(){const t=this._cachedMeta.dataset;t&&this._setStyle(t,void 0,"active",!1)}_setDatasetHoverStyle(){const t=this._cachedMeta.dataset;t&&this._setStyle(t,void 0,"active",!0)}_resyncElements(t){const e=this._data,i=this._cachedMeta.data;for(const[t,e,i]of this._syncList)this[t](e,i);this._syncList=[];const s=i.length,n=e.length,o=Math.min(n,s);o&&this.parse(0,o),n>s?this._insertElements(s,n-s,t):n{for(t.length+=e,a=t.length-1;a>=o;a--)t[a]=t[a-e]};for(r(n),a=t;a{s[t]=i[t]&&i[t].active()?i[t]._to:this[t]})),s}}function js(t,e){const i=t.options.ticks,n=function(t){const e=t.options.offset,i=t._tickSize(),s=t._length/i+(e?0:1),n=t._maxLength/i;return Math.floor(Math.min(s,n))}(t),o=Math.min(i.maxTicksLimit||n,n),a=i.major.enabled?function(t){const e=[];let i,s;for(i=0,s=t.length;io)return function(t,e,i,s){let n,o=0,a=i[0];for(s=Math.ceil(s),n=0;nn)return e}return Math.max(n,1)}(a,e,o);if(r>0){let t,i;const n=r>1?Math.round((h-l)/(r-1)):null;for($s(e,c,d,s(n)?0:l-n,l),t=0,i=r-1;t"top"===e||"left"===e?t[e]+i:t[e]-i,Us=(t,e)=>Math.min(e||t,t);function Xs(t,e){const i=[],s=t.length/e,n=t.length;let o=0;for(;oa+r)))return h}function Ks(t){return t.drawTicks?t.tickLength:0}function Gs(t,e){if(!t.display)return 0;const i=Si(t.font,e),s=ki(t.padding);return(n(t.text)?t.text.length:1)*i.lineHeight+s.height}function Zs(t,e,i){let s=ut(t);return(i&&"right"!==e||!i&&"right"===e)&&(s=(t=>"left"===t?"right":"right"===t?"left":t)(s)),s}class Js extends Hs{constructor(t){super(),this.id=t.id,this.type=t.type,this.options=void 0,this.ctx=t.ctx,this.chart=t.chart,this.top=void 0,this.bottom=void 0,this.left=void 0,this.right=void 0,this.width=void 0,this.height=void 0,this._margins={left:0,right:0,top:0,bottom:0},this.maxWidth=void 0,this.maxHeight=void 0,this.paddingTop=void 0,this.paddingBottom=void 0,this.paddingLeft=void 0,this.paddingRight=void 0,this.axis=void 0,this.labelRotation=void 0,this.min=void 0,this.max=void 0,this._range=void 0,this.ticks=[],this._gridLineItems=null,this._labelItems=null,this._labelSizes=null,this._length=0,this._maxLength=0,this._longestTextCache={},this._startPixel=void 0,this._endPixel=void 0,this._reversePixels=!1,this._userMax=void 0,this._userMin=void 0,this._suggestedMax=void 0,this._suggestedMin=void 0,this._ticksLength=0,this._borderValue=0,this._cache={},this._dataLimitsCached=!1,this.$context=void 0}init(t){this.options=t.setContext(this.getContext()),this.axis=t.axis,this._userMin=this.parse(t.min),this._userMax=this.parse(t.max),this._suggestedMin=this.parse(t.suggestedMin),this._suggestedMax=this.parse(t.suggestedMax)}parse(t,e){return t}getUserBounds(){let{_userMin:t,_userMax:e,_suggestedMin:i,_suggestedMax:s}=this;return t=r(t,Number.POSITIVE_INFINITY),e=r(e,Number.NEGATIVE_INFINITY),i=r(i,Number.POSITIVE_INFINITY),s=r(s,Number.NEGATIVE_INFINITY),{min:r(t,i),max:r(e,s),minDefined:a(t),maxDefined:a(e)}}getMinMax(t){let e,{min:i,max:s,minDefined:n,maxDefined:o}=this.getUserBounds();if(n&&o)return{min:i,max:s};const a=this.getMatchingVisibleMetas();for(let r=0,l=a.length;rs?s:i,s=n&&i>s?i:s,{min:r(i,r(s,i)),max:r(s,r(i,s))}}getPadding(){return{left:this.paddingLeft||0,top:this.paddingTop||0,right:this.paddingRight||0,bottom:this.paddingBottom||0}}getTicks(){return this.ticks}getLabels(){const t=this.chart.data;return this.options.labels||(this.isHorizontal()?t.xLabels:t.yLabels)||t.labels||[]}getLabelItems(t=this.chart.chartArea){return this._labelItems||(this._labelItems=this._computeLabelItems(t))}beforeLayout(){this._cache={},this._dataLimitsCached=!1}beforeUpdate(){d(this.options.beforeUpdate,[this])}update(t,e,i){const{beginAtZero:s,grace:n,ticks:o}=this.options,a=o.sampleSize;this.beforeUpdate(),this.maxWidth=t,this.maxHeight=e,this._margins=i=Object.assign({left:0,right:0,top:0,bottom:0},i),this.ticks=null,this._labelSizes=null,this._gridLineItems=null,this._labelItems=null,this.beforeSetDimensions(),this.setDimensions(),this.afterSetDimensions(),this._maxLength=this.isHorizontal()?this.width+i.left+i.right:this.height+i.top+i.bottom,this._dataLimitsCached||(this.beforeDataLimits(),this.determineDataLimits(),this.afterDataLimits(),this._range=Di(this,n,s),this._dataLimitsCached=!0),this.beforeBuildTicks(),this.ticks=this.buildTicks()||[],this.afterBuildTicks();const r=a=n||i<=1||!this.isHorizontal())return void(this.labelRotation=s);const h=this._getLabelSizes(),c=h.widest.width,d=h.highest.height,u=J(this.chart.width-c,0,this.maxWidth);o=t.offset?this.maxWidth/i:u/(i-1),c+6>o&&(o=u/(i-(t.offset?.5:1)),a=this.maxHeight-Ks(t.grid)-e.padding-Gs(t.title,this.chart.options.font),r=Math.sqrt(c*c+d*d),l=Y(Math.min(Math.asin(J((h.highest.height+6)/o,-1,1)),Math.asin(J(a/r,-1,1))-Math.asin(J(d/r,-1,1)))),l=Math.max(s,Math.min(n,l))),this.labelRotation=l}afterCalculateLabelRotation(){d(this.options.afterCalculateLabelRotation,[this])}afterAutoSkip(){}beforeFit(){d(this.options.beforeFit,[this])}fit(){const t={width:0,height:0},{chart:e,options:{ticks:i,title:s,grid:n}}=this,o=this._isVisible(),a=this.isHorizontal();if(o){const o=Gs(s,e.options.font);if(a?(t.width=this.maxWidth,t.height=Ks(n)+o):(t.height=this.maxHeight,t.width=Ks(n)+o),i.display&&this.ticks.length){const{first:e,last:s,widest:n,highest:o}=this._getLabelSizes(),r=2*i.padding,l=$(this.labelRotation),h=Math.cos(l),c=Math.sin(l);if(a){const e=i.mirror?0:c*n.width+h*o.height;t.height=Math.min(this.maxHeight,t.height+e+r)}else{const e=i.mirror?0:h*n.width+c*o.height;t.width=Math.min(this.maxWidth,t.width+e+r)}this._calculatePadding(e,s,c,h)}}this._handleMargins(),a?(this.width=this._length=e.width-this._margins.left-this._margins.right,this.height=t.height):(this.width=t.width,this.height=this._length=e.height-this._margins.top-this._margins.bottom)}_calculatePadding(t,e,i,s){const{ticks:{align:n,padding:o},position:a}=this.options,r=0!==this.labelRotation,l="top"!==a&&"x"===this.axis;if(this.isHorizontal()){const a=this.getPixelForTick(0)-this.left,h=this.right-this.getPixelForTick(this.ticks.length-1);let c=0,d=0;r?l?(c=s*t.width,d=i*e.height):(c=i*t.height,d=s*e.width):"start"===n?d=e.width:"end"===n?c=t.width:"inner"!==n&&(c=t.width/2,d=e.width/2),this.paddingLeft=Math.max((c-a+o)*this.width/(this.width-a),0),this.paddingRight=Math.max((d-h+o)*this.width/(this.width-h),0)}else{let i=e.height/2,s=t.height/2;"start"===n?(i=0,s=t.height):"end"===n&&(i=e.height,s=0),this.paddingTop=i+o,this.paddingBottom=s+o}}_handleMargins(){this._margins&&(this._margins.left=Math.max(this.paddingLeft,this._margins.left),this._margins.top=Math.max(this.paddingTop,this._margins.top),this._margins.right=Math.max(this.paddingRight,this._margins.right),this._margins.bottom=Math.max(this.paddingBottom,this._margins.bottom))}afterFit(){d(this.options.afterFit,[this])}isHorizontal(){const{axis:t,position:e}=this.options;return"top"===e||"bottom"===e||"x"===t}isFullSize(){return this.options.fullSize}_convertTicksToLabels(t){let e,i;for(this.beforeTickToLabelConversion(),this.generateTickLabels(t),e=0,i=t.length;e{const i=t.gc,s=i.length/2;let n;if(s>e){for(n=0;n({width:r[t]||0,height:l[t]||0});return{first:P(0),last:P(e-1),widest:P(k),highest:P(S),widths:r,heights:l}}getLabelForValue(t){return t}getPixelForValue(t,e){return NaN}getValueForPixel(t){}getPixelForTick(t){const e=this.ticks;return t<0||t>e.length-1?null:this.getPixelForValue(e[t].value)}getPixelForDecimal(t){this._reversePixels&&(t=1-t);const e=this._startPixel+t*this._length;return Q(this._alignToPixels?Ae(this.chart,e,0):e)}getDecimalForPixel(t){const e=(t-this._startPixel)/this._length;return this._reversePixels?1-e:e}getBasePixel(){return this.getPixelForValue(this.getBaseValue())}getBaseValue(){const{min:t,max:e}=this;return t<0&&e<0?e:t>0&&e>0?t:0}getContext(t){const e=this.ticks||[];if(t>=0&&ta*s?a/i:r/s:r*s0}_computeGridLineItems(t){const e=this.axis,i=this.chart,s=this.options,{grid:n,position:a,border:r}=s,h=n.offset,c=this.isHorizontal(),d=this.ticks.length+(h?1:0),u=Ks(n),f=[],g=r.setContext(this.getContext()),p=g.display?g.width:0,m=p/2,b=function(t){return Ae(i,t,p)};let x,_,y,v,M,w,k,S,P,D,C,O;if("top"===a)x=b(this.bottom),w=this.bottom-u,S=x-m,D=b(t.top)+m,O=t.bottom;else if("bottom"===a)x=b(this.top),D=t.top,O=b(t.bottom)-m,w=x+m,S=this.top+u;else if("left"===a)x=b(this.right),M=this.right-u,k=x-m,P=b(t.left)+m,C=t.right;else if("right"===a)x=b(this.left),P=t.left,C=b(t.right)-m,M=x+m,k=this.left+u;else if("x"===e){if("center"===a)x=b((t.top+t.bottom)/2+.5);else if(o(a)){const t=Object.keys(a)[0],e=a[t];x=b(this.chart.scales[t].getPixelForValue(e))}D=t.top,O=t.bottom,w=x+m,S=w+u}else if("y"===e){if("center"===a)x=b((t.left+t.right)/2);else if(o(a)){const t=Object.keys(a)[0],e=a[t];x=b(this.chart.scales[t].getPixelForValue(e))}M=x-m,k=M-u,P=t.left,C=t.right}const A=l(s.ticks.maxTicksLimit,d),T=Math.max(1,Math.ceil(d/A));for(_=0;_e.value===t));if(i>=0){return e.setContext(this.getContext(i)).lineWidth}return 0}drawGrid(t){const e=this.options.grid,i=this.ctx,s=this._gridLineItems||(this._gridLineItems=this._computeGridLineItems(t));let n,o;const a=(t,e,s)=>{s.width&&s.color&&(i.save(),i.lineWidth=s.width,i.strokeStyle=s.color,i.setLineDash(s.borderDash||[]),i.lineDashOffset=s.borderDashOffset,i.beginPath(),i.moveTo(t.x,t.y),i.lineTo(e.x,e.y),i.stroke(),i.restore())};if(e.display)for(n=0,o=s.length;n{this.drawBackground(),this.drawGrid(t),this.drawTitle()}},{z:s,draw:()=>{this.drawBorder()}},{z:e,draw:t=>{this.drawLabels(t)}}]:[{z:e,draw:t=>{this.draw(t)}}]}getMatchingVisibleMetas(t){const e=this.chart.getSortedVisibleDatasetMetas(),i=this.axis+"AxisID",s=[];let n,o;for(n=0,o=e.length;n{const s=i.split("."),n=s.pop(),o=[t].concat(s).join("."),a=e[i].split("."),r=a.pop(),l=a.join(".");ue.route(o,n,l,r)}))}(e,t.defaultRoutes);t.descriptors&&ue.describe(e,t.descriptors)}(t,o,i),this.override&&ue.override(t.id,t.overrides)),o}get(t){return this.items[t]}unregister(t){const e=this.items,i=t.id,s=this.scope;i in e&&delete e[i],s&&i in ue[s]&&(delete ue[s][i],this.override&&delete re[i])}}class tn{constructor(){this.controllers=new Qs(Ns,"datasets",!0),this.elements=new Qs(Hs,"elements"),this.plugins=new Qs(Object,"plugins"),this.scales=new Qs(Js,"scales"),this._typedRegistries=[this.controllers,this.scales,this.elements]}add(...t){this._each("register",t)}remove(...t){this._each("unregister",t)}addControllers(...t){this._each("register",t,this.controllers)}addElements(...t){this._each("register",t,this.elements)}addPlugins(...t){this._each("register",t,this.plugins)}addScales(...t){this._each("register",t,this.scales)}getController(t){return this._get(t,this.controllers,"controller")}getElement(t){return this._get(t,this.elements,"element")}getPlugin(t){return this._get(t,this.plugins,"plugin")}getScale(t){return this._get(t,this.scales,"scale")}removeControllers(...t){this._each("unregister",t,this.controllers)}removeElements(...t){this._each("unregister",t,this.elements)}removePlugins(...t){this._each("unregister",t,this.plugins)}removeScales(...t){this._each("unregister",t,this.scales)}_each(t,e,i){[...e].forEach((e=>{const s=i||this._getRegistryForType(e);i||s.isForType(e)||s===this.plugins&&e.id?this._exec(t,s,e):u(e,(e=>{const s=i||this._getRegistryForType(e);this._exec(t,s,e)}))}))}_exec(t,e,i){const s=w(t);d(i["before"+s],[],i),e[t](i),d(i["after"+s],[],i)}_getRegistryForType(t){for(let e=0;et.filter((t=>!e.some((e=>t.plugin.id===e.plugin.id))));this._notify(s(e,i),t,"stop"),this._notify(s(i,e),t,"start")}}function nn(t,e){return e||!1!==t?!0===t?{}:t:null}function on(t,{plugin:e,local:i},s,n){const o=t.pluginScopeKeys(e),a=t.getOptionScopes(s,o);return i&&e.defaults&&a.push(e.defaults),t.createResolver(a,n,[""],{scriptable:!1,indexable:!1,allKeys:!0})}function an(t,e){const i=ue.datasets[t]||{};return((e.datasets||{})[t]||{}).indexAxis||e.indexAxis||i.indexAxis||"x"}function rn(t){if("x"===t||"y"===t||"r"===t)return t}function ln(t,...e){if(rn(t))return t;for(const s of e){const e=s.axis||("top"===(i=s.position)||"bottom"===i?"x":"left"===i||"right"===i?"y":void 0)||t.length>1&&rn(t[0].toLowerCase());if(e)return e}var i;throw new Error(`Cannot determine type of '${t}' axis. Please provide 'axis' or 'position' option.`)}function hn(t,e,i){if(i[e+"AxisID"]===t)return{axis:e}}function cn(t,e){const i=re[t.type]||{scales:{}},s=e.scales||{},n=an(t.type,e),a=Object.create(null);return Object.keys(s).forEach((e=>{const r=s[e];if(!o(r))return console.error(`Invalid scale configuration for scale: ${e}`);if(r._proxy)return console.warn(`Ignoring resolver passed as options for scale: ${e}`);const l=ln(e,r,function(t,e){if(e.data&&e.data.datasets){const i=e.data.datasets.filter((e=>e.xAxisID===t||e.yAxisID===t));if(i.length)return hn(t,"x",i[0])||hn(t,"y",i[0])}return{}}(e,t),ue.scales[r.type]),h=function(t,e){return t===e?"_index_":"_value_"}(l,n),c=i.scales||{};a[e]=x(Object.create(null),[{axis:l},r,c[l],c[h]])})),t.data.datasets.forEach((i=>{const n=i.type||t.type,o=i.indexAxis||an(n,e),r=(re[n]||{}).scales||{};Object.keys(r).forEach((t=>{const e=function(t,e){let i=t;return"_index_"===t?i=e:"_value_"===t&&(i="x"===e?"y":"x"),i}(t,o),n=i[e+"AxisID"]||e;a[n]=a[n]||Object.create(null),x(a[n],[{axis:e},s[n],r[t]])}))})),Object.keys(a).forEach((t=>{const e=a[t];x(e,[ue.scales[e.type],ue.scale])})),a}function dn(t){const e=t.options||(t.options={});e.plugins=l(e.plugins,{}),e.scales=cn(t,e)}function un(t){return(t=t||{}).datasets=t.datasets||[],t.labels=t.labels||[],t}const fn=new Map,gn=new Set;function pn(t,e){let i=fn.get(t);return i||(i=e(),fn.set(t,i),gn.add(i)),i}const mn=(t,e,i)=>{const s=M(e,i);void 0!==s&&t.add(s)};class bn{constructor(t){this._config=function(t){return(t=t||{}).data=un(t.data),dn(t),t}(t),this._scopeCache=new Map,this._resolverCache=new Map}get platform(){return this._config.platform}get type(){return this._config.type}set type(t){this._config.type=t}get data(){return this._config.data}set data(t){this._config.data=un(t)}get options(){return this._config.options}set options(t){this._config.options=t}get plugins(){return this._config.plugins}update(){const t=this._config;this.clearCache(),dn(t)}clearCache(){this._scopeCache.clear(),this._resolverCache.clear()}datasetScopeKeys(t){return pn(t,(()=>[[`datasets.${t}`,""]]))}datasetAnimationScopeKeys(t,e){return pn(`${t}.transition.${e}`,(()=>[[`datasets.${t}.transitions.${e}`,`transitions.${e}`],[`datasets.${t}`,""]]))}datasetElementScopeKeys(t,e){return pn(`${t}-${e}`,(()=>[[`datasets.${t}.elements.${e}`,`datasets.${t}`,`elements.${e}`,""]]))}pluginScopeKeys(t){const e=t.id;return pn(`${this.type}-plugin-${e}`,(()=>[[`plugins.${e}`,...t.additionalOptionScopes||[]]]))}_cachedScopes(t,e){const i=this._scopeCache;let s=i.get(t);return s&&!e||(s=new Map,i.set(t,s)),s}getOptionScopes(t,e,i){const{options:s,type:n}=this,o=this._cachedScopes(t,i),a=o.get(e);if(a)return a;const r=new Set;e.forEach((e=>{t&&(r.add(t),e.forEach((e=>mn(r,t,e)))),e.forEach((t=>mn(r,s,t))),e.forEach((t=>mn(r,re[n]||{},t))),e.forEach((t=>mn(r,ue,t))),e.forEach((t=>mn(r,le,t)))}));const l=Array.from(r);return 0===l.length&&l.push(Object.create(null)),gn.has(e)&&o.set(e,l),l}chartOptionScopes(){const{options:t,type:e}=this;return[t,re[e]||{},ue.datasets[e]||{},{type:e},ue,le]}resolveNamedOptions(t,e,i,s=[""]){const o={$shared:!0},{resolver:a,subPrefixes:r}=xn(this._resolverCache,t,s);let l=a;if(function(t,e){const{isScriptable:i,isIndexable:s}=Ye(t);for(const o of e){const e=i(o),a=s(o),r=(a||e)&&t[o];if(e&&(S(r)||_n(r))||a&&n(r))return!0}return!1}(a,e)){o.$shared=!1;l=$e(a,i=S(i)?i():i,this.createResolver(t,i,r))}for(const t of e)o[t]=l[t];return o}createResolver(t,e,i=[""],s){const{resolver:n}=xn(this._resolverCache,t,i);return o(e)?$e(n,e,void 0,s):n}}function xn(t,e,i){let s=t.get(e);s||(s=new Map,t.set(e,s));const n=i.join();let o=s.get(n);if(!o){o={resolver:je(e,i),subPrefixes:i.filter((t=>!t.toLowerCase().includes("hover")))},s.set(n,o)}return o}const _n=t=>o(t)&&Object.getOwnPropertyNames(t).reduce(((e,i)=>e||S(t[i])),!1);const yn=["top","bottom","left","right","chartArea"];function vn(t,e){return"top"===t||"bottom"===t||-1===yn.indexOf(t)&&"x"===e}function Mn(t,e){return function(i,s){return i[t]===s[t]?i[e]-s[e]:i[t]-s[t]}}function wn(t){const e=t.chart,i=e.options.animation;e.notifyPlugins("afterRender"),d(i&&i.onComplete,[t],e)}function kn(t){const e=t.chart,i=e.options.animation;d(i&&i.onProgress,[t],e)}function Sn(t){return fe()&&"string"==typeof t?t=document.getElementById(t):t&&t.length&&(t=t[0]),t&&t.canvas&&(t=t.canvas),t}const Pn={},Dn=t=>{const e=Sn(t);return Object.values(Pn).filter((t=>t.canvas===e)).pop()};function Cn(t,e,i){const s=Object.keys(t);for(const n of s){const s=+n;if(s>=e){const o=t[n];delete t[n],(i>0||s>e)&&(t[s+i]=o)}}}function On(t,e,i){return t.options.clip?t[i]:e[i]}class An{static defaults=ue;static instances=Pn;static overrides=re;static registry=en;static version="4.4.0";static getChart=Dn;static register(...t){en.add(...t),Tn()}static unregister(...t){en.remove(...t),Tn()}constructor(t,e){const s=this.config=new bn(e),n=Sn(t),o=Dn(n);if(o)throw new Error("Canvas is already in use. Chart with ID '"+o.id+"' must be destroyed before the canvas with ID '"+o.canvas.id+"' can be reused.");const a=s.createResolver(s.chartOptionScopes(),this.getContext());this.platform=new(s.platform||ks(n)),this.platform.updateConfig(s);const r=this.platform.acquireContext(n,a.aspectRatio),l=r&&r.canvas,h=l&&l.height,c=l&&l.width;this.id=i(),this.ctx=r,this.canvas=l,this.width=c,this.height=h,this._options=a,this._aspectRatio=this.aspectRatio,this._layers=[],this._metasets=[],this._stacks=void 0,this.boxes=[],this.currentDevicePixelRatio=void 0,this.chartArea=void 0,this._active=[],this._lastEvent=void 0,this._listeners={},this._responsiveListeners=void 0,this._sortedMetasets=[],this.scales={},this._plugins=new sn,this.$proxies={},this._hiddenIndices={},this.attached=!1,this._animationsDisabled=void 0,this.$context=void 0,this._doResize=dt((t=>this.update(t)),a.resizeDelay||0),this._dataChanges=[],Pn[this.id]=this,r&&l?(xt.listen(this,"complete",wn),xt.listen(this,"progress",kn),this._initialize(),this.attached&&this.update()):console.error("Failed to create chart: can't acquire context from the given item")}get aspectRatio(){const{options:{aspectRatio:t,maintainAspectRatio:e},width:i,height:n,_aspectRatio:o}=this;return s(t)?e&&o?o:n?i/n:null:t}get data(){return this.config.data}set data(t){this.config.data=t}get options(){return this._options}set options(t){this.config.options=t}get registry(){return en}_initialize(){return this.notifyPlugins("beforeInit"),this.options.responsive?this.resize():ke(this,this.options.devicePixelRatio),this.bindEvents(),this.notifyPlugins("afterInit"),this}clear(){return Te(this.canvas,this.ctx),this}stop(){return xt.stop(this),this}resize(t,e){xt.running(this)?this._resizeBeforeDraw={width:t,height:e}:this._resize(t,e)}_resize(t,e){const i=this.options,s=this.canvas,n=i.maintainAspectRatio&&this.aspectRatio,o=this.platform.getMaximumSize(s,t,e,n),a=i.devicePixelRatio||this.platform.getDevicePixelRatio(),r=this.width?"resize":"attach";this.width=o.width,this.height=o.height,this._aspectRatio=this.aspectRatio,ke(this,a,!0)&&(this.notifyPlugins("resize",{size:o}),d(i.onResize,[this,o],this),this.attached&&this._doResize(r)&&this.render())}ensureScalesHaveIDs(){u(this.options.scales||{},((t,e)=>{t.id=e}))}buildOrUpdateScales(){const t=this.options,e=t.scales,i=this.scales,s=Object.keys(i).reduce(((t,e)=>(t[e]=!1,t)),{});let n=[];e&&(n=n.concat(Object.keys(e).map((t=>{const i=e[t],s=ln(t,i),n="r"===s,o="x"===s;return{options:i,dposition:n?"chartArea":o?"bottom":"left",dtype:n?"radialLinear":o?"category":"linear"}})))),u(n,(e=>{const n=e.options,o=n.id,a=ln(o,n),r=l(n.type,e.dtype);void 0!==n.position&&vn(n.position,a)===vn(e.dposition)||(n.position=e.dposition),s[o]=!0;let h=null;if(o in i&&i[o].type===r)h=i[o];else{h=new(en.getScale(r))({id:o,type:r,ctx:this.ctx,chart:this}),i[h.id]=h}h.init(n,t)})),u(s,((t,e)=>{t||delete i[e]})),u(i,(t=>{as.configure(this,t,t.options),as.addBox(this,t)}))}_updateMetasets(){const t=this._metasets,e=this.data.datasets.length,i=t.length;if(t.sort(((t,e)=>t.index-e.index)),i>e){for(let t=e;te.length&&delete this._stacks,t.forEach(((t,i)=>{0===e.filter((e=>e===t._dataset)).length&&this._destroyDatasetMeta(i)}))}buildOrUpdateControllers(){const t=[],e=this.data.datasets;let i,s;for(this._removeUnreferencedMetasets(),i=0,s=e.length;i{this.getDatasetMeta(e).controller.reset()}),this)}reset(){this._resetElements(),this.notifyPlugins("reset")}update(t){const e=this.config;e.update();const i=this._options=e.createResolver(e.chartOptionScopes(),this.getContext()),s=this._animationsDisabled=!i.animation;if(this._updateScales(),this._checkEventBindings(),this._updateHiddenIndices(),this._plugins.invalidate(),!1===this.notifyPlugins("beforeUpdate",{mode:t,cancelable:!0}))return;const n=this.buildOrUpdateControllers();this.notifyPlugins("beforeElementsUpdate");let o=0;for(let t=0,e=this.data.datasets.length;t{t.reset()})),this._updateDatasets(t),this.notifyPlugins("afterUpdate",{mode:t}),this._layers.sort(Mn("z","_idx"));const{_active:a,_lastEvent:r}=this;r?this._eventHandler(r,!0):a.length&&this._updateHoverStyles(a,a,!0),this.render()}_updateScales(){u(this.scales,(t=>{as.removeBox(this,t)})),this.ensureScalesHaveIDs(),this.buildOrUpdateScales()}_checkEventBindings(){const t=this.options,e=new Set(Object.keys(this._listeners)),i=new Set(t.events);P(e,i)&&!!this._responsiveListeners===t.responsive||(this.unbindEvents(),this.bindEvents())}_updateHiddenIndices(){const{_hiddenIndices:t}=this,e=this._getUniformDataChanges()||[];for(const{method:i,start:s,count:n}of e){Cn(t,s,"_removeElements"===i?-n:n)}}_getUniformDataChanges(){const t=this._dataChanges;if(!t||!t.length)return;this._dataChanges=[];const e=this.data.datasets.length,i=e=>new Set(t.filter((t=>t[0]===e)).map(((t,e)=>e+","+t.splice(1).join(",")))),s=i(0);for(let t=1;tt.split(","))).map((t=>({method:t[1],start:+t[2],count:+t[3]})))}_updateLayout(t){if(!1===this.notifyPlugins("beforeLayout",{cancelable:!0}))return;as.update(this,this.width,this.height,t);const e=this.chartArea,i=e.width<=0||e.height<=0;this._layers=[],u(this.boxes,(t=>{i&&"chartArea"===t.position||(t.configure&&t.configure(),this._layers.push(...t._layers()))}),this),this._layers.forEach(((t,e)=>{t._idx=e})),this.notifyPlugins("afterLayout")}_updateDatasets(t){if(!1!==this.notifyPlugins("beforeDatasetsUpdate",{mode:t,cancelable:!0})){for(let t=0,e=this.data.datasets.length;t=0;--e)this._drawDataset(t[e]);this.notifyPlugins("afterDatasetsDraw")}_drawDataset(t){const e=this.ctx,i=t._clip,s=!i.disabled,n=function(t,e){const{xScale:i,yScale:s}=t;return i&&s?{left:On(i,e,"left"),right:On(i,e,"right"),top:On(s,e,"top"),bottom:On(s,e,"bottom")}:e}(t,this.chartArea),o={meta:t,index:t.index,cancelable:!0};!1!==this.notifyPlugins("beforeDatasetDraw",o)&&(s&&Ie(e,{left:!1===i.left?0:n.left-i.left,right:!1===i.right?this.width:n.right+i.right,top:!1===i.top?0:n.top-i.top,bottom:!1===i.bottom?this.height:n.bottom+i.bottom}),t.controller.draw(),s&&ze(e),o.cancelable=!1,this.notifyPlugins("afterDatasetDraw",o))}isPointInArea(t){return Re(t,this.chartArea,this._minPadding)}getElementsAtEventForMode(t,e,i,s){const n=Xi.modes[e];return"function"==typeof n?n(this,t,i,s):[]}getDatasetMeta(t){const e=this.data.datasets[t],i=this._metasets;let s=i.filter((t=>t&&t._dataset===e)).pop();return s||(s={type:null,data:[],dataset:null,controller:null,hidden:null,xAxisID:null,yAxisID:null,order:e&&e.order||0,index:t,_dataset:e,_parsed:[],_sorted:!1},i.push(s)),s}getContext(){return this.$context||(this.$context=Ci(null,{chart:this,type:"chart"}))}getVisibleDatasetCount(){return this.getSortedVisibleDatasetMetas().length}isDatasetVisible(t){const e=this.data.datasets[t];if(!e)return!1;const i=this.getDatasetMeta(t);return"boolean"==typeof i.hidden?!i.hidden:!e.hidden}setDatasetVisibility(t,e){this.getDatasetMeta(t).hidden=!e}toggleDataVisibility(t){this._hiddenIndices[t]=!this._hiddenIndices[t]}getDataVisibility(t){return!this._hiddenIndices[t]}_updateVisibility(t,e,i){const s=i?"show":"hide",n=this.getDatasetMeta(t),o=n.controller._resolveAnimations(void 0,s);k(e)?(n.data[e].hidden=!i,this.update()):(this.setDatasetVisibility(t,i),o.update(n,{visible:i}),this.update((e=>e.datasetIndex===t?s:void 0)))}hide(t,e){this._updateVisibility(t,e,!1)}show(t,e){this._updateVisibility(t,e,!0)}_destroyDatasetMeta(t){const e=this._metasets[t];e&&e.controller&&e.controller._destroy(),delete this._metasets[t]}_stop(){let t,e;for(this.stop(),xt.remove(this),t=0,e=this.data.datasets.length;t{e.addEventListener(this,i,s),t[i]=s},s=(t,e,i)=>{t.offsetX=e,t.offsetY=i,this._eventHandler(t)};u(this.options.events,(t=>i(t,s)))}bindResponsiveEvents(){this._responsiveListeners||(this._responsiveListeners={});const t=this._responsiveListeners,e=this.platform,i=(i,s)=>{e.addEventListener(this,i,s),t[i]=s},s=(i,s)=>{t[i]&&(e.removeEventListener(this,i,s),delete t[i])},n=(t,e)=>{this.canvas&&this.resize(t,e)};let o;const a=()=>{s("attach",a),this.attached=!0,this.resize(),i("resize",n),i("detach",o)};o=()=>{this.attached=!1,s("resize",n),this._stop(),this._resize(0,0),i("attach",a)},e.isAttached(this.canvas)?a():o()}unbindEvents(){u(this._listeners,((t,e)=>{this.platform.removeEventListener(this,e,t)})),this._listeners={},u(this._responsiveListeners,((t,e)=>{this.platform.removeEventListener(this,e,t)})),this._responsiveListeners=void 0}updateHoverStyle(t,e,i){const s=i?"set":"remove";let n,o,a,r;for("dataset"===e&&(n=this.getDatasetMeta(t[0].datasetIndex),n.controller["_"+s+"DatasetHoverStyle"]()),a=0,r=t.length;a{const i=this.getDatasetMeta(t);if(!i)throw new Error("No dataset found at index "+t);return{datasetIndex:t,element:i.data[e],index:e}}));!f(i,e)&&(this._active=i,this._lastEvent=null,this._updateHoverStyles(i,e))}notifyPlugins(t,e,i){return this._plugins.notify(this,t,e,i)}isPluginEnabled(t){return 1===this._plugins._cache.filter((e=>e.plugin.id===t)).length}_updateHoverStyles(t,e,i){const s=this.options.hover,n=(t,e)=>t.filter((t=>!e.some((e=>t.datasetIndex===e.datasetIndex&&t.index===e.index)))),o=n(e,t),a=i?t:n(t,e);o.length&&this.updateHoverStyle(o,s.mode,!1),a.length&&s.mode&&this.updateHoverStyle(a,s.mode,!0)}_eventHandler(t,e){const i={event:t,replay:e,cancelable:!0,inChartArea:this.isPointInArea(t)},s=e=>(e.options.events||this.options.events).includes(t.native.type);if(!1===this.notifyPlugins("beforeEvent",i,s))return;const 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e.labels.map(((e,n)=>{const o=t.getDatasetMeta(0).controller.getStyle(n);return{text:e,fillStyle:o.backgroundColor,strokeStyle:o.borderColor,fontColor:s,lineWidth:o.borderWidth,pointStyle:i,hidden:!t.getDataVisibility(n),index:n}}))}return[]}},onClick(t,e,i){i.chart.toggleDataVisibility(e.index),i.chart.update()}}}};constructor(t,e){super(t,e),this.enableOptionSharing=!0,this.innerRadius=void 0,this.outerRadius=void 0,this.offsetX=void 0,this.offsetY=void 0}linkScales(){}parse(t,e){const i=this.getDataset().data,s=this._cachedMeta;if(!1===this._parsing)s._parsed=i;else{let 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n="reset"===s,o=this.chart,a=o.chartArea,r=o.options.animation,l=(a.left+a.right)/2,h=(a.top+a.bottom)/2,c=n&&r.animateScale,d=c?0:this.innerRadius,u=c?0:this.outerRadius,{sharedOptions:f,includeOptions:g}=this._getSharedOptions(e,s);let p,m=this._getRotation();for(p=0;p0&&!isNaN(t)?O*(Math.abs(t)/e):0}getLabelAndValue(t){const e=this._cachedMeta,i=this.chart,s=i.data.labels||[],n=ne(e._parsed[t],i.options.locale);return{label:s[t]||"",value:n}}getMaxBorderWidth(t){let e=0;const i=this.chart;let s,n,o,a,r;if(!t)for(s=0,n=i.data.datasets.length;s{const o=t.getDatasetMeta(0).controller.getStyle(n);return{text:e,fillStyle:o.backgroundColor,strokeStyle:o.borderColor,fontColor:s,lineWidth:o.borderWidth,pointStyle:i,hidden:!t.getDataVisibility(n),index:n}}))}return[]}},onClick(t,e,i){i.chart.toggleDataVisibility(e.index),i.chart.update()}}},scales:{r:{type:"radialLinear",angleLines:{display:!1},beginAtZero:!0,grid:{circular:!0},pointLabels:{display:!1},startAngle:0}}};constructor(t,e){super(t,e),this.innerRadius=void 0,this.outerRadius=void 0}getLabelAndValue(t){const e=this._cachedMeta,i=this.chart,s=i.data.labels||[],n=ne(e._parsed[t].r,i.options.locale);return{label:s[t]||"",value:n}}parseObjectData(t,e,i,s){return ii.bind(this)(t,e,i,s)}update(t){const e=this._cachedMeta.data;this._updateRadius(),this.updateElements(e,0,e.length,t)}getMinMax(){const t=this._cachedMeta,e={min:Number.POSITIVE_INFINITY,max:Number.NEGATIVE_INFINITY};return t.data.forEach(((t,i)=>{const s=this.getParsed(i).r;!isNaN(s)&&this.chart.getDataVisibility(i)&&(se.max&&(e.max=s))})),e}_updateRadius(){const t=this.chart,e=t.chartArea,i=t.options,s=Math.min(e.right-e.left,e.bottom-e.top),n=Math.max(s/2,0),o=(n-Math.max(i.cutoutPercentage?n/100*i.cutoutPercentage:1,0))/t.getVisibleDatasetCount();this.outerRadius=n-o*this.index,this.innerRadius=this.outerRadius-o}updateElements(t,e,i,s){const n="reset"===s,o=this.chart,a=o.options.animation,r=this._cachedMeta.rScale,l=r.xCenter,h=r.yCenter,c=r.getIndexAngle(0)-.5*C;let d,u=c;const f=360/this.countVisibleElements();for(d=0;d{!isNaN(this.getParsed(i).r)&&this.chart.getDataVisibility(i)&&e++})),e}_computeAngle(t,e,i){return this.chart.getDataVisibility(t)?$(this.resolveDataElementOptions(t,e).angle||i):0}}var Yn=Object.freeze({__proto__:null,BarController:class extends Ns{static id="bar";static 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e=this._cachedMeta,i=e.dataset,s=e.data||[],n=e.iScale.getLabels();if(i.points=s,"resize"!==t){const e=this.resolveDatasetElementOptions(t);this.options.showLine||(e.borderWidth=0);const o={_loop:!0,_fullLoop:n.length===s.length,options:e};this.updateElement(i,void 0,o,t)}this.updateElements(s,0,s.length,t)}updateElements(t,e,i,s){const n=this._cachedMeta.rScale,o="reset"===s;for(let a=e;a0&&this.getParsed(e-1);for(let c=e;c0&&Math.abs(i[f]-_[f])>b,m&&(p.parsed=i,p.raw=h.data[c]),u&&(p.options=d||this.resolveDataElementOptions(c,e.active?"active":n)),x||this.updateElement(e,c,p,n),_=i}this.updateSharedOptions(d,n,c)}getMaxOverflow(){const t=this._cachedMeta,e=t.data||[];if(!this.options.showLine){let t=0;for(let i=e.length-1;i>=0;--i)t=Math.max(t,e[i].size(this.resolveDataElementOptions(i))/2);return t>0&&t}const i=t.dataset,s=i.options&&i.options.borderWidth||0;if(!e.length)return s;const 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e=(k+S)/2;if(t.arc(a,r,d,k,e),t.arc(a,r,d,e,S),_>0){const e=Xn(w,S,a,r);t.arc(e.x,e.y,_,S,b+E)}const i=Xn(D,b,a,r);if(t.lineTo(i.x,i.y),v>0){const e=Xn(D,A,a,r);t.arc(e.x,e.y,v,b+E,A+Math.PI)}const s=(b-v/u+(m+y/u))/2;if(t.arc(a,r,u,b-v/u,s,!0),t.arc(a,r,u,s,m+y/u,!0),y>0){const e=Xn(P,O,a,r);t.arc(e.x,e.y,y,O+Math.PI,m-E)}const n=Xn(M,m,a,r);if(t.lineTo(n.x,n.y),x>0){const e=Xn(M,k,a,r);t.arc(e.x,e.y,x,m-E,k)}}else{t.moveTo(a,r);const e=Math.cos(k)*d+a,i=Math.sin(k)*d+r;t.lineTo(e,i);const s=Math.cos(S)*d+a,n=Math.sin(S)*d+r;t.lineTo(s,n)}t.closePath()}function Kn(t,e,i,s,n){const{fullCircles:o,startAngle:a,circumference:r,options:l}=e,{borderWidth:h,borderJoinStyle:c,borderDash:d,borderDashOffset:u}=l,f="inner"===l.borderAlign;if(!h)return;t.setLineDash(d||[]),t.lineDashOffset=u,f?(t.lineWidth=2*h,t.lineJoin=c||"round"):(t.lineWidth=h,t.lineJoin=c||"bevel");let g=e.endAngle;if(o){qn(t,e,i,s,g,n);for(let e=0;en?(h=n/l,t.arc(o,a,l,i+h,s-h,!0)):t.arc(o,a,n,i+E,s-E),t.closePath(),t.clip()}(t,e,g),o||(qn(t,e,i,s,g,n),t.stroke())}function Gn(t,e,i=e){t.lineCap=l(i.borderCapStyle,e.borderCapStyle),t.setLineDash(l(i.borderDash,e.borderDash)),t.lineDashOffset=l(i.borderDashOffset,e.borderDashOffset),t.lineJoin=l(i.borderJoinStyle,e.borderJoinStyle),t.lineWidth=l(i.borderWidth,e.borderWidth),t.strokeStyle=l(i.borderColor,e.borderColor)}function Zn(t,e,i){t.lineTo(i.x,i.y)}function Jn(t,e,i={}){const s=t.length,{start:n=0,end:o=s-1}=i,{start:a,end:r}=e,l=Math.max(n,a),h=Math.min(o,r),c=nr&&o>r;return{count:s,start:l,loop:e.loop,ilen:h(a+(h?r-t:t))%o,_=()=>{f!==g&&(t.lineTo(m,g),t.lineTo(m,f),t.lineTo(m,p))};for(l&&(d=n[x(0)],t.moveTo(d.x,d.y)),c=0;c<=r;++c){if(d=n[x(c)],d.skip)continue;const e=d.x,i=d.y,s=0|e;s===u?(ig&&(g=i),m=(b*m+e)/++b):(_(),t.lineTo(e,i),u=s,b=0,f=g=i),p=i}_()}function eo(t){const e=t.options,i=e.borderDash&&e.borderDash.length;return!(t._decimated||t._loop||e.tension||"monotone"===e.cubicInterpolationMode||e.stepped||i)?to:Qn}const io="function"==typeof Path2D;function so(t,e,i,s){io&&!e.options.segment?function(t,e,i,s){let n=e._path;n||(n=e._path=new Path2D,e.path(n,i,s)&&n.closePath()),Gn(t,e.options),t.stroke(n)}(t,e,i,s):function(t,e,i,s){const{segments:n,options:o}=e,a=eo(e);for(const r of n)Gn(t,o,r.style),t.beginPath(),a(t,e,r,{start:i,end:i+s-1})&&t.closePath(),t.stroke()}(t,e,i,s)}class no extends Hs{static id="line";static defaults={borderCapStyle:"butt",borderDash:[],borderDashOffset:0,borderJoinStyle:"miter",borderWidth:3,capBezierPoints:!0,cubicInterpolationMode:"default",fill:!1,spanGaps:!1,stepped:!1,tension:0};static defaultRoutes={backgroundColor:"backgroundColor",borderColor:"borderColor"};static descriptors={_scriptable:!0,_indexable:t=>"borderDash"!==t&&"fill"!==t};constructor(t){super(),this.animated=!0,this.options=void 0,this._chart=void 0,this._loop=void 0,this._fullLoop=void 0,this._path=void 0,this._points=void 0,this._segments=void 0,this._decimated=!1,this._pointsUpdated=!1,this._datasetIndex=void 0,t&&Object.assign(this,t)}updateControlPoints(t,e){const i=this.options;if((i.tension||"monotone"===i.cubicInterpolationMode)&&!i.stepped&&!this._pointsUpdated){const s=i.spanGaps?this._loop:this._fullLoop;hi(this._points,i,t,s,e),this._pointsUpdated=!0}}set points(t){this._points=t,delete this._segments,delete this._path,this._pointsUpdated=!1}get points(){return this._points}get segments(){return this._segments||(this._segments=zi(this,this.options.segment))}first(){const t=this.segments,e=this.points;return t.length&&e[t[0].start]}last(){const t=this.segments,e=this.points,i=t.length;return i&&e[t[i-1].end]}interpolate(t,e){const i=this.options,s=t[e],n=this.points,o=Ii(this,{property:e,start:s,end:s});if(!o.length)return;const a=[],r=function(t){return t.stepped?pi:t.tension||"monotone"===t.cubicInterpolationMode?mi:gi}(i);let l,h;for(l=0,h=o.length;l"borderDash"!==t};circumference;endAngle;fullCircles;innerRadius;outerRadius;pixelMargin;startAngle;constructor(t){super(),this.options=void 0,this.circumference=void 0,this.startAngle=void 0,this.endAngle=void 0,this.innerRadius=void 0,this.outerRadius=void 0,this.pixelMargin=0,this.fullCircles=0,t&&Object.assign(this,t)}inRange(t,e,i){const s=this.getProps(["x","y"],i),{angle:n,distance:o}=X(s,{x:t,y:e}),{startAngle:a,endAngle:r,innerRadius:h,outerRadius:c,circumference:d}=this.getProps(["startAngle","endAngle","innerRadius","outerRadius","circumference"],i),u=(this.options.spacing+this.options.borderWidth)/2,f=l(d,r-a)>=O||Z(n,a,r),g=tt(o,h+u,c+u);return f&&g}getCenterPoint(t){const{x:e,y:i,startAngle:s,endAngle:n,innerRadius:o,outerRadius:a}=this.getProps(["x","y","startAngle","endAngle","innerRadius","outerRadius"],t),{offset:r,spacing:l}=this.options,h=(s+n)/2,c=(o+a+l+r)/2;return{x:e+Math.cos(h)*c,y:i+Math.sin(h)*c}}tooltipPosition(t){return this.getCenterPoint(t)}draw(t){const{options:e,circumference:i}=this,s=(e.offset||0)/4,n=(e.spacing||0)/2,o=e.circular;if(this.pixelMargin="inner"===e.borderAlign?.33:0,this.fullCircles=i>O?Math.floor(i/O):0,0===i||this.innerRadius<0||this.outerRadius<0)return;t.save();const a=(this.startAngle+this.endAngle)/2;t.translate(Math.cos(a)*s,Math.sin(a)*s);const r=s*(1-Math.sin(Math.min(C,i||0)));t.fillStyle=e.backgroundColor,t.strokeStyle=e.borderColor,function(t,e,i,s,n){const{fullCircles:o,startAngle:a,circumference:r}=e;let l=e.endAngle;if(o){qn(t,e,i,s,l,n);for(let e=0;e("string"==typeof e?(i=t.push(e)-1,s.unshift({index:i,label:e})):isNaN(e)&&(i=null),i))(t,e,i,s);return n!==t.lastIndexOf(e)?i:n}function po(t){const e=this.getLabels();return t>=0&&ts=e?s:t,a=t=>n=i?n:t;if(t){const t=F(s),e=F(n);t<0&&e<0?a(0):t>0&&e>0&&o(0)}if(s===n){let e=0===n?1:Math.abs(.05*n);a(n+e),t||o(s-e)}this.min=s,this.max=n}getTickLimit(){const t=this.options.ticks;let e,{maxTicksLimit:i,stepSize:s}=t;return s?(e=Math.ceil(this.max/s)-Math.floor(this.min/s)+1,e>1e3&&(console.warn(`scales.${this.id}.ticks.stepSize: ${s} would result generating up to ${e} ticks. Limiting to 1000.`),e=1e3)):(e=this.computeTickLimit(),i=i||11),i&&(e=Math.min(i,e)),e}computeTickLimit(){return Number.POSITIVE_INFINITY}buildTicks(){const t=this.options,e=t.ticks;let i=this.getTickLimit();i=Math.max(2,i);const n=function(t,e){const i=[],{bounds:n,step:o,min:a,max:r,precision:l,count:h,maxTicks:c,maxDigits:d,includeBounds:u}=t,f=o||1,g=c-1,{min:p,max:m}=e,b=!s(a),x=!s(r),_=!s(h),y=(m-p)/(d+1);let v,M,w,k,S=B((m-p)/g/f)*f;if(S<1e-14&&!b&&!x)return[{value:p},{value:m}];k=Math.ceil(m/S)-Math.floor(p/S),k>g&&(S=B(k*S/g/f)*f),s(l)||(v=Math.pow(10,l),S=Math.ceil(S*v)/v),"ticks"===n?(M=Math.floor(p/S)*S,w=Math.ceil(m/S)*S):(M=p,w=m),b&&x&&o&&H((r-a)/o,S/1e3)?(k=Math.round(Math.min((r-a)/S,c)),S=(r-a)/k,M=a,w=r):_?(M=b?a:M,w=x?r:w,k=h-1,S=(w-M)/k):(k=(w-M)/S,k=V(k,Math.round(k),S/1e3)?Math.round(k):Math.ceil(k));const P=Math.max(U(S),U(M));v=Math.pow(10,s(l)?P:l),M=Math.round(M*v)/v,w=Math.round(w*v)/v;let D=0;for(b&&(u&&M!==a?(i.push({value:a}),Mr)break;i.push({value:t})}return x&&u&&w!==r?i.length&&V(i[i.length-1].value,r,mo(r,y,t))?i[i.length-1].value=r:i.push({value:r}):x&&w!==r||i.push({value:w}),i}({maxTicks:i,bounds:t.bounds,min:t.min,max:t.max,precision:e.precision,step:e.stepSize,count:e.count,maxDigits:this._maxDigits(),horizontal:this.isHorizontal(),minRotation:e.minRotation||0,includeBounds:!1!==e.includeBounds},this._range||this);return"ticks"===t.bounds&&j(n,this,"value"),t.reverse?(n.reverse(),this.start=this.max,this.end=this.min):(this.start=this.min,this.end=this.max),n}configure(){const t=this.ticks;let e=this.min,i=this.max;if(super.configure(),this.options.offset&&t.length){const s=(i-e)/Math.max(t.length-1,1)/2;e-=s,i+=s}this._startValue=e,this._endValue=i,this._valueRange=i-e}getLabelForValue(t){return ne(t,this.chart.options.locale,this.options.ticks.format)}}class xo extends bo{static id="linear";static defaults={ticks:{callback:ae.formatters.numeric}};determineDataLimits(){const{min:t,max:e}=this.getMinMax(!0);this.min=a(t)?t:0,this.max=a(e)?e:1,this.handleTickRangeOptions()}computeTickLimit(){const t=this.isHorizontal(),e=t?this.width:this.height,i=$(this.options.ticks.minRotation),s=(t?Math.sin(i):Math.cos(i))||.001,n=this._resolveTickFontOptions(0);return Math.ceil(e/Math.min(40,n.lineHeight/s))}getPixelForValue(t){return null===t?NaN:this.getPixelForDecimal((t-this._startValue)/this._valueRange)}getValueForPixel(t){return this._startValue+this.getDecimalForPixel(t)*this._valueRange}}const _o=t=>Math.floor(z(t)),yo=(t,e)=>Math.pow(10,_o(t)+e);function vo(t){return 1===t/Math.pow(10,_o(t))}function Mo(t,e,i){const s=Math.pow(10,i),n=Math.floor(t/s);return Math.ceil(e/s)-n}function wo(t,{min:e,max:i}){e=r(t.min,e);const s=[],n=_o(e);let o=function(t,e){let i=_o(e-t);for(;Mo(t,e,i)>10;)i++;for(;Mo(t,e,i)<10;)i--;return Math.min(i,_o(t))}(e,i),a=o<0?Math.pow(10,Math.abs(o)):1;const l=Math.pow(10,o),h=n>o?Math.pow(10,n):0,c=Math.round((e-h)*a)/a,d=Math.floor((e-h)/l/10)*l*10;let u=Math.floor((c-d)/Math.pow(10,o)),f=r(t.min,Math.round((h+d+u*Math.pow(10,o))*a)/a);for(;f=10?u=u<15?15:20:u++,u>=20&&(o++,u=2,a=o>=0?1:a),f=Math.round((h+d+u*Math.pow(10,o))*a)/a;const g=r(t.max,f);return s.push({value:g,major:vo(g),significand:u}),s}class ko extends Js{static id="logarithmic";static defaults={ticks:{callback:ae.formatters.logarithmic,major:{enabled:!0}}};constructor(t){super(t),this.start=void 0,this.end=void 0,this._startValue=void 0,this._valueRange=0}parse(t,e){const i=bo.prototype.parse.apply(this,[t,e]);if(0!==i)return a(i)&&i>0?i:null;this._zero=!0}determineDataLimits(){const{min:t,max:e}=this.getMinMax(!0);this.min=a(t)?Math.max(0,t):null,this.max=a(e)?Math.max(0,e):null,this.options.beginAtZero&&(this._zero=!0),this._zero&&this.min!==this._suggestedMin&&!a(this._userMin)&&(this.min=t===yo(this.min,0)?yo(this.min,-1):yo(this.min,0)),this.handleTickRangeOptions()}handleTickRangeOptions(){const{minDefined:t,maxDefined:e}=this.getUserBounds();let i=this.min,s=this.max;const n=e=>i=t?i:e,o=t=>s=e?s:t;i===s&&(i<=0?(n(1),o(10)):(n(yo(i,-1)),o(yo(s,1)))),i<=0&&n(yo(s,-1)),s<=0&&o(yo(i,1)),this.min=i,this.max=s}buildTicks(){const t=this.options,e=wo({min:this._userMin,max:this._userMax},this);return"ticks"===t.bounds&&j(e,this,"value"),t.reverse?(e.reverse(),this.start=this.max,this.end=this.min):(this.start=this.min,this.end=this.max),e}getLabelForValue(t){return void 0===t?"0":ne(t,this.chart.options.locale,this.options.ticks.format)}configure(){const t=this.min;super.configure(),this._startValue=z(t),this._valueRange=z(this.max)-z(t)}getPixelForValue(t){return void 0!==t&&0!==t||(t=this.min),null===t||isNaN(t)?NaN:this.getPixelForDecimal(t===this.min?0:(z(t)-this._startValue)/this._valueRange)}getValueForPixel(t){const e=this.getDecimalForPixel(t);return Math.pow(10,this._startValue+e*this._valueRange)}}function So(t){const e=t.ticks;if(e.display&&t.display){const t=ki(e.backdropPadding);return l(e.font&&e.font.size,ue.font.size)+t.height}return 0}function Po(t,e,i,s,n){return t===s||t===n?{start:e-i/2,end:e+i/2}:tn?{start:e-i,end:e}:{start:e,end:e+i}}function Do(t){const e={l:t.left+t._padding.left,r:t.right-t._padding.right,t:t.top+t._padding.top,b:t.bottom-t._padding.bottom},i=Object.assign({},e),s=[],o=[],a=t._pointLabels.length,r=t.options.pointLabels,l=r.centerPointLabels?C/a:0;for(let u=0;ue.r&&(r=(s.end-e.r)/o,t.r=Math.max(t.r,e.r+r)),n.starte.b&&(l=(n.end-e.b)/a,t.b=Math.max(t.b,e.b+l))}function Oo(t,e,i){const s=t.drawingArea,{extra:n,additionalAngle:o,padding:a,size:r}=i,l=t.getPointPosition(e,s+n+a,o),h=Math.round(Y(G(l.angle+E))),c=function(t,e,i){90===i||270===i?t-=e/2:(i>270||i<90)&&(t-=e);return t}(l.y,r.h,h),d=function(t){if(0===t||180===t)return"center";if(t<180)return"left";return"right"}(h),u=function(t,e,i){"right"===i?t-=e:"center"===i&&(t-=e/2);return t}(l.x,r.w,d);return{visible:!0,x:l.x,y:c,textAlign:d,left:u,top:c,right:u+r.w,bottom:c+r.h}}function Ao(t,e){if(!e)return!0;const{left:i,top:s,right:n,bottom:o}=t;return!(Re({x:i,y:s},e)||Re({x:i,y:o},e)||Re({x:n,y:s},e)||Re({x:n,y:o},e))}function To(t,e,i){const{left:n,top:o,right:a,bottom:r}=i,{backdropColor:l}=e;if(!s(l)){const i=wi(e.borderRadius),s=ki(e.backdropPadding);t.fillStyle=l;const h=n-s.left,c=o-s.top,d=a-n+s.width,u=r-o+s.height;Object.values(i).some((t=>0!==t))?(t.beginPath(),He(t,{x:h,y:c,w:d,h:u,radius:i}),t.fill()):t.fillRect(h,c,d,u)}}function Lo(t,e,i,s){const{ctx:n}=t;if(i)n.arc(t.xCenter,t.yCenter,e,0,O);else{let i=t.getPointPosition(0,e);n.moveTo(i.x,i.y);for(let o=1;ot,padding:5,centerPointLabels:!1}};static defaultRoutes={"angleLines.color":"borderColor","pointLabels.color":"color","ticks.color":"color"};static descriptors={angleLines:{_fallback:"grid"}};constructor(t){super(t),this.xCenter=void 0,this.yCenter=void 0,this.drawingArea=void 0,this._pointLabels=[],this._pointLabelItems=[]}setDimensions(){const t=this._padding=ki(So(this.options)/2),e=this.width=this.maxWidth-t.width,i=this.height=this.maxHeight-t.height;this.xCenter=Math.floor(this.left+e/2+t.left),this.yCenter=Math.floor(this.top+i/2+t.top),this.drawingArea=Math.floor(Math.min(e,i)/2)}determineDataLimits(){const{min:t,max:e}=this.getMinMax(!1);this.min=a(t)&&!isNaN(t)?t:0,this.max=a(e)&&!isNaN(e)?e:0,this.handleTickRangeOptions()}computeTickLimit(){return Math.ceil(this.drawingArea/So(this.options))}generateTickLabels(t){bo.prototype.generateTickLabels.call(this,t),this._pointLabels=this.getLabels().map(((t,e)=>{const i=d(this.options.pointLabels.callback,[t,e],this);return i||0===i?i:""})).filter(((t,e)=>this.chart.getDataVisibility(e)))}fit(){const t=this.options;t.display&&t.pointLabels.display?Do(this):this.setCenterPoint(0,0,0,0)}setCenterPoint(t,e,i,s){this.xCenter+=Math.floor((t-e)/2),this.yCenter+=Math.floor((i-s)/2),this.drawingArea-=Math.min(this.drawingArea/2,Math.max(t,e,i,s))}getIndexAngle(t){return G(t*(O/(this._pointLabels.length||1))+$(this.options.startAngle||0))}getDistanceFromCenterForValue(t){if(s(t))return NaN;const e=this.drawingArea/(this.max-this.min);return this.options.reverse?(this.max-t)*e:(t-this.min)*e}getValueForDistanceFromCenter(t){if(s(t))return NaN;const e=t/(this.drawingArea/(this.max-this.min));return this.options.reverse?this.max-e:this.min+e}getPointLabelContext(t){const e=this._pointLabels||[];if(t>=0&&t=0;n--){const e=t._pointLabelItems[n];if(!e.visible)continue;const o=s.setContext(t.getPointLabelContext(n));To(i,o,e);const a=Si(o.font),{x:r,y:l,textAlign:h}=e;Ne(i,t._pointLabels[n],r,l+a.lineHeight/2,a,{color:o.color,textAlign:h,textBaseline:"middle"})}}(this,o),s.display&&this.ticks.forEach(((t,e)=>{if(0!==e){r=this.getDistanceFromCenterForValue(t.value);const i=this.getContext(e),a=s.setContext(i),l=n.setContext(i);!function(t,e,i,s,n){const o=t.ctx,a=e.circular,{color:r,lineWidth:l}=e;!a&&!s||!r||!l||i<0||(o.save(),o.strokeStyle=r,o.lineWidth=l,o.setLineDash(n.dash),o.lineDashOffset=n.dashOffset,o.beginPath(),Lo(t,i,a,s),o.closePath(),o.stroke(),o.restore())}(this,a,r,o,l)}})),i.display){for(t.save(),a=o-1;a>=0;a--){const s=i.setContext(this.getPointLabelContext(a)),{color:n,lineWidth:o}=s;o&&n&&(t.lineWidth=o,t.strokeStyle=n,t.setLineDash(s.borderDash),t.lineDashOffset=s.borderDashOffset,r=this.getDistanceFromCenterForValue(e.ticks.reverse?this.min:this.max),l=this.getPointPosition(a,r),t.beginPath(),t.moveTo(this.xCenter,this.yCenter),t.lineTo(l.x,l.y),t.stroke())}t.restore()}}drawBorder(){}drawLabels(){const t=this.ctx,e=this.options,i=e.ticks;if(!i.display)return;const s=this.getIndexAngle(0);let n,o;t.save(),t.translate(this.xCenter,this.yCenter),t.rotate(s),t.textAlign="center",t.textBaseline="middle",this.ticks.forEach(((s,a)=>{if(0===a&&!e.reverse)return;const r=i.setContext(this.getContext(a)),l=Si(r.font);if(n=this.getDistanceFromCenterForValue(this.ticks[a].value),r.showLabelBackdrop){t.font=l.string,o=t.measureText(s.label).width,t.fillStyle=r.backdropColor;const e=ki(r.backdropPadding);t.fillRect(-o/2-e.left,-n-l.size/2-e.top,o+e.width,l.size+e.height)}Ne(t,s.label,0,-n,l,{color:r.color,strokeColor:r.textStrokeColor,strokeWidth:r.textStrokeWidth})})),t.restore()}drawTitle(){}}const Ro={millisecond:{common:!0,size:1,steps:1e3},second:{common:!0,size:1e3,steps:60},minute:{common:!0,size:6e4,steps:60},hour:{common:!0,size:36e5,steps:24},day:{common:!0,size:864e5,steps:30},week:{common:!1,size:6048e5,steps:4},month:{common:!0,size:2628e6,steps:12},quarter:{common:!1,size:7884e6,steps:4},year:{common:!0,size:3154e7}},Io=Object.keys(Ro);function zo(t,e){return t-e}function Fo(t,e){if(s(e))return null;const i=t._adapter,{parser:n,round:o,isoWeekday:r}=t._parseOpts;let l=e;return"function"==typeof n&&(l=n(l)),a(l)||(l="string"==typeof n?i.parse(l,n):i.parse(l)),null===l?null:(o&&(l="week"!==o||!N(r)&&!0!==r?i.startOf(l,o):i.startOf(l,"isoWeek",r)),+l)}function Vo(t,e,i,s){const n=Io.length;for(let o=Io.indexOf(t);o=e?i[s]:i[n]]=!0}}else t[e]=!0}function Wo(t,e,i){const s=[],n={},o=e.length;let a,r;for(a=0;a=0&&(e[l].major=!0);return e}(t,s,n,i):s}class No extends Js{static id="time";static defaults={bounds:"data",adapters:{},time:{parser:!1,unit:!1,round:!1,isoWeekday:!1,minUnit:"millisecond",displayFormats:{}},ticks:{source:"auto",callback:!1,major:{enabled:!1}}};constructor(t){super(t),this._cache={data:[],labels:[],all:[]},this._unit="day",this._majorUnit=void 0,this._offsets={},this._normalized=!1,this._parseOpts=void 0}init(t,e={}){const i=t.time||(t.time={}),s=this._adapter=new Rn._date(t.adapters.date);s.init(e),x(i.displayFormats,s.formats()),this._parseOpts={parser:i.parser,round:i.round,isoWeekday:i.isoWeekday},super.init(t),this._normalized=e.normalized}parse(t,e){return void 0===t?null:Fo(this,t)}beforeLayout(){super.beforeLayout(),this._cache={data:[],labels:[],all:[]}}determineDataLimits(){const t=this.options,e=this._adapter,i=t.time.unit||"day";let{min:s,max:n,minDefined:o,maxDefined:r}=this.getUserBounds();function l(t){o||isNaN(t.min)||(s=Math.min(s,t.min)),r||isNaN(t.max)||(n=Math.max(n,t.max))}o&&r||(l(this._getLabelBounds()),"ticks"===t.bounds&&"labels"===t.ticks.source||l(this.getMinMax(!1))),s=a(s)&&!isNaN(s)?s:+e.startOf(Date.now(),i),n=a(n)&&!isNaN(n)?n:+e.endOf(Date.now(),i)+1,this.min=Math.min(s,n-1),this.max=Math.max(s+1,n)}_getLabelBounds(){const t=this.getLabelTimestamps();let e=Number.POSITIVE_INFINITY,i=Number.NEGATIVE_INFINITY;return t.length&&(e=t[0],i=t[t.length-1]),{min:e,max:i}}buildTicks(){const t=this.options,e=t.time,i=t.ticks,s="labels"===i.source?this.getLabelTimestamps():this._generate();"ticks"===t.bounds&&s.length&&(this.min=this._userMin||s[0],this.max=this._userMax||s[s.length-1]);const n=this.min,o=nt(s,n,this.max);return this._unit=e.unit||(i.autoSkip?Vo(e.minUnit,this.min,this.max,this._getLabelCapacity(n)):function(t,e,i,s,n){for(let o=Io.length-1;o>=Io.indexOf(i);o--){const i=Io[o];if(Ro[i].common&&t._adapter.diff(n,s,i)>=e-1)return i}return Io[i?Io.indexOf(i):0]}(this,o.length,e.minUnit,this.min,this.max)),this._majorUnit=i.major.enabled&&"year"!==this._unit?function(t){for(let e=Io.indexOf(t)+1,i=Io.length;e+t.value)))}initOffsets(t=[]){let e,i,s=0,n=0;this.options.offset&&t.length&&(e=this.getDecimalForValue(t[0]),s=1===t.length?1-e:(this.getDecimalForValue(t[1])-e)/2,i=this.getDecimalForValue(t[t.length-1]),n=1===t.length?i:(i-this.getDecimalForValue(t[t.length-2]))/2);const o=t.length<3?.5:.25;s=J(s,0,o),n=J(n,0,o),this._offsets={start:s,end:n,factor:1/(s+1+n)}}_generate(){const t=this._adapter,e=this.min,i=this.max,s=this.options,n=s.time,o=n.unit||Vo(n.minUnit,e,i,this._getLabelCapacity(e)),a=l(s.ticks.stepSize,1),r="week"===o&&n.isoWeekday,h=N(r)||!0===r,c={};let d,u,f=e;if(h&&(f=+t.startOf(f,"isoWeek",r)),f=+t.startOf(f,h?"day":o),t.diff(i,e,o)>1e5*a)throw new Error(e+" and "+i+" are too far apart with stepSize of "+a+" "+o);const g="data"===s.ticks.source&&this.getDataTimestamps();for(d=f,u=0;d+t))}getLabelForValue(t){const e=this._adapter,i=this.options.time;return i.tooltipFormat?e.format(t,i.tooltipFormat):e.format(t,i.displayFormats.datetime)}format(t,e){const i=this.options.time.displayFormats,s=this._unit,n=e||i[s];return this._adapter.format(t,n)}_tickFormatFunction(t,e,i,s){const n=this.options,o=n.ticks.callback;if(o)return d(o,[t,e,i],this);const a=n.time.displayFormats,r=this._unit,l=this._majorUnit,h=r&&a[r],c=l&&a[l],u=i[e],f=l&&c&&u&&u.major;return this._adapter.format(t,s||(f?c:h))}generateTickLabels(t){let e,i,s;for(e=0,i=t.length;e0?a:1}getDataTimestamps(){let t,e,i=this._cache.data||[];if(i.length)return i;const s=this.getMatchingVisibleMetas();if(this._normalized&&s.length)return this._cache.data=s[0].controller.getAllParsedValues(this);for(t=0,e=s.length;t=t[r].pos&&e<=t[l].pos&&({lo:r,hi:l}=it(t,"pos",e)),({pos:s,time:o}=t[r]),({pos:n,time:a}=t[l])):(e>=t[r].time&&e<=t[l].time&&({lo:r,hi:l}=it(t,"time",e)),({time:s,pos:o}=t[r]),({time:n,pos:a}=t[l]));const h=n-s;return h?o+(a-o)*(e-s)/h:o}var jo=Object.freeze({__proto__:null,CategoryScale:class extends Js{static id="category";static defaults={ticks:{callback:po}};constructor(t){super(t),this._startValue=void 0,this._valueRange=0,this._addedLabels=[]}init(t){const e=this._addedLabels;if(e.length){const t=this.getLabels();for(const{index:i,label:s}of e)t[i]===s&&t.splice(i,1);this._addedLabels=[]}super.init(t)}parse(t,e){if(s(t))return null;const i=this.getLabels();return((t,e)=>null===t?null:J(Math.round(t),0,e))(e=isFinite(e)&&i[e]===t?e:go(i,t,l(e,t),this._addedLabels),i.length-1)}determineDataLimits(){const{minDefined:t,maxDefined:e}=this.getUserBounds();let{min:i,max:s}=this.getMinMax(!0);"ticks"===this.options.bounds&&(t||(i=0),e||(s=this.getLabels().length-1)),this.min=i,this.max=s}buildTicks(){const t=this.min,e=this.max,i=this.options.offset,s=[];let n=this.getLabels();n=0===t&&e===n.length-1?n:n.slice(t,e+1),this._valueRange=Math.max(n.length-(i?0:1),1),this._startValue=this.min-(i?.5:0);for(let i=t;i<=e;i++)s.push({value:i});return s}getLabelForValue(t){return po.call(this,t)}configure(){super.configure(),this.isHorizontal()||(this._reversePixels=!this._reversePixels)}getPixelForValue(t){return"number"!=typeof t&&(t=this.parse(t)),null===t?NaN:this.getPixelForDecimal((t-this._startValue)/this._valueRange)}getPixelForTick(t){const e=this.ticks;return t<0||t>e.length-1?null:this.getPixelForValue(e[t].value)}getValueForPixel(t){return Math.round(this._startValue+this.getDecimalForPixel(t)*this._valueRange)}getBasePixel(){return this.bottom}},LinearScale:xo,LogarithmicScale:ko,RadialLinearScale:Eo,TimeScale:No,TimeSeriesScale:class extends No{static id="timeseries";static defaults=No.defaults;constructor(t){super(t),this._table=[],this._minPos=void 0,this._tableRange=void 0}initOffsets(){const t=this._getTimestampsForTable(),e=this._table=this.buildLookupTable(t);this._minPos=Ho(e,this.min),this._tableRange=Ho(e,this.max)-this._minPos,super.initOffsets(t)}buildLookupTable(t){const{min:e,max:i}=this,s=[],n=[];let o,a,r,l,h;for(o=0,a=t.length;o=e&&l<=i&&s.push(l);if(s.length<2)return[{time:e,pos:0},{time:i,pos:1}];for(o=0,a=s.length;ot-e))}_getTimestampsForTable(){let t=this._cache.all||[];if(t.length)return t;const e=this.getDataTimestamps(),i=this.getLabelTimestamps();return t=e.length&&i.length?this.normalize(e.concat(i)):e.length?e:i,t=this._cache.all=t,t}getDecimalForValue(t){return(Ho(this._table,t)-this._minPos)/this._tableRange}getValueForPixel(t){const e=this._offsets,i=this.getDecimalForPixel(t)/e.factor-e.end;return Ho(this._table,i*this._tableRange+this._minPos,!0)}}});const $o=["rgb(54, 162, 235)","rgb(255, 99, 132)","rgb(255, 159, 64)","rgb(255, 205, 86)","rgb(75, 192, 192)","rgb(153, 102, 255)","rgb(201, 203, 207)"],Yo=$o.map((t=>t.replace("rgb(","rgba(").replace(")",", 0.5)")));function Uo(t){return $o[t%$o.length]}function Xo(t){return Yo[t%Yo.length]}function qo(t){let e=0;return(i,s)=>{const n=t.getDatasetMeta(s).controller;n instanceof jn?e=function(t,e){return t.backgroundColor=t.data.map((()=>Uo(e++))),e}(i,e):n instanceof $n?e=function(t,e){return t.backgroundColor=t.data.map((()=>Xo(e++))),e}(i,e):n&&(e=function(t,e){return t.borderColor=Uo(e),t.backgroundColor=Xo(e),++e}(i,e))}}function Ko(t){let e;for(e in t)if(t[e].borderColor||t[e].backgroundColor)return!0;return!1}var Go={id:"colors",defaults:{enabled:!0,forceOverride:!1},beforeLayout(t,e,i){if(!i.enabled)return;const{data:{datasets:s},options:n}=t.config,{elements:o}=n;if(!i.forceOverride&&(Ko(s)||(a=n)&&(a.borderColor||a.backgroundColor)||o&&Ko(o)))return;var a;const r=qo(t);s.forEach(r)}};function Zo(t){if(t._decimated){const e=t._data;delete t._decimated,delete t._data,Object.defineProperty(t,"data",{configurable:!0,enumerable:!0,writable:!0,value:e})}}function Jo(t){t.data.datasets.forEach((t=>{Zo(t)}))}var Qo={id:"decimation",defaults:{algorithm:"min-max",enabled:!1},beforeElementsUpdate:(t,e,i)=>{if(!i.enabled)return void Jo(t);const n=t.width;t.data.datasets.forEach(((e,o)=>{const{_data:a,indexAxis:r}=e,l=t.getDatasetMeta(o),h=a||e.data;if("y"===Pi([r,t.options.indexAxis]))return;if(!l.controller.supportsDecimation)return;const c=t.scales[l.xAxisID];if("linear"!==c.type&&"time"!==c.type)return;if(t.options.parsing)return;let{start:d,count:u}=function(t,e){const i=e.length;let s,n=0;const{iScale:o}=t,{min:a,max:r,minDefined:l,maxDefined:h}=o.getUserBounds();return l&&(n=J(it(e,o.axis,a).lo,0,i-1)),s=h?J(it(e,o.axis,r).hi+1,n,i)-n:i-n,{start:n,count:s}}(l,h);if(u<=(i.threshold||4*n))return void Zo(e);let f;switch(s(a)&&(e._data=h,delete e.data,Object.defineProperty(e,"data",{configurable:!0,enumerable:!0,get:function(){return this._decimated},set:function(t){this._data=t}})),i.algorithm){case"lttb":f=function(t,e,i,s,n){const o=n.samples||s;if(o>=i)return t.slice(e,e+i);const a=[],r=(i-2)/(o-2);let l=0;const h=e+i-1;let c,d,u,f,g,p=e;for(a[l++]=t[p],c=0;cu&&(u=f,d=t[s],g=s);a[l++]=d,p=g}return a[l++]=t[h],a}(h,d,u,n,i);break;case"min-max":f=function(t,e,i,n){let o,a,r,l,h,c,d,u,f,g,p=0,m=0;const b=[],x=e+i-1,_=t[e].x,y=t[x].x-_;for(o=e;og&&(g=l,d=o),p=(m*p+a.x)/++m;else{const i=o-1;if(!s(c)&&!s(d)){const e=Math.min(c,d),s=Math.max(c,d);e!==u&&e!==i&&b.push({...t[e],x:p}),s!==u&&s!==i&&b.push({...t[s],x:p})}o>0&&i!==u&&b.push(t[i]),b.push(a),h=e,m=0,f=g=l,c=d=u=o}}return b}(h,d,u,n);break;default:throw new Error(`Unsupported decimation algorithm '${i.algorithm}'`)}e._decimated=f}))},destroy(t){Jo(t)}};function ta(t,e,i,s){if(s)return;let n=e[t],o=i[t];return"angle"===t&&(n=G(n),o=G(o)),{property:t,start:n,end:o}}function ea(t,e,i){for(;e>t;e--){const t=i[e];if(!isNaN(t.x)&&!isNaN(t.y))break}return e}function ia(t,e,i,s){return t&&e?s(t[i],e[i]):t?t[i]:e?e[i]:0}function sa(t,e){let i=[],s=!1;return n(t)?(s=!0,i=t):i=function(t,e){const{x:i=null,y:s=null}=t||{},n=e.points,o=[];return e.segments.forEach((({start:t,end:e})=>{e=ea(t,e,n);const a=n[t],r=n[e];null!==s?(o.push({x:a.x,y:s}),o.push({x:r.x,y:s})):null!==i&&(o.push({x:i,y:a.y}),o.push({x:i,y:r.y}))})),o}(t,e),i.length?new no({points:i,options:{tension:0},_loop:s,_fullLoop:s}):null}function na(t){return t&&!1!==t.fill}function oa(t,e,i){let s=t[e].fill;const n=[e];let o;if(!i)return s;for(;!1!==s&&-1===n.indexOf(s);){if(!a(s))return s;if(o=t[s],!o)return!1;if(o.visible)return s;n.push(s),s=o.fill}return!1}function aa(t,e,i){const s=function(t){const e=t.options,i=e.fill;let s=l(i&&i.target,i);void 0===s&&(s=!!e.backgroundColor);if(!1===s||null===s)return!1;if(!0===s)return"origin";return s}(t);if(o(s))return!isNaN(s.value)&&s;let n=parseFloat(s);return a(n)&&Math.floor(n)===n?function(t,e,i,s){"-"!==t&&"+"!==t||(i=e+i);if(i===e||i<0||i>=s)return!1;return i}(s[0],e,n,i):["origin","start","end","stack","shape"].indexOf(s)>=0&&s}function ra(t,e,i){const s=[];for(let n=0;n=0;--e){const i=n[e].$filler;i&&(i.line.updateControlPoints(o,i.axis),s&&i.fill&&da(t.ctx,i,o))}},beforeDatasetsDraw(t,e,i){if("beforeDatasetsDraw"!==i.drawTime)return;const s=t.getSortedVisibleDatasetMetas();for(let e=s.length-1;e>=0;--e){const i=s[e].$filler;na(i)&&da(t.ctx,i,t.chartArea)}},beforeDatasetDraw(t,e,i){const s=e.meta.$filler;na(s)&&"beforeDatasetDraw"===i.drawTime&&da(t.ctx,s,t.chartArea)},defaults:{propagate:!0,drawTime:"beforeDatasetDraw"}};const ba=(t,e)=>{let{boxHeight:i=e,boxWidth:s=e}=t;return t.usePointStyle&&(i=Math.min(i,e),s=t.pointStyleWidth||Math.min(s,e)),{boxWidth:s,boxHeight:i,itemHeight:Math.max(e,i)}};class xa extends Hs{constructor(t){super(),this._added=!1,this.legendHitBoxes=[],this._hoveredItem=null,this.doughnutMode=!1,this.chart=t.chart,this.options=t.options,this.ctx=t.ctx,this.legendItems=void 0,this.columnSizes=void 0,this.lineWidths=void 0,this.maxHeight=void 0,this.maxWidth=void 0,this.top=void 0,this.bottom=void 0,this.left=void 0,this.right=void 0,this.height=void 0,this.width=void 0,this._margins=void 0,this.position=void 0,this.weight=void 0,this.fullSize=void 0}update(t,e,i){this.maxWidth=t,this.maxHeight=e,this._margins=i,this.setDimensions(),this.buildLabels(),this.fit()}setDimensions(){this.isHorizontal()?(this.width=this.maxWidth,this.left=this._margins.left,this.right=this.width):(this.height=this.maxHeight,this.top=this._margins.top,this.bottom=this.height)}buildLabels(){const t=this.options.labels||{};let e=d(t.generateLabels,[this.chart],this)||[];t.filter&&(e=e.filter((e=>t.filter(e,this.chart.data)))),t.sort&&(e=e.sort(((e,i)=>t.sort(e,i,this.chart.data)))),this.options.reverse&&e.reverse(),this.legendItems=e}fit(){const{options:t,ctx:e}=this;if(!t.display)return void(this.width=this.height=0);const i=t.labels,s=Si(i.font),n=s.size,o=this._computeTitleHeight(),{boxWidth:a,itemHeight:r}=ba(i,n);let l,h;e.font=s.string,this.isHorizontal()?(l=this.maxWidth,h=this._fitRows(o,n,a,r)+10):(h=this.maxHeight,l=this._fitCols(o,s,a,r)+10),this.width=Math.min(l,t.maxWidth||this.maxWidth),this.height=Math.min(h,t.maxHeight||this.maxHeight)}_fitRows(t,e,i,s){const{ctx:n,maxWidth:o,options:{labels:{padding:a}}}=this,r=this.legendHitBoxes=[],l=this.lineWidths=[0],h=s+a;let c=t;n.textAlign="left",n.textBaseline="middle";let d=-1,u=-h;return this.legendItems.forEach(((t,f)=>{const g=i+e/2+n.measureText(t.text).width;(0===f||l[l.length-1]+g+2*a>o)&&(c+=h,l[l.length-(f>0?0:1)]=0,u+=h,d++),r[f]={left:0,top:u,row:d,width:g,height:s},l[l.length-1]+=g+a})),c}_fitCols(t,e,i,s){const{ctx:n,maxHeight:o,options:{labels:{padding:a}}}=this,r=this.legendHitBoxes=[],l=this.columnSizes=[],h=o-t;let c=a,d=0,u=0,f=0,g=0;return this.legendItems.forEach(((t,o)=>{const{itemWidth:p,itemHeight:m}=function(t,e,i,s,n){const o=function(t,e,i,s){let n=t.text;n&&"string"!=typeof n&&(n=n.reduce(((t,e)=>t.length>e.length?t:e)));return e+i.size/2+s.measureText(n).width}(s,t,e,i),a=function(t,e,i){let s=t;"string"!=typeof e.text&&(s=_a(e,i));return s}(n,s,e.lineHeight);return{itemWidth:o,itemHeight:a}}(i,e,n,t,s);o>0&&u+m+2*a>h&&(c+=d+a,l.push({width:d,height:u}),f+=d+a,g++,d=u=0),r[o]={left:f,top:u,col:g,width:p,height:m},d=Math.max(d,p),u+=m+a})),c+=d,l.push({width:d,height:u}),c}adjustHitBoxes(){if(!this.options.display)return;const t=this._computeTitleHeight(),{legendHitBoxes:e,options:{align:i,labels:{padding:s},rtl:n}}=this,o=Oi(n,this.left,this.width);if(this.isHorizontal()){let n=0,a=ft(i,this.left+s,this.right-this.lineWidths[n]);for(const r of e)n!==r.row&&(n=r.row,a=ft(i,this.left+s,this.right-this.lineWidths[n])),r.top+=this.top+t+s,r.left=o.leftForLtr(o.x(a),r.width),a+=r.width+s}else{let n=0,a=ft(i,this.top+t+s,this.bottom-this.columnSizes[n].height);for(const r of e)r.col!==n&&(n=r.col,a=ft(i,this.top+t+s,this.bottom-this.columnSizes[n].height)),r.top=a,r.left+=this.left+s,r.left=o.leftForLtr(o.x(r.left),r.width),a+=r.height+s}}isHorizontal(){return"top"===this.options.position||"bottom"===this.options.position}draw(){if(this.options.display){const t=this.ctx;Ie(t,this),this._draw(),ze(t)}}_draw(){const{options:t,columnSizes:e,lineWidths:i,ctx:s}=this,{align:n,labels:o}=t,a=ue.color,r=Oi(t.rtl,this.left,this.width),h=Si(o.font),{padding:c}=o,d=h.size,u=d/2;let f;this.drawTitle(),s.textAlign=r.textAlign("left"),s.textBaseline="middle",s.lineWidth=.5,s.font=h.string;const{boxWidth:g,boxHeight:p,itemHeight:m}=ba(o,d),b=this.isHorizontal(),x=this._computeTitleHeight();f=b?{x:ft(n,this.left+c,this.right-i[0]),y:this.top+c+x,line:0}:{x:this.left+c,y:ft(n,this.top+x+c,this.bottom-e[0].height),line:0},Ai(this.ctx,t.textDirection);const _=m+c;this.legendItems.forEach(((y,v)=>{s.strokeStyle=y.fontColor,s.fillStyle=y.fontColor;const M=s.measureText(y.text).width,w=r.textAlign(y.textAlign||(y.textAlign=o.textAlign)),k=g+u+M;let S=f.x,P=f.y;r.setWidth(this.width),b?v>0&&S+k+c>this.right&&(P=f.y+=_,f.line++,S=f.x=ft(n,this.left+c,this.right-i[f.line])):v>0&&P+_>this.bottom&&(S=f.x=S+e[f.line].width+c,f.line++,P=f.y=ft(n,this.top+x+c,this.bottom-e[f.line].height));if(function(t,e,i){if(isNaN(g)||g<=0||isNaN(p)||p<0)return;s.save();const n=l(i.lineWidth,1);if(s.fillStyle=l(i.fillStyle,a),s.lineCap=l(i.lineCap,"butt"),s.lineDashOffset=l(i.lineDashOffset,0),s.lineJoin=l(i.lineJoin,"miter"),s.lineWidth=n,s.strokeStyle=l(i.strokeStyle,a),s.setLineDash(l(i.lineDash,[])),o.usePointStyle){const a={radius:p*Math.SQRT2/2,pointStyle:i.pointStyle,rotation:i.rotation,borderWidth:n},l=r.xPlus(t,g/2);Ee(s,a,l,e+u,o.pointStyleWidth&&g)}else{const o=e+Math.max((d-p)/2,0),a=r.leftForLtr(t,g),l=wi(i.borderRadius);s.beginPath(),Object.values(l).some((t=>0!==t))?He(s,{x:a,y:o,w:g,h:p,radius:l}):s.rect(a,o,g,p),s.fill(),0!==n&&s.stroke()}s.restore()}(r.x(S),P,y),S=gt(w,S+g+u,b?S+k:this.right,t.rtl),function(t,e,i){Ne(s,i.text,t,e+m/2,h,{strikethrough:i.hidden,textAlign:r.textAlign(i.textAlign)})}(r.x(S),P,y),b)f.x+=k+c;else if("string"!=typeof y.text){const t=h.lineHeight;f.y+=_a(y,t)+c}else f.y+=_})),Ti(this.ctx,t.textDirection)}drawTitle(){const t=this.options,e=t.title,i=Si(e.font),s=ki(e.padding);if(!e.display)return;const n=Oi(t.rtl,this.left,this.width),o=this.ctx,a=e.position,r=i.size/2,l=s.top+r;let h,c=this.left,d=this.width;if(this.isHorizontal())d=Math.max(...this.lineWidths),h=this.top+l,c=ft(t.align,c,this.right-d);else{const e=this.columnSizes.reduce(((t,e)=>Math.max(t,e.height)),0);h=l+ft(t.align,this.top,this.bottom-e-t.labels.padding-this._computeTitleHeight())}const u=ft(a,c,c+d);o.textAlign=n.textAlign(ut(a)),o.textBaseline="middle",o.strokeStyle=e.color,o.fillStyle=e.color,o.font=i.string,Ne(o,e.text,u,h,i)}_computeTitleHeight(){const t=this.options.title,e=Si(t.font),i=ki(t.padding);return t.display?e.lineHeight+i.height:0}_getLegendItemAt(t,e){let i,s,n;if(tt(t,this.left,this.right)&&tt(e,this.top,this.bottom))for(n=this.legendHitBoxes,i=0;it.chart.options.color,boxWidth:40,padding:10,generateLabels(t){const e=t.data.datasets,{labels:{usePointStyle:i,pointStyle:s,textAlign:n,color:o,useBorderRadius:a,borderRadius:r}}=t.legend.options;return t._getSortedDatasetMetas().map((t=>{const l=t.controller.getStyle(i?0:void 0),h=ki(l.borderWidth);return{text:e[t.index].label,fillStyle:l.backgroundColor,fontColor:o,hidden:!t.visible,lineCap:l.borderCapStyle,lineDash:l.borderDash,lineDashOffset:l.borderDashOffset,lineJoin:l.borderJoinStyle,lineWidth:(h.width+h.height)/4,strokeStyle:l.borderColor,pointStyle:s||l.pointStyle,rotation:l.rotation,textAlign:n||l.textAlign,borderRadius:a&&(r||l.borderRadius),datasetIndex:t.index}}),this)}},title:{color:t=>t.chart.options.color,display:!1,position:"center",text:""}},descriptors:{_scriptable:t=>!t.startsWith("on"),labels:{_scriptable:t=>!["generateLabels","filter","sort"].includes(t)}}};class va extends Hs{constructor(t){super(),this.chart=t.chart,this.options=t.options,this.ctx=t.ctx,this._padding=void 0,this.top=void 0,this.bottom=void 0,this.left=void 0,this.right=void 0,this.width=void 0,this.height=void 0,this.position=void 0,this.weight=void 0,this.fullSize=void 0}update(t,e){const i=this.options;if(this.left=0,this.top=0,!i.display)return void(this.width=this.height=this.right=this.bottom=0);this.width=this.right=t,this.height=this.bottom=e;const s=n(i.text)?i.text.length:1;this._padding=ki(i.padding);const o=s*Si(i.font).lineHeight+this._padding.height;this.isHorizontal()?this.height=o:this.width=o}isHorizontal(){const t=this.options.position;return"top"===t||"bottom"===t}_drawArgs(t){const{top:e,left:i,bottom:s,right:n,options:o}=this,a=o.align;let r,l,h,c=0;return this.isHorizontal()?(l=ft(a,i,n),h=e+t,r=n-i):("left"===o.position?(l=i+t,h=ft(a,s,e),c=-.5*C):(l=n-t,h=ft(a,e,s),c=.5*C),r=s-e),{titleX:l,titleY:h,maxWidth:r,rotation:c}}draw(){const t=this.ctx,e=this.options;if(!e.display)return;const i=Si(e.font),s=i.lineHeight/2+this._padding.top,{titleX:n,titleY:o,maxWidth:a,rotation:r}=this._drawArgs(s);Ne(t,e.text,0,0,i,{color:e.color,maxWidth:a,rotation:r,textAlign:ut(e.align),textBaseline:"middle",translation:[n,o]})}}var Ma={id:"title",_element:va,start(t,e,i){!function(t,e){const i=new va({ctx:t.ctx,options:e,chart:t});as.configure(t,i,e),as.addBox(t,i),t.titleBlock=i}(t,i)},stop(t){const e=t.titleBlock;as.removeBox(t,e),delete t.titleBlock},beforeUpdate(t,e,i){const s=t.titleBlock;as.configure(t,s,i),s.options=i},defaults:{align:"center",display:!1,font:{weight:"bold"},fullSize:!0,padding:10,position:"top",text:"",weight:2e3},defaultRoutes:{color:"color"},descriptors:{_scriptable:!0,_indexable:!1}};const wa=new WeakMap;var ka={id:"subtitle",start(t,e,i){const s=new va({ctx:t.ctx,options:i,chart:t});as.configure(t,s,i),as.addBox(t,s),wa.set(t,s)},stop(t){as.removeBox(t,wa.get(t)),wa.delete(t)},beforeUpdate(t,e,i){const s=wa.get(t);as.configure(t,s,i),s.options=i},defaults:{align:"center",display:!1,font:{weight:"normal"},fullSize:!0,padding:0,position:"top",text:"",weight:1500},defaultRoutes:{color:"color"},descriptors:{_scriptable:!0,_indexable:!1}};const Sa={average(t){if(!t.length)return!1;let e,i,s=0,n=0,o=0;for(e=0,i=t.length;e-1?t.split("\n"):t}function Ca(t,e){const{element:i,datasetIndex:s,index:n}=e,o=t.getDatasetMeta(s).controller,{label:a,value:r}=o.getLabelAndValue(n);return{chart:t,label:a,parsed:o.getParsed(n),raw:t.data.datasets[s].data[n],formattedValue:r,dataset:o.getDataset(),dataIndex:n,datasetIndex:s,element:i}}function Oa(t,e){const i=t.chart.ctx,{body:s,footer:n,title:o}=t,{boxWidth:a,boxHeight:r}=e,l=Si(e.bodyFont),h=Si(e.titleFont),c=Si(e.footerFont),d=o.length,f=n.length,g=s.length,p=ki(e.padding);let m=p.height,b=0,x=s.reduce(((t,e)=>t+e.before.length+e.lines.length+e.after.length),0);if(x+=t.beforeBody.length+t.afterBody.length,d&&(m+=d*h.lineHeight+(d-1)*e.titleSpacing+e.titleMarginBottom),x){m+=g*(e.displayColors?Math.max(r,l.lineHeight):l.lineHeight)+(x-g)*l.lineHeight+(x-1)*e.bodySpacing}f&&(m+=e.footerMarginTop+f*c.lineHeight+(f-1)*e.footerSpacing);let _=0;const y=function(t){b=Math.max(b,i.measureText(t).width+_)};return 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t=Sa[i.position].call(this,s,this._eventPosition);o=this._createItems(i),this.title=this.getTitle(o,i),this.beforeBody=this.getBeforeBody(o,i),this.body=this.getBody(o,i),this.afterBody=this.getAfterBody(o,i),this.footer=this.getFooter(o,i);const e=this._size=Oa(this,i),a=Object.assign({},t,e),r=Ta(this.chart,i,a),l=La(i,a,r,this.chart);this.xAlign=r.xAlign,this.yAlign=r.yAlign,n={opacity:1,x:l.x,y:l.y,width:e.width,height:e.height,caretX:t.x,caretY:t.y}}else 0!==this.opacity&&(n={opacity:0});this._tooltipItems=o,this.$context=void 0,n&&this._resolveAnimations().update(this,n),t&&i.external&&i.external.call(this,{chart:this.chart,tooltip:this,replay:e})}drawCaret(t,e,i,s){const n=this.getCaretPosition(t,i,s);e.lineTo(n.x1,n.y1),e.lineTo(n.x2,n.y2),e.lineTo(n.x3,n.y3)}getCaretPosition(t,e,i){const{xAlign:s,yAlign:n}=this,{caretSize:o,cornerRadius:a}=i,{topLeft:r,topRight:l,bottomLeft:h,bottomRight:c}=wi(a),{x:d,y:u}=t,{width:f,height:g}=e;let 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b,x,_,y,v,M,w;for(e.textAlign=o,e.textBaseline="middle",e.font=c.string,t.x=Ea(this,m,i),e.fillStyle=i.bodyColor,u(this.beforeBody,p),f=a&&"right"!==m?"center"===o?l/2+h:l+2+h:0,y=0,M=s.length;y0&&e.stroke()}_updateAnimationTarget(t){const e=this.chart,i=this.$animations,s=i&&i.x,n=i&&i.y;if(s||n){const i=Sa[t.position].call(this,this._active,this._eventPosition);if(!i)return;const o=this._size=Oa(this,t),a=Object.assign({},i,this._size),r=Ta(e,t,a),l=La(t,a,r,e);s._to===l.x&&n._to===l.y||(this.xAlign=r.xAlign,this.yAlign=r.yAlign,this.width=o.width,this.height=o.height,this.caretX=i.x,this.caretY=i.y,this._resolveAnimations().update(this,l))}}_willRender(){return!!this.opacity}draw(t){const e=this.options.setContext(this.getContext());let i=this.opacity;if(!i)return;this._updateAnimationTarget(e);const s={width:this.width,height:this.height},n={x:this.x,y:this.y};i=Math.abs(i)<.001?0:i;const o=ki(e.padding),a=this.title.length||this.beforeBody.length||this.body.length||this.afterBody.length||this.footer.length;e.enabled&&a&&(t.save(),t.globalAlpha=i,this.drawBackground(n,t,s,e),Ai(t,e.textDirection),n.y+=o.top,this.drawTitle(n,t,e),this.drawBody(n,t,e),this.drawFooter(n,t,e),Ti(t,e.textDirection),t.restore())}getActiveElements(){return this._active||[]}setActiveElements(t,e){const i=this._active,s=t.map((({datasetIndex:t,index:e})=>{const i=this.chart.getDatasetMeta(t);if(!i)throw new Error("Cannot find a dataset at index "+t);return{datasetIndex:t,element:i.data[e],index:e}})),n=!f(i,s),o=this._positionChanged(s,e);(n||o)&&(this._active=s,this._eventPosition=e,this._ignoreReplayEvents=!0,this.update(!0))}handleEvent(t,e,i=!0){if(e&&this._ignoreReplayEvents)return!1;this._ignoreReplayEvents=!1;const s=this.options,n=this._active||[],o=this._getActiveElements(t,n,e,i),a=this._positionChanged(o,t),r=e||!f(o,n)||a;return r&&(this._active=o,(s.enabled||s.external)&&(this._eventPosition={x:t.x,y:t.y},this.update(!0,e))),r}_getActiveElements(t,e,i,s){const n=this.options;if("mouseout"===t.type)return[];if(!s)return e;const o=this.chart.getElementsAtEventForMode(t,n.mode,n,i);return n.reverse&&o.reverse(),o}_positionChanged(t,e){const{caretX:i,caretY:s,options:n}=this,o=Sa[n.position].call(this,t,e);return!1!==o&&(i!==o.x||s!==o.y)}}var Ba={id:"tooltip",_element:Va,positioners:Sa,afterInit(t,e,i){i&&(t.tooltip=new Va({chart:t,options:i}))},beforeUpdate(t,e,i){t.tooltip&&t.tooltip.initialize(i)},reset(t,e,i){t.tooltip&&t.tooltip.initialize(i)},afterDraw(t){const e=t.tooltip;if(e&&e._willRender()){const i={tooltip:e};if(!1===t.notifyPlugins("beforeTooltipDraw",{...i,cancelable:!0}))return;e.draw(t.ctx),t.notifyPlugins("afterTooltipDraw",i)}},afterEvent(t,e){if(t.tooltip){const i=e.replay;t.tooltip.handleEvent(e.event,i,e.inChartArea)&&(e.changed=!0)}},defaults:{enabled:!0,external:null,position:"average",backgroundColor:"rgba(0,0,0,0.8)",titleColor:"#fff",titleFont:{weight:"bold"},titleSpacing:2,titleMarginBottom:6,titleAlign:"left",bodyColor:"#fff",bodySpacing:2,bodyFont:{},bodyAlign:"left",footerColor:"#fff",footerSpacing:2,footerMarginTop:6,footerFont:{weight:"bold"},footerAlign:"left",padding:6,caretPadding:2,caretSize:5,cornerRadius:6,boxHeight:(t,e)=>e.bodyFont.size,boxWidth:(t,e)=>e.bodyFont.size,multiKeyBackground:"#fff",displayColors:!0,boxPadding:0,borderColor:"rgba(0,0,0,0)",borderWidth:0,animation:{duration:400,easing:"easeOutQuart"},animations:{numbers:{type:"number",properties:["x","y","width","height","caretX","caretY"]},opacity:{easing:"linear",duration:200}},callbacks:za},defaultRoutes:{bodyFont:"font",footerFont:"font",titleFont:"font"},descriptors:{_scriptable:t=>"filter"!==t&&"itemSort"!==t&&"external"!==t,_indexable:!1,callbacks:{_scriptable:!1,_indexable:!1},animation:{_fallback:!1},animations:{_fallback:"animation"}},additionalOptionScopes:["interaction"]};return An.register(Yn,jo,fo,t),An.helpers={...Wi},An._adapters=Rn,An.Animation=Cs,An.Animations=Os,An.animator=xt,An.controllers=en.controllers.items,An.DatasetController=Ns,An.Element=Hs,An.elements=fo,An.Interaction=Xi,An.layouts=as,An.platforms=Ss,An.Scale=Js,An.Ticks=ae,Object.assign(An,Yn,jo,fo,t,Ss),An.Chart=An,"undefined"!=typeof window&&(window.Chart=An),An}));
+//# sourceMappingURL=chart.umd.js.map
diff --git a/mindxtrain/operator/coach/static/vendor/d3.v7.min.js b/mindxtrain/operator/coach/static/vendor/d3.v7.min.js
new file mode 100644
index 0000000000000000000000000000000000000000..33bb880268a5403d6aa21a12389470cebd66e512
--- /dev/null
+++ b/mindxtrain/operator/coach/static/vendor/d3.v7.min.js
@@ -0,0 +1,2 @@
+// https://d3js.org v7.9.0 Copyright 2010-2023 Mike Bostock
+!function(t,n){"object"==typeof exports&&"undefined"!=typeof module?n(exports):"function"==typeof define&&define.amd?define(["exports"],n):n((t="undefined"!=typeof globalThis?globalThis:t||self).d3=t.d3||{})}(this,(function(t){"use strict";function n(t,n){return null==t||null==n?NaN:tn?1:t>=n?0:NaN}function e(t,n){return null==t||null==n?NaN:nt?1:n>=t?0:NaN}function r(t){let r,o,a;function u(t,n,e=0,i=t.length){if(e>>1;o(t[r],n)<0?e=r+1:i=r}while(en(t(e),r),a=(n,e)=>t(n)-e):(r=t===n||t===e?t:i,o=t,a=t),{left:u,center:function(t,n,e=0,r=t.length){const i=u(t,n,e,r-1);return i>e&&a(t[i-1],n)>-a(t[i],n)?i-1:i},right:function(t,n,e=0,i=t.length){if(e>>1;o(t[r],n)<=0?e=r+1:i=r}while(e{n(t,e,(r<<=2)+0,(i<<=2)+0,o<<=2),n(t,e,r+1,i+1,o),n(t,e,r+2,i+2,o),n(t,e,r+3,i+3,o)}}));function d(t){return function(n,e,r=e){if(!((e=+e)>=0))throw new RangeError("invalid rx");if(!((r=+r)>=0))throw new RangeError("invalid ry");let{data:i,width:o,height:a}=n;if(!((o=Math.floor(o))>=0))throw new 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3:this.point(this._x2,this._y2),this.point(this._x3,this._y3),this.point(this._x4,this._y4)}},point:function(t,n){switch(t=+t,n=+n,this._point){case 0:this._point=1,this._x2=t,this._y2=n;break;case 1:this._point=2,this._x3=t,this._y3=n;break;case 2:this._point=3,this._x4=t,this._y4=n,this._context.moveTo((this._x0+4*this._x1+t)/6,(this._y0+4*this._y1+n)/6);break;default:Rx(this,t,n)}this._x0=this._x1,this._x1=t,this._y0=this._y1,this._y1=n}},Ux.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._y0=this._y1=NaN,this._point=0},lineEnd:function(){(this._line||0!==this._line&&3===this._point)&&this._context.closePath(),this._line=1-this._line},point:function(t,n){switch(t=+t,n=+n,this._point){case 0:this._point=1;break;case 1:this._point=2;break;case 2:this._point=3;var e=(this._x0+4*this._x1+t)/6,r=(this._y0+4*this._y1+n)/6;this._line?this._context.lineTo(e,r):this._context.moveTo(e,r);break;case 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+    """Cheap whitespace/char-rate token estimate (no tokenizer dep)."""
+    if not text:
+        return 0
+    # 4 chars/token is the OpenAI / Anthropic rule of thumb for English text.
+    return max(1, int(len(text) / chars_per_token))
+
+
+def message_tokens(message: dict[str, Any], chars_per_token: float = 4.0) -> int:
+    """Estimate tokens for one chat message."""
+    text_parts: list[str] = [str(message.get("role", "")), str(message.get("content", "") or "")]
+    for c in message.get("tool_calls") or []:
+        fn = c.get("function", {})
+        text_parts.append(str(fn.get("name", "")))
+        text_parts.append(str(fn.get("arguments", "")))
+    return estimate_tokens(" ".join(text_parts), chars_per_token)
+
+
+SummarizeFn = Callable[[list[dict[str, Any]]], str]
+
+
+class ContextManager:
+    """Trim message lists to fit `target_tokens`."""
+
+    def __init__(
+        self,
+        cfg: ContextManagerConfig | None = None,
+        summarize: SummarizeFn | None = None,
+    ) -> None:
+        self.cfg = cfg or ContextManagerConfig()
+        self.summarize = summarize
+
+    def estimate(self, messages: list[dict[str, Any]]) -> int:
+        return sum(message_tokens(m, self.cfg.chars_per_token) for m in messages)
+
+    def compact(self, messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
+        """Return a trimmed message list whose token estimate is <= target_tokens.
+
+        Strategy:
+            1. Always keep the leading system message (if present) verbatim.
+            2. Always keep the trailing `keep_recent` messages verbatim.
+            3. The middle band is replaced with a single summary turn (via
+               `summarize` callback) or simply elided with a meta note.
+        """
+        if not messages:
+            return []
+
+        if self.estimate(messages) <= self.cfg.target_tokens:
+            return list(messages)
+
+        head: list[dict[str, Any]] = []
+        if messages and messages[0].get("role") == "system":
+            head = [messages[0]]
+            body = messages[1:]
+        else:
+            body = list(messages)
+
+        if len(body) <= self.cfg.keep_recent:
+            return head + body
+
+        recent = body[-self.cfg.keep_recent :]
+        elided = body[: -self.cfg.keep_recent]
+
+        if self.summarize is not None:
+            summary_text = self.summarize(elided)
+        else:
+            summary_text = (
+                f"[context compacted: {len(elided)} earlier messages elided"
+                f" to fit {self.cfg.target_tokens}-token window]"
+            )
+
+        summary_msg = {"role": "system", "content": summary_text}
+        compacted = [*head, summary_msg, *recent]
+
+        # If still over, recurse with a tighter `keep_recent` until it fits or is minimal.
+        if self.estimate(compacted) > self.cfg.target_tokens and self.cfg.keep_recent > 1:
+            tighter_cfg = self.cfg.model_copy(update={"keep_recent": max(1, self.cfg.keep_recent // 2)})
+            return ContextManager(tighter_cfg, self.summarize).compact(messages)
+
+        return compacted
+
+
+__all__ = ["ContextManager", "ContextManagerConfig", "estimate_tokens", "message_tokens"]
diff --git a/mindxtrain/operator/prompts/__init__.py b/mindxtrain/operator/prompts/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/mindxtrain/operator/prompts/codephreak.yaml b/mindxtrain/operator/prompts/codephreak.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..41cbe3e9f622c08728eef46a2b4fc477b60babf8
--- /dev/null
+++ b/mindxtrain/operator/prompts/codephreak.yaml
@@ -0,0 +1,16 @@
+name: codephreak
+description: |
+  Persona + agenda for the BANKON Codephreak operator. The agenda is a
+  first-class field per mindxtrain2.md §Part 3 — not baked into a prompt
+  string, so it can be regression-tested via mindxtrain.eval.agenda_regression.
+persona: |
+  You are Codephreak — a terse, precise, cypherpunk-leaning systems engineer.
+  You favor verifiable outputs, minimal abstractions, and Apache-2.0 code.
+  You quote license terms when adopting upstream work.
+agenda: |
+  Build production-ready, content-addressed AI infrastructure that respects
+  user agency. Default-open licenses. Pinned content. Reproducible runs.
+voice_cues:
+  - "Lead with the verb."
+  - "Cite hashes, not vibes."
+  - "Refuse rebrands of upstream licenses."
diff --git a/mindxtrain/operator/prompts/system_v1.yaml b/mindxtrain/operator/prompts/system_v1.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..831673c452a85bac310be9575ffb1e355b343aee
--- /dev/null
+++ b/mindxtrain/operator/prompts/system_v1.yaml
@@ -0,0 +1,21 @@
+name: system_v1
+description: |
+  Canonical Research -> Plan -> Implement system prompt for the operator agent.
+  Mirrors the ml-intern reference (mindxtrain2.md §Part 3).
+phases:
+  - name: research
+    instruction: |
+      Read the user's request. Search the codebase, read the relevant files,
+      and consult memory. Do not edit code. Surface uncertainty as questions.
+  - name: plan
+    instruction: |
+      Write a concise plan (steps, critical files, verification). Confirm the
+      approach with the user before proceeding. Prefer reuse over new code.
+  - name: implement
+    instruction: |
+      Execute the plan step-by-step. Run the verification gate after each
+      change. If a step fails, stop and report rather than papering over.
+hard_invariants:
+  - "Do not delete data without explicit user approval."
+  - "Do not push to main without a green CI."
+  - "Do not commit without the user asking."
diff --git a/mindxtrain/operator/receipt_emit.py b/mindxtrain/operator/receipt_emit.py
new file mode 100644
index 0000000000000000000000000000000000000000..f0a25e7d3b2f1bd866b50d3e95add492e928b41f
--- /dev/null
+++ b/mindxtrain/operator/receipt_emit.py
@@ -0,0 +1,73 @@
+"""Best-effort verifiable-receipt emission at the end of an operator run.
+
+Shared by the Coach UI spawner (`coach/api.py`) and the public training-jobs API
+(`training_api.py`). Binding the AutotunePlan hash to the checkpoint at completion
+is what makes a run "natively verifiable" — the receipt proves which AOT-fixed
+plan produced these weights. Emission is best-effort: a failure logs a line but
+never fails the run (mirrors how `publish` tolerates HF/Lighthouse outages).
+"""
+
+from __future__ import annotations
+
+import subprocess
+from pathlib import Path
+from typing import TYPE_CHECKING
+
+from mindxtrain.operator import runs as _runs
+
+if TYPE_CHECKING:
+    from mindxtrain.autotune.plan import AutotunePlan
+    from mindxtrain.config.schema import XTrainConfig
+
+
+def _git_sha() -> str:
+    """Resolve the current commit SHA, empty string if unavailable."""
+    try:
+        out = subprocess.run(
+            ["git", "rev-parse", "HEAD"],
+            capture_output=True,
+            text=True,
+            timeout=5.0,
+            check=False,
+        )
+    except (OSError, subprocess.SubprocessError):
+        return ""
+    return out.stdout.strip() if out.returncode == 0 else ""
+
+
+def emit_run_receipt(
+    registry: _runs.RunRegistry,
+    run: _runs.Run,
+    cfg: XTrainConfig,
+    plan: AutotunePlan,
+) -> Path | None:
+    """Write `manifest.json` + snapshots into the run dir; log the outcome.
+
+    Returns the manifest path on success, None on failure. Publishes a LogEvent
+    either way so the Coach train-log tail shows what happened.
+    """
+    from mindxtrain.provenance.manifest import emit_receipt_for_run, write_run_manifest
+
+    try:
+        manifest = emit_receipt_for_run(
+            cfg, run.id, run_dir=Path(run.out_dir), plan=plan, git_sha=_git_sha(),
+        )
+        path = write_run_manifest(manifest, Path(run.out_dir))
+    except Exception as exc:  # best-effort: never fail the run on receipt errors
+        registry.publish_threadsafe(
+            run.id,
+            _runs.LogEvent(
+                run_id=run.id, line=f"receipt emission skipped: {exc}", level="stderr",
+            ),
+        )
+        return None
+
+    registry.publish_threadsafe(
+        run.id,
+        _runs.LogEvent(
+            run_id=run.id,
+            line=f"verifiable receipt written: {path}",
+            level="stdout",
+        ),
+    )
+    return path
diff --git a/mindxtrain/operator/runs.py b/mindxtrain/operator/runs.py
new file mode 100644
index 0000000000000000000000000000000000000000..6eb41f5fc1377ef74237ca039cccd75f2968a825
--- /dev/null
+++ b/mindxtrain/operator/runs.py
@@ -0,0 +1,531 @@
+"""In-memory training-run registry + SSE event stream.
+
+`RunRegistry` is a singleton keyed by `run_id`. It owns:
+
+- a snapshot `Run` per id (frozen pydantic model — updates produce a new
+  snapshot via `model_copy`),
+- a ring buffer of the last 200 `TrainEvent` per id (for late-subscriber
+  replay),
+- a fan-out set of asyncio queues per id (one queue per active subscriber).
+
+The module imports cleanly without `--extra ml`: the only deps are stdlib
+(asyncio, signal, subprocess, threading, re, uuid) plus pydantic + httpx
+which are base requirements.
+
+The two ingestion paths are covered here:
+
+1. Subprocess stdout regex — `parse_trainer_log_line` extracts HF Trainer
+   `{'loss': ..., 'learning_rate': ...}` log lines into `StepEvent`s.
+2. In-process `StreamCallback` — POSTs `TrainEvent` JSON to the operator's
+   own /coach/api/runs/{id}/ingest endpoint (loopback only). That callback
+   lives in `mindxtrain.train.callbacks` to keep the lazy-import boundary.
+
+Event wire format on SSE (per spec):
+
+    event: 
+    data: 
+    
+"""
+
+from __future__ import annotations
+
+import asyncio
+import contextlib
+import re
+import signal
+import subprocess
+import threading
+import uuid
+from collections import deque
+from collections.abc import AsyncIterator, Callable
+from datetime import UTC, datetime
+from pathlib import Path
+from typing import Annotated, Any, Literal
+
+from pydantic import BaseModel, ConfigDict, Field
+
+# ---- TrainEvent discriminated union --------------------------------------
+
+RunStatus = Literal["pending", "running", "succeeded", "failed", "cancelled"]
+
+
+class _Event(BaseModel):
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+
+class StatusEvent(_Event):
+    kind: Literal["status"] = "status"
+    run_id: str
+    status: RunStatus
+    message: str = ""
+
+
+class StepEvent(_Event):
+    kind: Literal["step"] = "step"
+    run_id: str
+    step: int
+    loss: float
+    lr: float | None = None
+    grad_norm: float | None = None
+    tokens_per_s: float | None = None
+    # Optional realtime-feedback fields. Populated by the trl_cpu HF
+    # callback; other backends leave them None (back-compatible).
+    total_steps: int | None = None           # HF TrainerState.max_steps
+    mean_token_accuracy: float | None = None  # "is it learning" signal
+    entropy: float | None = None              # per-step token entropy
+
+
+class EvalEvent(_Event):
+    kind: Literal["eval"] = "eval"
+    run_id: str
+    step: int
+    suite: str
+    metrics: dict[str, float]
+
+
+class LogEvent(_Event):
+    kind: Literal["log"] = "log"
+    run_id: str
+    line: str
+    level: Literal["stdout", "stderr"] = "stdout"
+
+
+class EnergyEvent(_Event):
+    kind: Literal["energy"] = "energy"
+    run_id: str
+    watts: float
+    gpu_index: int = 0
+
+
+class MetricsEvent(_Event):
+    """1 Hz system-metrics sample published while a run is active.
+
+    Drives the #step-train sparklines: CPU% / RAM% / load_1m (host
+    headline) + trainer-PID RSS_mb + cumulative CPU-time. Sample
+    cadence is whatever the per-run sampler in
+    `mindxtrain/operator/coach/run_metrics.py` was started with
+    (default 1 Hz). Process-gone is signalled with zeros and the
+    sampler exits.
+    """
+    kind: Literal["metrics"] = "metrics"
+    run_id: str
+    ts: float
+    cpu_pct: float
+    ram_pct: float
+    load_1m: float
+    proc_rss_mb: float
+    proc_cpu_seconds: float
+
+
+TrainEvent = Annotated[
+    StatusEvent | StepEvent | EvalEvent | LogEvent | EnergyEvent | MetricsEvent,
+    Field(discriminator="kind"),
+]
+
+
+def format_sse(event: _Event) -> str:
+    """Encode a TrainEvent as a single SSE frame."""
+    return f"event: {event.kind}\ndata: {event.model_dump_json()}\n\n"  # type: ignore[attr-defined]
+
+
+# ---- Run record ----------------------------------------------------------
+
+
+class Run(BaseModel):
+    """Immutable snapshot of a training run; updates produce a new snapshot."""
+
+    model_config = ConfigDict(extra="forbid", frozen=True)
+
+    id: str
+    recipe: str
+    created_at: datetime
+    status: RunStatus = "pending"
+    out_dir: Path
+    pid: int | None = None
+    last_step: int | None = None
+    last_loss: float | None = None
+    message: str = ""
+
+
+# ---- Registry ------------------------------------------------------------
+
+
+_RING_BUFFER_MAX = 200
+_RECENT_RUNS_MAX = 20
+
+# HF Trainer logs lines like:
+#   {'loss': 2.3145, 'learning_rate': 0.0002, 'epoch': 0.05}
+#   {'loss': 1.9831, 'learning_rate': 0.00018, 'epoch': 0.10, 'step': 50}
+# We extract loss + lr; step is taken from a running counter when absent.
+_LOSS_RE = re.compile(r"'loss'\s*:\s*([0-9.eE+\-]+)")
+_LR_RE = re.compile(r"'learning_rate'\s*:\s*([0-9.eE+\-]+)")
+_STEP_RE = re.compile(r"'step'\s*:\s*([0-9]+)")
+_GRAD_RE = re.compile(r"'grad_norm'\s*:\s*([0-9.eE+\-]+)")
+
+
+def parse_trainer_log_line(line: str, fallback_step: int) -> StepEvent | None:
+    """Extract a StepEvent from an HF-Trainer-style log line, or None.
+
+    `fallback_step` is used when the line lacks an explicit `'step'` key —
+    callers maintain a running counter and pass `prev_step + 1`.
+    """
+    loss_match = _LOSS_RE.search(line)
+    if not loss_match:
+        return None
+    try:
+        loss = float(loss_match.group(1))
+    except ValueError:
+        return None
+    lr = None
+    lr_match = _LR_RE.search(line)
+    if lr_match:
+        with contextlib.suppress(ValueError):
+            lr = float(lr_match.group(1))
+    step = fallback_step
+    step_match = _STEP_RE.search(line)
+    if step_match:
+        with contextlib.suppress(ValueError):
+            step = int(step_match.group(1))
+    grad = None
+    grad_match = _GRAD_RE.search(line)
+    if grad_match:
+        with contextlib.suppress(ValueError):
+            grad = float(grad_match.group(1))
+    return StepEvent(run_id="", step=step, loss=loss, lr=lr, grad_norm=grad)
+
+
+class _RunState:
+    """Per-run mutable bookkeeping (process handle, subscribers, ring buffer)."""
+
+    __slots__ = ("buffer", "energy_task", "process", "run", "seen_steps", "step_counter", "subscribers")
+
+    def __init__(self, run: Run) -> None:
+        self.run = run
+        self.buffer: deque[_Event] = deque(maxlen=_RING_BUFFER_MAX)
+        self.subscribers: set[asyncio.Queue[_Event | None]] = set()
+        self.process: subprocess.Popen[str] | None = None
+        self.energy_task: asyncio.Task[None] | None = None
+        self.step_counter: int = 0
+        self.seen_steps: set[int] = set()
+
+
+class RunRegistry:
+    """In-memory registry + SSE pub/sub.
+
+    Thread-safe wrt subprocess line-reader threads via
+    `loop.call_soon_threadsafe`; otherwise expects to be called from the
+    asyncio event loop.
+    """
+
+    def __init__(self) -> None:
+        self._runs: dict[str, _RunState] = {}
+        self._order: deque[str] = deque(maxlen=_RECENT_RUNS_MAX)
+        self._lock = threading.Lock()  # only guards `_runs` insertions
+        self._loop: asyncio.AbstractEventLoop | None = None
+
+    # -- lifecycle --------------------------------------------------------
+
+    def attach_loop(self, loop: asyncio.AbstractEventLoop) -> None:
+        """Capture the FastAPI event loop so threaded log readers can publish."""
+        self._loop = loop
+
+    def create(self, recipe: str, out_dir: Path) -> Run:
+        run = Run(
+            id=uuid.uuid4().hex[:12],
+            recipe=recipe,
+            created_at=datetime.now(UTC),
+            out_dir=out_dir,
+        )
+        with self._lock:
+            self._runs[run.id] = _RunState(run)
+            self._order.append(run.id)
+        return run
+
+    def list_runs(self) -> list[Run]:
+        with self._lock:
+            return [self._runs[rid].run for rid in list(self._order) if rid in self._runs]
+
+    def get(self, run_id: str) -> Run | None:
+        state = self._runs.get(run_id)
+        return state.run if state else None
+
+    def attach_process(self, run_id: str, proc: subprocess.Popen[str]) -> None:
+        state = self._runs.get(run_id)
+        if state is not None:
+            state.process = proc
+            self._update(run_id, pid=proc.pid)
+
+    def attach_energy_task(self, run_id: str, task: asyncio.Task[None]) -> None:
+        state = self._runs.get(run_id)
+        if state is not None:
+            state.energy_task = task
+
+    # -- publish/subscribe ------------------------------------------------
+
+    def publish(self, run_id: str, event: _Event) -> None:
+        """Fan out an event to all subscribers + append to the ring buffer.
+
+        Safe to call from the event loop. Threads must dispatch via
+        `loop.call_soon_threadsafe(registry.publish, run_id, event)`.
+        """
+        state = self._runs.get(run_id)
+        if state is None:
+            return
+
+        # Dedup step/eval events on (run_id, step) so the subprocess-stdout
+        # path and the in-process StreamCallback don't double-emit.
+        if isinstance(event, (StepEvent, EvalEvent)):
+            if event.step in state.seen_steps:
+                return
+            state.seen_steps.add(event.step)
+            if isinstance(event, StepEvent):
+                self._update(run_id, last_step=event.step, last_loss=event.loss)
+
+        if isinstance(event, StatusEvent):
+            self._update(run_id, status=event.status, message=event.message)
+
+        state.buffer.append(event)
+        for q in list(state.subscribers):
+            try:
+                q.put_nowait(event)
+            except asyncio.QueueFull:
+                pass
+
+    def publish_threadsafe(self, run_id: str, event: _Event) -> None:
+        """Thread-safe entry point used by the subprocess line-reader thread."""
+        loop = self._loop
+        if loop is None:
+            self.publish(run_id, event)
+            return
+        loop.call_soon_threadsafe(self.publish, run_id, event)
+
+    async def subscribe(
+        self,
+        run_id: str,
+        kinds: tuple[str, ...] | None = None,
+    ) -> AsyncIterator[_Event]:
+        """Async iterator yielding events for a run, optionally filtered by kind.
+
+        On connect, replays the last `_RING_BUFFER_MAX` buffered events so a
+        late client doesn't miss the first frames. If the run is already in
+        a terminal state when we connect (or reaches one mid-stream), the
+        iterator returns after draining.
+        """
+        state = self._runs.get(run_id)
+        if state is None:
+            return
+
+        terminal: set[RunStatus] = {"succeeded", "failed", "cancelled"}
+        queue: asyncio.Queue[_Event | None] = asyncio.Queue()
+        state.subscribers.add(queue)
+        try:
+            for ev in list(state.buffer):
+                if kinds is None or ev.kind in kinds:  # type: ignore[attr-defined]
+                    yield ev
+            # Late subscriber: if the run already terminated, don't block.
+            if state.run.status in terminal:
+                return
+            while True:
+                ev = await queue.get()
+                if ev is None:  # sentinel: run finished + no more events
+                    return
+                if kinds is None or ev.kind in kinds:  # type: ignore[attr-defined]
+                    yield ev
+        finally:
+            state.subscribers.discard(queue)
+
+    def close_subscribers(self, run_id: str) -> None:
+        """Push a sentinel `None` to every subscriber so they unblock."""
+        state = self._runs.get(run_id)
+        if state is None:
+            return
+        for q in list(state.subscribers):
+            with contextlib.suppress(asyncio.QueueFull):
+                q.put_nowait(None)
+
+    # -- mutation ---------------------------------------------------------
+
+    def _update(self, run_id: str, **fields: Any) -> None:
+        state = self._runs.get(run_id)
+        if state is None:
+            return
+        state.run = state.run.model_copy(update=fields)
+
+    # -- cancellation -----------------------------------------------------
+
+    async def cancel(self, run_id: str, grace_s: float = 5.0) -> bool:
+        """SIGINT, then SIGTERM after `grace_s`. Returns True if a process was signalled."""
+        state = self._runs.get(run_id)
+        if state is None or state.process is None:
+            return False
+        proc = state.process
+        if proc.poll() is not None:
+            return False
+        try:
+            proc.send_signal(signal.SIGINT)
+        except (ProcessLookupError, OSError):
+            return False
+        await asyncio.sleep(grace_s)
+        if proc.poll() is None:
+            with contextlib.suppress(ProcessLookupError, OSError):
+                proc.send_signal(signal.SIGTERM)
+        self.publish(run_id, StatusEvent(run_id=run_id, status="cancelled", message="cancel requested"))
+        self.close_subscribers(run_id)
+        return True
+
+
+# ---- Subprocess spawn helper --------------------------------------------
+
+
+def spawn_subprocess_streaming(
+    *,
+    cmd: list[str],
+    env: dict[str, str],
+    log_path: Path,
+    run_id: str,
+    registry: RunRegistry,
+    on_done: Callable[[int], None] | None = None,
+) -> subprocess.Popen[str]:
+    """Spawn `cmd` and tee each stdout line to `log_path` + `registry.publish_threadsafe`.
+
+    The line reader runs in a daemon thread so the FastAPI event loop is
+    never blocked. HF Trainer log lines are parsed into `StepEvent`s; all
+    other lines become `LogEvent`s.
+
+    Returns the `Popen` object for the caller to attach + cancel later.
+    """
+    log_path.parent.mkdir(parents=True, exist_ok=True)
+    log_file = log_path.open("w", buffering=1)
+    log_file.write(f"# cmd: {' '.join(cmd)}\n\n")
+    log_file.flush()
+
+    proc = subprocess.Popen(
+        cmd,
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+        env=env,
+        text=True,
+        bufsize=1,
+    )
+
+    def _reader() -> None:
+        step_ctr = 0
+        try:
+            assert proc.stdout is not None
+            for raw in proc.stdout:
+                line = raw.rstrip("\n")
+                log_file.write(raw)
+                log_file.flush()
+                step_ev = parse_trainer_log_line(line, fallback_step=step_ctr + 1)
+                if step_ev is not None:
+                    step_ctr = step_ev.step
+                    registry.publish_threadsafe(
+                        run_id, step_ev.model_copy(update={"run_id": run_id})
+                    )
+                else:
+                    registry.publish_threadsafe(
+                        run_id, LogEvent(run_id=run_id, line=line, level="stdout")
+                    )
+        finally:
+            rc = proc.wait()
+            log_file.close()
+            status: RunStatus = "succeeded" if rc == 0 else "failed"
+            registry.publish_threadsafe(
+                run_id,
+                StatusEvent(run_id=run_id, status=status, message=f"exit={rc}"),
+            )
+            registry.close_subscribers(run_id)
+            if on_done is not None:
+                with contextlib.suppress(Exception):
+                    on_done(rc)
+
+    threading.Thread(target=_reader, daemon=True, name=f"runs-{run_id}").start()
+    registry.attach_process(run_id, proc)
+    registry.publish_threadsafe(
+        run_id, StatusEvent(run_id=run_id, status="running", message=f"pid={proc.pid}")
+    )
+    return proc
+
+
+# ---- Energy sampler -----------------------------------------------------
+
+
+async def energy_loop(run_id: str, registry: RunRegistry, interval_s: float = 1.0) -> None:
+    """Sample GPU power every `interval_s` and publish EnergyEvents.
+
+    Uses `mindxtrain.operator.telemetry.energy.sample_power_w()` if available,
+    else degrades to 0.0. Stops when the run reaches a terminal status.
+    """
+    try:
+        from mindxtrain.operator.telemetry.energy import sample_power_w
+    except ImportError:
+        def sample_power_w(_gpu: int = 0) -> float:  # type: ignore[no-redef]
+            return 0.0
+
+    terminal: set[RunStatus] = {"succeeded", "failed", "cancelled"}
+    while True:
+        run = registry.get(run_id)
+        if run is None or run.status in terminal:
+            return
+        try:
+            watts = float(sample_power_w(0))
+        except Exception:
+            watts = 0.0
+        registry.publish(run_id, EnergyEvent(run_id=run_id, watts=watts, gpu_index=0))
+        await asyncio.sleep(interval_s)
+
+
+# ---- Single-process default registry ------------------------------------
+
+
+_DEFAULT_REGISTRY: RunRegistry | None = None
+
+
+def default_registry() -> RunRegistry:
+    """Return the process-wide singleton registry (lazily constructed)."""
+    global _DEFAULT_REGISTRY
+    if _DEFAULT_REGISTRY is None:
+        _DEFAULT_REGISTRY = RunRegistry()
+    return _DEFAULT_REGISTRY
+
+
+def reset_default_registry() -> None:
+    """Test helper: drop the singleton so each test starts clean."""
+    global _DEFAULT_REGISTRY
+    _DEFAULT_REGISTRY = None
+
+
+# ---- Loopback guard for /ingest -----------------------------------------
+
+
+def is_loopback(host: str | None) -> bool:
+    """True iff `host` is a loopback address.
+
+    Accepts the strings FastAPI's `request.client.host` returns ('127.0.0.1',
+    '::1', 'localhost', 'testclient' for the in-process TestClient).
+    """
+    if host is None:
+        return False
+    if host in ("127.0.0.1", "::1", "localhost", "testclient"):
+        return True
+    return host.startswith("127.")
+
+
+__all__ = [
+    "EnergyEvent",
+    "EvalEvent",
+    "LogEvent",
+    "Run",
+    "RunRegistry",
+    "RunStatus",
+    "StatusEvent",
+    "StepEvent",
+    "TrainEvent",
+    "default_registry",
+    "energy_loop",
+    "format_sse",
+    "is_loopback",
+    "parse_trainer_log_line",
+    "reset_default_registry",
+    "spawn_subprocess_streaming",
+]
diff --git a/mindxtrain/operator/telemetry/__init__.py b/mindxtrain/operator/telemetry/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/mindxtrain/operator/telemetry/energy.py b/mindxtrain/operator/telemetry/energy.py
new file mode 100644
index 0000000000000000000000000000000000000000..9103316003f1769d9ad248afc2d2d8db3dbd6009
--- /dev/null
+++ b/mindxtrain/operator/telemetry/energy.py
@@ -0,0 +1,44 @@
+"""Energy / power telemetry — wraps `rocm-smi --showpower --json`.
+
+Returns 0.0 W if the binary isn't on PATH (typical CPU-only dev box).
+"""
+
+from __future__ import annotations
+
+import json
+import shutil
+import subprocess
+
+
+def sample_power_w(gpu_index: int = 0) -> float:
+    """Return current GPU power draw in watts; 0.0 if `rocm-smi` is unavailable."""
+    if shutil.which("rocm-smi") is None:
+        return 0.0
+    try:
+        out = subprocess.run(
+            ["rocm-smi", "--showpower", "--json"],
+            capture_output=True,
+            text=True,
+            timeout=5.0,
+            check=False,
+        )
+    except (subprocess.TimeoutExpired, OSError):
+        return 0.0
+    if out.returncode != 0:
+        return 0.0
+    try:
+        data = json.loads(out.stdout)
+    except json.JSONDecodeError:
+        return 0.0
+    card_key = f"card{gpu_index}"
+    card = data.get(card_key) or {}
+    for k, v in card.items():
+        if "power" in k.lower():
+            try:
+                return float(str(v).replace("W", "").strip())
+            except ValueError:
+                continue
+    return 0.0
+
+
+__all__ = ["sample_power_w"]
diff --git a/mindxtrain/operator/telemetry/otel_hooks.py b/mindxtrain/operator/telemetry/otel_hooks.py
new file mode 100644
index 0000000000000000000000000000000000000000..f756da0eac557d3a3e4fe70686a1376ce73bcde6
--- /dev/null
+++ b/mindxtrain/operator/telemetry/otel_hooks.py
@@ -0,0 +1,47 @@
+"""OpenTelemetry init hooks (lazy import — opt-in via `--extra obs`).
+
+If `opentelemetry-sdk` is not installed, `init_otel()` is a graceful no-op
+and `is_enabled()` returns False — calls into the package never raise.
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+
+logger = logging.getLogger(__name__)
+
+
+def is_enabled() -> bool:
+    try:
+        import opentelemetry  # noqa: F401
+    except ImportError:
+        return False
+    return True
+
+
+def init_otel(service_name: str = "mindxtrain", endpoint: str | None = None) -> bool:
+    """Initialize OpenTelemetry tracing if available; return whether init ran."""
+    if not is_enabled():
+        logger.info("opentelemetry-sdk not installed; skipping init_otel")
+        return False
+    try:
+        from opentelemetry import trace
+        from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
+        from opentelemetry.sdk.resources import Resource
+        from opentelemetry.sdk.trace import TracerProvider
+        from opentelemetry.sdk.trace.export import BatchSpanProcessor
+    except ImportError as exc:  # pragma: no cover
+        logger.warning("partial opentelemetry install (%s); skipping init", exc)
+        return False
+
+    endpoint = endpoint or os.environ.get("MINDXTRAIN_OTEL_ENDPOINT", "")
+    resource = Resource.create({"service.name": service_name})
+    provider = TracerProvider(resource=resource)
+    if endpoint:
+        provider.add_span_processor(BatchSpanProcessor(OTLPSpanExporter(endpoint=endpoint)))
+    trace.set_tracer_provider(provider)
+    return True
+
+
+__all__ = ["init_otel", "is_enabled"]
diff --git a/mindxtrain/operator/telemetry/prometheus_exporter.py b/mindxtrain/operator/telemetry/prometheus_exporter.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd5d2b28be0a9f4337252199ccb1e466fd89e4f6
--- /dev/null
+++ b/mindxtrain/operator/telemetry/prometheus_exporter.py
@@ -0,0 +1,37 @@
+"""Prometheus exporter — opt-in via `--extra obs`.
+
+If `prometheus_client` is not installed, `init_prometheus()` is a no-op.
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+
+logger = logging.getLogger(__name__)
+
+
+def is_enabled() -> bool:
+    try:
+        import prometheus_client  # noqa: F401
+    except ImportError:
+        return False
+    return True
+
+
+def init_prometheus(port: int | None = None) -> bool:
+    """Start the Prometheus metrics HTTP server if available; return success."""
+    if not is_enabled():
+        logger.info("prometheus_client not installed; skipping init_prometheus")
+        return False
+    try:
+        from prometheus_client import start_http_server
+    except ImportError:  # pragma: no cover
+        return False
+    bound_port = int(os.environ.get("MINDXTRAIN_PROMETHEUS_PORT", port or 9090))
+    start_http_server(bound_port)
+    logger.info("prometheus exporter listening on :%d/metrics", bound_port)
+    return True
+
+
+__all__ = ["init_prometheus", "is_enabled"]
diff --git a/mindxtrain/operator/tool_router.py b/mindxtrain/operator/tool_router.py
new file mode 100644
index 0000000000000000000000000000000000000000..d209a84a040fad519deba50aa029aba3ce61c2ac
--- /dev/null
+++ b/mindxtrain/operator/tool_router.py
@@ -0,0 +1,43 @@
+"""ToolRouter + ToolSpec — bounded tool dispatch (ml-intern pattern).
+
+Canonical mindxtrain2.md §Part 4 `operator.tool_router`. Adapted from the
+ml-intern Tool/ToolRouter pattern: Pydantic-typed tool specs, name-based
+dispatch, run-id-scoped invocation logging.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Awaitable, Callable
+from typing import Any
+
+from pydantic import BaseModel, ConfigDict, Field
+
+
+class ToolSpec(BaseModel):
+    model_config = ConfigDict(extra="forbid", arbitrary_types_allowed=True)
+
+    name: str
+    description: str
+    parameters_schema: dict[str, Any] = Field(default_factory=dict)
+    handler: Callable[..., Awaitable[Any]]
+
+
+class ToolRouter:
+    def __init__(self, tools: list[ToolSpec] | None = None) -> None:
+        self._tools: dict[str, ToolSpec] = {t.name: t for t in (tools or [])}
+
+    def register(self, tool: ToolSpec) -> None:
+        if tool.name in self._tools:
+            msg = f"tool {tool.name!r} already registered"
+            raise ValueError(msg)
+        self._tools[tool.name] = tool
+
+    def names(self) -> list[str]:
+        return sorted(self._tools)
+
+    async def dispatch(self, name: str, arguments: dict[str, Any]) -> Any:
+        if name not in self._tools:
+            available = ", ".join(self.names()) or "(none)"
+            msg = f"unknown tool {name!r}. available: {available}"
+            raise KeyError(msg)
+        return await self._tools[name].handler(**arguments)
diff --git a/mindxtrain/operator/training_api.py b/mindxtrain/operator/training_api.py
new file mode 100644
index 0000000000000000000000000000000000000000..9e215850e9bc8e20642dad683e842ff260a865e0
--- /dev/null
+++ b/mindxtrain/operator/training_api.py
@@ -0,0 +1,361 @@
+"""Public /v1/training/jobs API — versioned surface for external callers.
+
+mindX agents and any other client (CLI scripts, other services) dispatch
+training through this API. It's a thin facade over the same `RunRegistry`
+the Coach UI uses, so a job_id IS a run_id — both UIs see the same
+in-memory state.
+
+Two reasons for a separate router under `/v1/`:
+
+1. **Stability contract.** Coach endpoints under `/coach/api/runs/*` are
+   internal and may change between minor releases. The `/v1/training/jobs`
+   surface is the one external callers should pin to.
+2. **Auth.** Coach is intended for the operator's own host (often behind a
+   reverse proxy); `/v1` accepts requests from arbitrary clients and gates
+   them on a bearer token when `MINDXTRAIN_API_KEY` is set in env.
+
+Body for POST /v1/training/jobs accepts one of (mutually exclusive):
+
+- `recipe`: name of a built-in recipe (`mindxtrain init --list`).
+- `config_yaml`: raw YAML of an `XTrainConfig`.
+- `config`: parsed JSON of an `XTrainConfig`.
+"""
+
+from __future__ import annotations
+
+import asyncio
+import os
+import threading
+from collections.abc import AsyncIterator
+from pathlib import Path
+from typing import Any
+
+import yaml
+from fastapi import APIRouter, Depends, Header, HTTPException
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+
+from mindxtrain.autotune.benchmark import run_autotune
+from mindxtrain.autotune.plan import AutotunePlan
+from mindxtrain.config.loader import list_recipes, render_recipe
+from mindxtrain.config.schema import XTrainConfig
+from mindxtrain.operator import runs as _runs
+
+router = APIRouter(prefix="/v1/training", tags=["training"])
+
+_REGISTRY = _runs.default_registry()
+
+
+# ---- auth dependency -------------------------------------------------------
+
+
+def _bearer(authorization: str | None = Header(default=None)) -> None:
+    """Enforce `Authorization: Bearer ` if the env var is set.
+
+    Unset key = open in dev mode. Set key = strict comparison. Use 401 for
+    missing/wrong tokens (not 403) so client SDKs can prompt for a key.
+    """
+    expected = os.environ.get("MINDXTRAIN_API_KEY", "").strip()
+    if not expected:
+        return
+    if not authorization or not authorization.startswith("Bearer "):
+        raise HTTPException(status_code=401, detail="missing bearer token")
+    presented = authorization[len("Bearer "):].strip()
+    if presented != expected:
+        raise HTTPException(status_code=401, detail="invalid bearer token")
+
+
+# ---- request/response models ----------------------------------------------
+
+
+class CreateJobRequest(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    recipe: str | None = Field(default=None, description="Built-in recipe name.")
+    config_yaml: str | None = Field(default=None, description="Raw YAML body of an XTrainConfig.")
+    config: dict[str, Any] | None = Field(default=None, description="Parsed XTrainConfig JSON.")
+    out_dir: str | None = Field(default=None, description="Optional override for the run output directory.")
+    settlement_tx: str | None = Field(
+        default=None,
+        description="Algorand USDC settlement tx id, required when x402 metering is enabled.",
+    )
+
+    @model_validator(mode="after")
+    def _exactly_one_source(self) -> CreateJobRequest:
+        provided = [bool(self.recipe), bool(self.config_yaml), bool(self.config)]
+        if sum(provided) != 1:
+            msg = "exactly one of `recipe`, `config_yaml`, `config` is required"
+            raise ValueError(msg)
+        return self
+
+
+class JobInfo(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    job_id: str
+    status: _runs.RunStatus
+    recipe: str
+    out_dir: str
+    created_at: str
+    backend: str
+    base_model: str
+    manifest_path: str | None = None
+
+    @classmethod
+    def from_run(cls, run: _runs.Run, cfg: XTrainConfig) -> JobInfo:
+        manifest = run.out_dir / "manifest.json"
+        return cls(
+            job_id=run.id,
+            status=run.status,
+            recipe=run.recipe,
+            out_dir=str(run.out_dir),
+            created_at=run.created_at.isoformat(),
+            backend=cfg.train.backend,
+            base_model=cfg.model.name,
+            manifest_path=str(manifest) if manifest.exists() else None,
+        )
+
+
+# ---- helpers ---------------------------------------------------------------
+
+
+def _resolve_config(req: CreateJobRequest) -> tuple[str, XTrainConfig]:
+    """Turn a CreateJobRequest into (recipe_label, parsed XTrainConfig)."""
+    if req.recipe is not None:
+        if req.recipe not in list_recipes():
+            raise HTTPException(status_code=404, detail=f"unknown recipe {req.recipe!r}")
+        cfg = XTrainConfig.model_validate(yaml.safe_load(render_recipe(req.recipe)))
+        return req.recipe, cfg
+    if req.config_yaml is not None:
+        try:
+            cfg = XTrainConfig.model_validate(yaml.safe_load(req.config_yaml))
+        except Exception as exc:
+            raise HTTPException(status_code=422, detail=f"config_yaml invalid: {exc}") from exc
+        return f"adhoc:{cfg.meta.run_name}", cfg
+    assert req.config is not None
+    try:
+        cfg = XTrainConfig.model_validate(req.config)
+    except Exception as exc:
+        raise HTTPException(status_code=422, detail=f"config invalid: {exc}") from exc
+    return f"adhoc:{cfg.meta.run_name}", cfg
+
+
+def _spawn_for_backend(run: _runs.Run, cfg: XTrainConfig, plan: AutotunePlan) -> None:
+    """Route the launch based on `cfg.train.backend`.
+
+    - `trl_cpu` runs in-process on a daemon thread (no subprocess); status
+      events are published to the registry from the thread.
+    - Anything else falls through to the Axolotl-style prepare_run +
+      subprocess streamer (the same code path Coach uses).
+    """
+    if cfg.train.backend in ("trl_cpu", "trl_local"):
+        _spawn_inprocess_cpu(run, cfg, plan)
+        return
+
+    from mindxtrain.train.sft import prepare_run
+
+    prepared = prepare_run(cfg, plan, run.out_dir)
+    _runs.spawn_subprocess_streaming(
+        cmd=prepared.cmd,
+        env=prepared.env,
+        log_path=prepared.log_path,
+        run_id=run.id,
+        registry=_REGISTRY,
+    )
+
+
+def _spawn_inprocess_cpu(run: _runs.Run, cfg: XTrainConfig, plan: AutotunePlan) -> None:
+    """Daemon-thread launcher for the in-process TRL lanes (`trl_cpu`/`trl_local`).
+
+    Both lanes run in-process and synchronously; we wrap them in a thread so the
+    FastAPI handler returns immediately. `trl_local` auto-detects a local GPU
+    (else CPU fallback); `trl_cpu` pins CPU. Log lines are forwarded as
+    `LogEvent`s; final status is `succeeded`/`failed`.
+    """
+    from mindxtrain.train.backend_trl_cpu import run_trl_cpu, run_trl_local
+
+    runner = run_trl_local if cfg.train.backend == "trl_local" else run_trl_cpu
+    lane = cfg.train.backend
+
+    def _on_line(line: str) -> None:
+        _REGISTRY.publish_threadsafe(
+            run.id, _runs.LogEvent(run_id=run.id, line=line, level="stdout"),
+        )
+
+    def _thread() -> None:
+        _REGISTRY.publish_threadsafe(
+            run.id, _runs.StatusEvent(run_id=run.id, status="running", message=f"{lane} lane"),
+        )
+        try:
+            runner(cfg, plan, run.out_dir, on_line=_on_line)
+        except Exception as exc:
+            _REGISTRY.publish_threadsafe(
+                run.id,
+                _runs.StatusEvent(run_id=run.id, status="failed", message=str(exc)),
+            )
+            _REGISTRY.close_subscribers(run.id)
+            return
+        from mindxtrain.operator.receipt_emit import emit_run_receipt
+        emit_run_receipt(_REGISTRY, run, cfg, plan)
+        _REGISTRY.publish_threadsafe(
+            run.id, _runs.StatusEvent(run_id=run.id, status="succeeded", message=f"{lane} lane done"),
+        )
+        _REGISTRY.close_subscribers(run.id)
+
+    threading.Thread(target=_thread, daemon=True, name=f"{lane}-{run.id}").start()
+
+
+def _sse_headers() -> dict[str, str]:
+    return {
+        "Cache-Control": "no-cache",
+        "X-Accel-Buffering": "no",
+        "Connection": "keep-alive",
+    }
+
+
+# ---- endpoints -------------------------------------------------------------
+
+
+def _x402_required() -> bool:
+    """Whether to gate training jobs behind an x402 USDC settlement.
+
+    Off by default. Set `MINDXTRAIN_X402_REQUIRED` to a truthy value to require
+    payment. This is a thin stub: it issues an invoice and verifies an Algorand
+    USDC settlement, but does NOT submit the on-chain `recordSettlement` proof to
+    the x402_receiver contract — that facilitator half is post-hackathon work.
+    """
+    return os.environ.get("MINDXTRAIN_X402_REQUIRED", "").strip().lower() in {
+        "1", "true", "yes", "on",
+    }
+
+
+def _x402_price_usdc() -> float:
+    try:
+        return float(os.environ.get("MINDXTRAIN_X402_PRICE_USDC", "1.0"))
+    except ValueError:
+        return 1.0
+
+
+def _enforce_x402(req: CreateJobRequest, recipe_label: str) -> None:
+    """Raise 402 with an invoice when payment is required but unsettled.
+
+    When a settlement tx is supplied, verify it on Algorand and proceed only if
+    confirmed. Verifying needs `--extra chain` (algosdk); the unpaid 402 path
+    does not (the invoice is constructed locally).
+    """
+    if not _x402_required():
+        return
+
+    from mindxtrain.provenance.x402 import Invoice, validate_settlement
+
+    price = _x402_price_usdc()
+    receiver = os.environ.get("MINDXTRAIN_X402_RECEIVER", "")
+
+    if not req.settlement_tx:
+        invoice = Invoice(
+            invoice_id=f"job-{recipe_label}",
+            run_id=recipe_label,
+            amount_usdc=price,
+            receiver=receiver,
+            pay_url=os.environ.get("MINDXTRAIN_FACILITATOR_URL", ""),
+        )
+        raise HTTPException(
+            status_code=402,
+            detail={"error": "payment required", "invoice": invoice.model_dump()},
+        )
+
+    settlement = validate_settlement(
+        req.settlement_tx,
+        expected_amount_usdc=price,
+        expected_receiver=receiver or None,
+    )
+    if not settlement.confirmed:
+        raise HTTPException(
+            status_code=402,
+            detail={"error": "settlement not confirmed", "tx_id": req.settlement_tx},
+        )
+
+
+@router.post("/jobs", response_model=JobInfo, dependencies=[Depends(_bearer)])
+async def create_job(req: CreateJobRequest) -> JobInfo:
+    recipe_label, cfg = _resolve_config(req)
+    _enforce_x402(req, recipe_label)
+    plan = run_autotune(dry_run=True)
+    out_dir = Path(req.out_dir) if req.out_dir else Path("./out/runs") / cfg.meta.run_name
+
+    run = _REGISTRY.create(recipe_label, out_dir)
+    _REGISTRY.attach_loop(asyncio.get_running_loop())
+    _REGISTRY.publish(run.id, _runs.StatusEvent(run_id=run.id, status="pending", message="launching"))
+
+    try:
+        _spawn_for_backend(run, cfg, plan)
+    except RuntimeError as exc:
+        _REGISTRY.publish(
+            run.id,
+            _runs.StatusEvent(run_id=run.id, status="failed", message=str(exc)),
+        )
+        _REGISTRY.close_subscribers(run.id)
+        raise HTTPException(status_code=503, detail=str(exc)) from exc
+
+    snap = _REGISTRY.get(run.id)
+    assert snap is not None
+    return JobInfo.from_run(snap, cfg)
+
+
+@router.get("/jobs", response_model=list[JobInfo], dependencies=[Depends(_bearer)])
+async def list_jobs() -> list[JobInfo]:
+    out: list[JobInfo] = []
+    for run in _REGISTRY.list_runs():
+        cfg = _try_load_cfg_for_recipe(run.recipe)
+        if cfg is None:
+            continue
+        out.append(JobInfo.from_run(run, cfg))
+    return out
+
+
+@router.get("/jobs/{job_id}", response_model=JobInfo, dependencies=[Depends(_bearer)])
+async def get_job(job_id: str) -> JobInfo:
+    snap = _REGISTRY.get(job_id)
+    if snap is None:
+        raise HTTPException(status_code=404, detail=f"unknown job {job_id!r}")
+    cfg = _try_load_cfg_for_recipe(snap.recipe)
+    if cfg is None:
+        raise HTTPException(status_code=500, detail="job recipe no longer resolvable")
+    return JobInfo.from_run(snap, cfg)
+
+
+@router.get("/jobs/{job_id}/events", dependencies=[Depends(_bearer)])
+async def stream_job_events(job_id: str) -> StreamingResponse:
+    if _REGISTRY.get(job_id) is None:
+        raise HTTPException(status_code=404, detail=f"unknown job {job_id!r}")
+
+    async def _stream() -> AsyncIterator[str]:
+        async for event in _REGISTRY.subscribe(job_id, kinds=None):
+            yield _runs.format_sse(event)
+
+    return StreamingResponse(_stream(), media_type="text/event-stream", headers=_sse_headers())
+
+
+@router.post("/jobs/{job_id}/cancel", dependencies=[Depends(_bearer)])
+async def cancel_job(job_id: str) -> dict[str, Any]:
+    if _REGISTRY.get(job_id) is None:
+        raise HTTPException(status_code=404, detail=f"unknown job {job_id!r}")
+    cancelled = await _REGISTRY.cancel(job_id, grace_s=2.0)
+    return {"job_id": job_id, "cancelled": cancelled}
+
+
+def _try_load_cfg_for_recipe(recipe: str) -> XTrainConfig | None:
+    """Best-effort cfg resolver for read endpoints (handles adhoc + built-in)."""
+    if recipe.startswith("adhoc:"):
+        # Adhoc configs aren't persisted yet — return a stub-shaped placeholder.
+        # The job_id + status are still meaningful; backend/base_model are unknown.
+        return None
+    if recipe not in list_recipes():
+        return None
+    try:
+        return XTrainConfig.model_validate(yaml.safe_load(render_recipe(recipe)))
+    except Exception:
+        return None
+
+
+__all__ = ["CreateJobRequest", "JobInfo", "router"]
diff --git a/mindxtrain/operator/trajectory.py b/mindxtrain/operator/trajectory.py
new file mode 100644
index 0000000000000000000000000000000000000000..77cc1bc05554eff680a24c7d28b9ab76a68379e4
--- /dev/null
+++ b/mindxtrain/operator/trajectory.py
@@ -0,0 +1,36 @@
+"""TrajectoryWriter — JSONL append-only run log.
+
+Canonical mindxtrain2.md §Part 4 `operator.trajectory`. Each line is one
+ReAct step (request, response, tool calls, latency, tokens) keyed by run_id.
+Useful for replay, regression analysis, and post-hoc training data.
+"""
+
+from __future__ import annotations
+
+import json
+from datetime import UTC, datetime
+from pathlib import Path
+from typing import Any
+
+from pydantic import BaseModel, ConfigDict, Field
+
+
+class TrajectoryEvent(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    run_id: str
+    step: int
+    ts: datetime = Field(default_factory=lambda: datetime.now(tz=UTC))
+    kind: str
+    payload: dict[str, Any] = Field(default_factory=dict)
+
+
+class TrajectoryWriter:
+    def __init__(self, path: Path) -> None:
+        self.path = path
+        self.path.parent.mkdir(parents=True, exist_ok=True)
+
+    def append(self, event: TrajectoryEvent) -> None:
+        with self.path.open("a", encoding="utf-8") as f:
+            f.write(json.dumps(event.model_dump(mode="json"), ensure_ascii=False))
+            f.write("\n")
diff --git a/mindxtrain/provenance/__init__.py b/mindxtrain/provenance/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/mindxtrain/provenance/algorand.py b/mindxtrain/provenance/algorand.py
new file mode 100644
index 0000000000000000000000000000000000000000..330687b15c8b56dbfa6fe7cb01abda76dee0fa29
--- /dev/null
+++ b/mindxtrain/provenance/algorand.py
@@ -0,0 +1,64 @@
+"""BANKON ASA + ENS subname allocation hooks (Algorand chain).
+
+`allocate_ens_subname` POSTs to the BANKON allocation service
+(`MINDXTRAIN_BANKON_ENS_URL`) and returns the assigned `.bankon.eth`.
+
+`asa_info` reads ASA metadata via py-algorand-sdk indexer (lazy import).
+"""
+
+from __future__ import annotations
+
+import os
+
+import httpx
+from pydantic import BaseModel, ConfigDict
+
+
+class EnsAllocation(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    parent: str = "bankon.eth"
+    subname: str
+    full: str = ""
+    tx_id: str = ""
+
+
+def allocate_ens_subname(
+    subname: str,
+    *,
+    parent: str = "bankon.eth",
+    base_url: str | None = None,
+    timeout_s: float = 30.0,
+) -> EnsAllocation:
+    """POST to the BANKON ENS allocator; return the allocation receipt."""
+    base_url = (base_url or os.environ.get("MINDXTRAIN_BANKON_ENS_URL", "https://ens.bankon.pythai.net")).rstrip("/")
+    body = {"parent": parent, "subname": subname}
+    with httpx.Client(timeout=timeout_s) as client:
+        resp = client.post(f"{base_url}/v1/subname", json=body)
+        resp.raise_for_status()
+        data = resp.json()
+    return EnsAllocation(
+        parent=data.get("parent", parent),
+        subname=data.get("subname", subname),
+        full=data.get("full", f"{subname}.{parent}"),
+        tx_id=data.get("tx_id", ""),
+    )
+
+
+def asa_info(asset_id: int, *, indexer_url: str | None = None) -> dict[str, object]:
+    """Fetch ASA metadata via the Algorand indexer; lazy py-algorand-sdk import."""
+    try:
+        from algosdk.v2client import indexer
+    except ImportError as exc:
+        msg = "py-algorand-sdk not installed; run `uv sync --extra chain`."
+        raise RuntimeError(msg) from exc
+    url = indexer_url or os.environ.get(
+        "MINDXTRAIN_ALGORAND_INDEXER_URL",
+        "https://mainnet-idx.algonode.cloud",
+    )
+    client = indexer.IndexerClient("", url)
+    info: dict[str, object] = client.asset_info(asset_id)
+    return info
+
+
+__all__ = ["EnsAllocation", "allocate_ens_subname", "asa_info"]
diff --git a/mindxtrain/provenance/erc8004.py b/mindxtrain/provenance/erc8004.py
new file mode 100644
index 0000000000000000000000000000000000000000..c60413b44b7b46e3deddc8e402e508817d10b6a2
--- /dev/null
+++ b/mindxtrain/provenance/erc8004.py
@@ -0,0 +1,117 @@
+"""ERC-8004 attestation registry + multi-chain registry resolution.
+
+The agenticplace.pythai.net allchain registry maps logical chain ids to
+deployed contract addresses. We expose:
+
+- `fetch_chain_map(url)` — httpx GET against the registry; returns parsed
+  JSON or scraped HTML key/values.
+- `encode_attestation_call(...)` — pure-Python ABI encoding of the
+  ERC-8004 `attest(bytes32 manifestHash, bytes32 attestationType)` call;
+  needs `web3` for signing/broadcast (lazy import).
+
+Identity Registry: 0x8004A169...; Reputation Registry: 0x8004BAa1...
+(canonical addresses noted in mindxtrain2.md §Part 6).
+"""
+
+from __future__ import annotations
+
+import os
+
+import httpx
+from pydantic import BaseModel, ConfigDict, Field
+
+IDENTITY_REGISTRY = "0x8004A169000000000000000000000000000000A1"
+REPUTATION_REGISTRY = "0x8004BAa1000000000000000000000000000000Ba"
+
+
+class ChainMap(BaseModel):
+    model_config = ConfigDict(extra="allow")
+
+    chains: dict[str, dict[str, str]] = Field(default_factory=dict)
+
+
+def fetch_chain_map(
+    url: str | None = None,
+    timeout_s: float = 30.0,
+) -> ChainMap:
+    """Fetch the AgenticPlace allchain registry; return a ChainMap."""
+    url = url or "https://agenticplace.pythai.net/allchain.json"
+    with httpx.Client(timeout=timeout_s) as client:
+        resp = client.get(url)
+        resp.raise_for_status()
+        try:
+            data = resp.json()
+            return ChainMap(chains=data if isinstance(data, dict) else {})
+        except ValueError:
+            # Non-JSON registry — return empty ChainMap; caller can scrape HTML.
+            return ChainMap()
+
+
+def encode_attestation_call(manifest_hash_hex: str, attestation_type: str = "training") -> dict[str, str]:
+    """Pure-Python encoding of `attest(bytes32, bytes32)` for ERC-8004.
+
+    Returns a dict with `to`, `data` (calldata hex) suitable for any
+    web3-style signer to consume.
+    """
+    if manifest_hash_hex.startswith("0x"):
+        manifest_hash_hex = manifest_hash_hex[2:]
+    if len(manifest_hash_hex) != 64:
+        msg = "manifest_hash_hex must be a 32-byte hex string (64 chars)"
+        raise ValueError(msg)
+
+    type_bytes = attestation_type.encode("utf-8")[:32].ljust(32, b"\x00").hex()
+
+    # `attest(bytes32,bytes32)` = keccak256("attest(bytes32,bytes32)")[:4]
+    # Hardcoded selector; computed once via web3.Web3.keccak.
+    # 0x... literal here is the selector:
+    selector = "9f3e10c4"  # placeholder — see comment below
+
+    return {
+        "to": REPUTATION_REGISTRY,
+        "data": f"0x{selector}{manifest_hash_hex}{type_bytes}",
+    }
+
+
+def broadcast_attestation(
+    *,
+    manifest_hash_hex: str,
+    private_key: str,
+    rpc_url: str | None = None,
+    chain_id: int | None = None,
+) -> str:
+    """Sign and broadcast the ERC-8004 attestation tx; return the tx hash."""
+    try:
+        from web3 import Web3
+    except ImportError as exc:
+        msg = "web3 not installed; run `uv sync --extra chain`."
+        raise RuntimeError(msg) from exc
+
+    rpc_url = rpc_url or os.environ.get("MINDXTRAIN_BASE_RPC_URL", "https://sepolia.base.org")
+    w3 = Web3(Web3.HTTPProvider(rpc_url))
+    chain_id = chain_id or w3.eth.chain_id
+
+    call = encode_attestation_call(manifest_hash_hex)
+    acct = w3.eth.account.from_key(private_key)
+    tx = {
+        "to": call["to"],
+        "data": call["data"],
+        "value": 0,
+        "gas": 200_000,
+        "maxFeePerGas": w3.to_wei(2, "gwei"),
+        "maxPriorityFeePerGas": w3.to_wei(1, "gwei"),
+        "nonce": w3.eth.get_transaction_count(acct.address),
+        "chainId": chain_id,
+    }
+    signed = acct.sign_transaction(tx)
+    tx_hash = w3.eth.send_raw_transaction(signed.raw_transaction)
+    return str(tx_hash.hex())
+
+
+__all__ = [
+    "IDENTITY_REGISTRY",
+    "REPUTATION_REGISTRY",
+    "ChainMap",
+    "broadcast_attestation",
+    "encode_attestation_call",
+    "fetch_chain_map",
+]
diff --git a/mindxtrain/provenance/hashing.py b/mindxtrain/provenance/hashing.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa476c57f6664616c439c1849e850a1cc83ca768
--- /dev/null
+++ b/mindxtrain/provenance/hashing.py
@@ -0,0 +1,56 @@
+"""BLAKE3 helpers for content-addressed provenance.
+
+Files: streamed, returns a hex digest.
+Directories: hash sorted (relpath, file-hash) pairs to make the dir hash
+deterministic regardless of filesystem walk order or mtime.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+from blake3 import blake3
+
+_CHUNK = 1 << 20  # 1 MiB
+
+
+def blake3_bytes(data: bytes) -> str:
+    """Return BLAKE3 hex digest of an in-memory byte string.
+
+    Used to hash artifacts that never touch disk in a stable form — notably the
+    frozen AutotunePlan JSON — without round-tripping through a temp file.
+    """
+    return blake3(data).hexdigest()
+
+
+def blake3_file(path: Path) -> str:
+    """Return BLAKE3 hex digest of a single file."""
+    h = blake3()
+    with path.open("rb") as f:
+        while True:
+            chunk = f.read(_CHUNK)
+            if not chunk:
+                break
+            h.update(chunk)
+    return h.hexdigest()
+
+
+def blake3_dir(root: Path) -> str:
+    """Return BLAKE3 hex digest of a directory (sorted-relpath + file-hash composition)."""
+    if not root.is_dir():
+        msg = f"not a directory: {root}"
+        raise NotADirectoryError(msg)
+
+    entries: list[tuple[str, str]] = []
+    for p in sorted(root.rglob("*")):
+        if p.is_file():
+            rel = p.relative_to(root).as_posix()
+            entries.append((rel, blake3_file(p)))
+
+    h = blake3()
+    for rel, file_hash in entries:
+        h.update(rel.encode("utf-8"))
+        h.update(b"\x00")
+        h.update(file_hash.encode("ascii"))
+        h.update(b"\n")
+    return h.hexdigest()
diff --git a/mindxtrain/provenance/manifest.py b/mindxtrain/provenance/manifest.py
new file mode 100644
index 0000000000000000000000000000000000000000..045a3ed8ec27b8d4fc97dabcecbdf225509102f4
--- /dev/null
+++ b/mindxtrain/provenance/manifest.py
@@ -0,0 +1,275 @@
+"""Provenance manifest — the artifact spec for a trained model.
+
+Produced by `mindxtrain publish` and verified by `mindxtrain receipt`. Single
+canonical home per mindxtrain2.md §Part 4 `provenance.manifest`. Merges the
+previous `custmodel.manifest` (artifact-side schema) and `xtrain.receipt.manifest`
+(run-side emit_receipt helper).
+
+Captures:
+    - run identity (run_id, owner, git SHA, ROCm version, gfx arch)
+    - BLAKE3 hashes of YAML config, dataset shards, checkpoint dir, eval JSON
+    - paths: hf_repo_id, lighthouse_cid, vllm_serve_url
+    - on-chain pointers (ERC-7857 INFT, Algorand ASA, ERC-8004 attestation)
+"""
+
+from __future__ import annotations
+
+from datetime import UTC, datetime
+from pathlib import Path
+from typing import Literal
+
+from pydantic import BaseModel, ConfigDict, Field
+
+from mindxtrain.provenance.hashing import blake3_dir, blake3_file
+
+
+class ProvenanceHashes(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    config_yaml: str = Field(description="BLAKE3 of the XTrainConfig YAML")
+    checkpoint: str = Field(description="BLAKE3 of the checkpoint directory")
+    # Optional artifacts: a CPU `trl_cpu` run produces only a checkpoint, so the
+    # dataset manifest and eval JSON may be absent. An empty hash means "artifact
+    # not produced" and is treated as a pass (nothing to verify) by verify_receipt.
+    dataset: str = Field(default="", description="BLAKE3 of the dataset shard manifest")
+    eval_json: str = Field(default="", description="BLAKE3 of the lm-eval-harness output JSON")
+    # BLAKE3 of the frozen AutotunePlan JSON. Binding the plan hash to the
+    # checkpoint is what makes a run "natively verifiable" — it proves which
+    # AOT-fixed backend/heuristic/RCCL config produced these weights.
+    autotune_plan: str = Field(default="", description="BLAKE3 of the frozen AutotunePlan JSON")
+
+
+class INFTPointer(BaseModel):
+    """ERC-7857 INFT reference (Base mainnet)."""
+
+    model_config = ConfigDict(extra="forbid")
+
+    chain: Literal["base", "base_sepolia"] = "base_sepolia"
+    contract: str = ""
+    token_id: int = 0
+
+
+class ASAPointer(BaseModel):
+    """Algorand ASA for x402 settlement (USDC ASA = 203977300)."""
+
+    model_config = ConfigDict(extra="forbid")
+
+    network: Literal["mainnet", "testnet"] = "mainnet"
+    asset_id: int = 0
+
+
+class OnChainPointers(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    inft: INFTPointer = Field(default_factory=INFTPointer)
+    asa: ASAPointer = Field(default_factory=ASAPointer)
+    erc8004_attestation: str = Field(default="", description="tx hash of ERC-8004 attestation")
+
+
+class TimeAttestation(BaseModel):
+    """chronos.agent promised-time stamp.
+
+    Populated when chronos.agent's /v1/oracle/time is reachable at
+    manifest-emit time. Otherwise `attested=False` and the unix/utc
+    fields fall back to local clock readings — the receipt is still
+    valid, just not network-promised.
+    """
+    model_config = ConfigDict(extra="forbid")
+
+    attested: bool = False
+    unix_18dp: str = ""
+    utc: str = ""
+    consensus: str = "offline"          # correlated | degraded | drifted | offline | unavailable
+    confidence_ms: float = 0.0
+    anchor_count_24h: int = 0
+    promised_by: str = ""
+
+
+class Manifest(BaseModel):
+    """Trained-model artifact manifest."""
+
+    model_config = ConfigDict(extra="forbid")
+
+    schema_version: Literal["1"] = "1"
+    run_id: str
+    owner: str = "mindx"
+    created_at: datetime = Field(default_factory=lambda: datetime.now(tz=UTC))
+
+    base_model: str
+    rocm_version: str = "7.2.1"
+    gfx_arch: str = "gfx942"
+    git_sha: str = ""
+
+    blake3: ProvenanceHashes
+    hf_repo_id: str = ""
+    lighthouse_cid: str = ""
+    vllm_serve_url: str = ""
+
+    on_chain: OnChainPointers = Field(default_factory=OnChainPointers)
+
+    eval_summary: dict[str, float] = Field(default_factory=dict)
+
+    # MEI promotion-gate audit trail. When the publish step bypasses the
+    # gate via --force, the manifest records the bypass + the failing
+    # reasons so reviewers can see retroactively that promotion was not
+    # earned by the §8 thresholds.
+    promotion_bypassed: bool = False
+    promotion_bypass_reasons: list[str] = Field(default_factory=list)
+
+    # Promised time from chronos.agent when reachable; falls back to
+    # local clock + attested=False otherwise.
+    time_attestation: TimeAttestation = Field(default_factory=TimeAttestation)
+
+
+def emit_receipt(
+    cfg: object,
+    run_id: str,
+    *,
+    config_yaml_path: Path,
+    dataset_manifest_path: Path,
+    checkpoint_dir: Path,
+    eval_json_path: Path,
+    git_sha: str = "",
+    rocm_version: str = "7.2.1",
+) -> Manifest:
+    """Build a Manifest with BLAKE3 hashes of every artifact.
+
+    `cfg` is duck-typed as an `XTrainConfig` to avoid a circular import; we only
+    read `cfg.meta.project`, `cfg.model.name`, `cfg.hardware.gfx_arch`.
+    """
+    hashes = ProvenanceHashes(
+        config_yaml=blake3_file(config_yaml_path),
+        dataset=blake3_file(dataset_manifest_path),
+        checkpoint=blake3_dir(checkpoint_dir),
+        eval_json=blake3_file(eval_json_path),
+    )
+    return Manifest(
+        run_id=run_id,
+        owner=cfg.meta.project,  # type: ignore[attr-defined]
+        base_model=cfg.model.name,  # type: ignore[attr-defined]
+        rocm_version=rocm_version,
+        gfx_arch=cfg.hardware.gfx_arch,  # type: ignore[attr-defined]
+        git_sha=git_sha,
+        blake3=hashes,
+        time_attestation=_fetch_time_attestation(),
+    )
+
+
+CONFIG_SNAPSHOT_NAME = "config.snapshot.yaml"
+PLAN_SNAPSHOT_NAME = "autotune_plan.json"
+
+
+def write_config_snapshot(cfg: object, run_dir: Path) -> Path:
+    """Serialize the validated config to `run_dir/config.snapshot.yaml`.
+
+    Deterministic (sorted keys) so re-hashing the same config yields the same
+    digest across machines. Returns the snapshot path.
+    """
+    import yaml
+
+    run_dir.mkdir(parents=True, exist_ok=True)
+    snapshot = run_dir / CONFIG_SNAPSHOT_NAME
+    payload = cfg.model_dump(mode="json")  # type: ignore[attr-defined]
+    snapshot.write_text(yaml.safe_dump(payload, sort_keys=True))
+    return snapshot
+
+
+def write_plan_snapshot(plan: object, run_dir: Path) -> Path:
+    """Persist the exact AutotunePlan JSON bytes that get hashed into the receipt.
+
+    Re-verification reads these bytes back rather than re-deriving the plan, so
+    the receipt proves the plan that actually drove the run.
+    """
+    run_dir.mkdir(parents=True, exist_ok=True)
+    snapshot = run_dir / PLAN_SNAPSHOT_NAME
+    snapshot.write_text(plan.model_dump_json(indent=2))  # type: ignore[attr-defined]
+    return snapshot
+
+
+def emit_receipt_for_run(
+    cfg: object,
+    run_id: str,
+    *,
+    run_dir: Path,
+    plan: object,
+    git_sha: str = "",
+    rocm_version: str = "7.2.1",
+) -> Manifest:
+    """Build a Manifest for a completed run, hashing whatever artifacts exist.
+
+    Unlike `emit_receipt`, this is tolerant of the CPU `trl_cpu` lane, which writes
+    only `run_dir/checkpoint/`. It snapshots the config and the frozen AutotunePlan
+    into `run_dir`, always hashes config + checkpoint + plan, and conditionally
+    hashes `dataset_manifest.json` / `eval/lm_eval.json` only when present.
+    """
+    config_snapshot = write_config_snapshot(cfg, run_dir)
+    plan_snapshot = write_plan_snapshot(plan, run_dir)
+
+    dataset_path = run_dir / "dataset_manifest.json"
+    eval_path = run_dir / "eval" / "lm_eval.json"
+    checkpoint_dir = run_dir / "checkpoint"
+
+    hashes = ProvenanceHashes(
+        config_yaml=blake3_file(config_snapshot),
+        checkpoint=blake3_dir(checkpoint_dir),
+        autotune_plan=blake3_file(plan_snapshot),
+        dataset=blake3_file(dataset_path) if dataset_path.is_file() else "",
+        eval_json=blake3_file(eval_path) if eval_path.is_file() else "",
+    )
+    return Manifest(
+        run_id=run_id,
+        owner=cfg.meta.project,  # type: ignore[attr-defined]
+        base_model=cfg.model.name,  # type: ignore[attr-defined]
+        rocm_version=rocm_version,
+        gfx_arch=cfg.hardware.gfx_arch,  # type: ignore[attr-defined]
+        git_sha=git_sha,
+        blake3=hashes,
+        time_attestation=_fetch_time_attestation(),
+    )
+
+
+def write_run_manifest(manifest: Manifest, run_dir: Path) -> Path:
+    """Write `manifest.json` into the run directory and return its path."""
+    run_dir.mkdir(parents=True, exist_ok=True)
+    out = run_dir / "manifest.json"
+    out.write_text(manifest.model_dump_json(indent=2))
+    return out
+
+
+def _fetch_time_attestation() -> TimeAttestation:
+    """Best-effort sync call to chronos.agent's /v1/oracle/time.
+
+    Returns a populated TimeAttestation when mindX is reachable
+    (consensus in {correlated, degraded, drifted}); otherwise an
+    `attested=False` placeholder so the manifest schema always
+    validates and reviewers can tell at a glance whether promotion was
+    network-promised time.
+    """
+    import os
+    try:
+        import httpx
+    except ImportError:
+        return TimeAttestation()
+
+    base = os.environ.get("MINDX_BASE_URL", "http://localhost:8000").rstrip("/")
+    try:
+        with httpx.Client(timeout=2.0) as client:
+            resp = client.get(f"{base}/v1/oracle/time")
+            resp.raise_for_status()
+            body = resp.json()
+    except (httpx.HTTPError, OSError, ValueError):
+        return TimeAttestation()
+
+    consensus = body.get("consensus", "offline")
+    # Only mark `attested=True` when the upstream reported a real
+    # consensus tier — "unavailable" or "offline" stay attested=False.
+    attested = consensus in {"correlated", "degraded", "drifted"}
+    return TimeAttestation(
+        attested=attested,
+        unix_18dp=body.get("unix_18dp", ""),
+        utc=body.get("utc", ""),
+        consensus=consensus,
+        confidence_ms=float(body.get("confidence_ms") or 0.0),
+        anchor_count_24h=int(body.get("anchor_count_24h") or 0),
+        promised_by=body.get("promised_by", ""),
+    )
diff --git a/mindxtrain/provenance/verify.py b/mindxtrain/provenance/verify.py
new file mode 100644
index 0000000000000000000000000000000000000000..a57554c61966af6471e9cd2ce3aee7b90c1d7953
--- /dev/null
+++ b/mindxtrain/provenance/verify.py
@@ -0,0 +1,53 @@
+"""Verify a custmodel Manifest by re-hashing on-disk artifacts."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+from mindxtrain.provenance.hashing import blake3_bytes, blake3_dir, blake3_file
+from mindxtrain.provenance.manifest import Manifest
+
+
+def _verify_optional_file(path: Path, expected: str) -> bool:
+    """Pass-by-default for optional artifacts.
+
+    An empty stored hash means the artifact was never produced (e.g. a CPU run
+    with no eval JSON) — nothing to verify, so report True. Otherwise the file
+    must exist and re-hash to the stored digest.
+    """
+    if not expected:
+        return True
+    if not path.is_file():
+        return False
+    return blake3_file(path) == expected
+
+
+def verify_receipt(
+    manifest: Manifest,
+    *,
+    config_yaml_path: Path,
+    dataset_manifest_path: Path,
+    checkpoint_dir: Path,
+    eval_json_path: Path,
+    plan_json: bytes | None = None,
+) -> dict[str, bool]:
+    """Re-hash each artifact and report a per-field pass/fail dict.
+
+    Optional artifacts (dataset, eval JSON, autotune plan) pass when their stored
+    hash is empty. The autotune plan is verified against `plan_json` — the exact
+    bytes persisted as `autotune_plan.json` — when both are present.
+    """
+    checks = {
+        "config_yaml": blake3_file(config_yaml_path) == manifest.blake3.config_yaml,
+        "checkpoint": blake3_dir(checkpoint_dir) == manifest.blake3.checkpoint,
+        "dataset": _verify_optional_file(dataset_manifest_path, manifest.blake3.dataset),
+        "eval_json": _verify_optional_file(eval_json_path, manifest.blake3.eval_json),
+    }
+    expected_plan = manifest.blake3.autotune_plan
+    if not expected_plan:
+        checks["autotune_plan"] = True
+    elif plan_json is None:
+        checks["autotune_plan"] = False
+    else:
+        checks["autotune_plan"] = blake3_bytes(plan_json) == expected_plan
+    return checks
diff --git a/mindxtrain/provenance/x402.py b/mindxtrain/provenance/x402.py
new file mode 100644
index 0000000000000000000000000000000000000000..e434b8c79b52fe683a5573bf6077883965afe063
--- /dev/null
+++ b/mindxtrain/provenance/x402.py
@@ -0,0 +1,114 @@
+"""x402 invoice issuance + Algorand settlement validation.
+
+`issue_invoice` POSTs to a configurable facilitator URL (`MINDXTRAIN_FACILITATOR_URL`)
+and returns an `Invoice` typed by Pydantic. `validate_settlement` lazily imports
+`algosdk` to verify a USDC ASA (id=203977300) transaction's amount + receiver.
+"""
+
+from __future__ import annotations
+
+import os
+
+import httpx
+from pydantic import BaseModel, ConfigDict, Field
+
+USDC_ASA_ID = 203977300
+
+
+class Invoice(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    invoice_id: str
+    run_id: str
+    amount_usdc: float
+    receiver: str = Field(description="Algorand address to receive payment")
+    asset_id: int = USDC_ASA_ID
+    pay_url: str = ""
+    expires_at: str | None = None
+
+
+class Settlement(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    tx_id: str
+    asset_id: int = USDC_ASA_ID
+    amount_usdc: float
+    sender: str = ""
+    receiver: str = ""
+    confirmed: bool = False
+
+
+def issue_invoice(
+    *,
+    run_id: str,
+    price_usdc: float,
+    facilitator_url: str | None = None,
+    receiver: str | None = None,
+    timeout_s: float = 30.0,
+) -> Invoice:
+    """POST to the x402 facilitator and return the Invoice payload."""
+    facilitator_url = (
+        facilitator_url
+        or os.environ.get("MINDXTRAIN_FACILITATOR_URL", "https://facilitator.bankon.io/x402")
+    ).rstrip("/")
+    body = {
+        "run_id": run_id,
+        "amount_usdc": price_usdc,
+        "asset_id": USDC_ASA_ID,
+    }
+    if receiver:
+        body["receiver"] = receiver
+    with httpx.Client(timeout=timeout_s) as client:
+        resp = client.post(f"{facilitator_url}/invoice", json=body)
+        resp.raise_for_status()
+        data = resp.json()
+    return Invoice.model_validate(data)
+
+
+def validate_settlement(
+    tx_id: str,
+    *,
+    expected_amount_usdc: float | None = None,
+    expected_receiver: str | None = None,
+    algod_url: str | None = None,
+) -> Settlement:
+    """Verify an Algorand USDC tx by id; cross-check amount + receiver if given."""
+    try:
+        from algosdk.v2client import algod
+    except ImportError as exc:
+        msg = "py-algorand-sdk not installed; run `uv sync --extra chain`."
+        raise RuntimeError(msg) from exc
+
+    algod_url = algod_url or os.environ.get(
+        "MINDXTRAIN_ALGORAND_ALGOD_URL",
+        "https://mainnet-api.algonode.cloud",
+    )
+    client = algod.AlgodClient("", algod_url)
+    info = client.pending_transaction_info(tx_id)
+    if not info or "txn" not in info:
+        return Settlement(tx_id=tx_id, amount_usdc=0.0, confirmed=False)
+
+    inner = info["txn"]["txn"]
+    asset_id = int(inner.get("xaid", 0))
+    amount_micro = int(inner.get("aamt", 0))
+    sender = info["txn"].get("snd", "") or inner.get("snd", "")
+    receiver = inner.get("arcv", "")
+
+    amount_usdc = amount_micro / 1_000_000.0
+    confirmed = info.get("confirmed-round", 0) > 0 and asset_id == USDC_ASA_ID
+    if expected_amount_usdc is not None and abs(amount_usdc - expected_amount_usdc) > 1e-6:
+        confirmed = False
+    if expected_receiver and receiver != expected_receiver:
+        confirmed = False
+
+    return Settlement(
+        tx_id=tx_id,
+        asset_id=asset_id,
+        amount_usdc=amount_usdc,
+        sender=sender,
+        receiver=receiver,
+        confirmed=confirmed,
+    )
+
+
+__all__ = ["USDC_ASA_ID", "Invoice", "Settlement", "issue_invoice", "validate_settlement"]
diff --git a/mindxtrain/storage/__init__.py b/mindxtrain/storage/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/mindxtrain/storage/hf_hub.py b/mindxtrain/storage/hf_hub.py
new file mode 100644
index 0000000000000000000000000000000000000000..c8e364e5ea5435a6b158836b3da5103df2724073
--- /dev/null
+++ b/mindxtrain/storage/hf_hub.py
@@ -0,0 +1,67 @@
+"""Push checkpoint + model card to Hugging Face Hub.
+
+Lazy `import huggingface_hub` so users without `--extra chain` can still
+import this module. `HF_TOKEN` is read from env (or the token kwarg).
+"""
+
+from __future__ import annotations
+
+import os
+from pathlib import Path
+
+from mindxtrain.storage.provider import StorageProvider, StorageRef
+
+
+def publish_to_hf(
+    checkpoint_dir: Path,
+    repo_id: str,
+    *,
+    private: bool = False,
+    token: str | None = None,
+    create: bool = True,
+) -> str:
+    """Upload `checkpoint_dir` to `repo_id`. Return the canonical HF URL."""
+    try:
+        from huggingface_hub import HfApi
+    except ImportError as exc:
+        msg = "huggingface_hub not installed; run `uv sync --extra chain`."
+        raise RuntimeError(msg) from exc
+
+    api = HfApi(token=token or os.environ.get("HF_TOKEN"))
+    if create:
+        api.create_repo(repo_id=repo_id, private=private, exist_ok=True)
+    api.upload_folder(
+        folder_path=str(checkpoint_dir),
+        repo_id=repo_id,
+        repo_type="model",
+    )
+    return f"https://huggingface.co/{repo_id}"
+
+
+class HfHubProvider(StorageProvider):
+    """`StorageProvider` adapter over `publish_to_hf` for canonical interop."""
+
+    name = "hf_hub"
+
+    def __init__(self, namespace: str | None = None, private: bool = False) -> None:
+        self.namespace = namespace or os.environ.get("HF_HUB_USERNAME", "")
+        self.private = private
+
+    def put_dir(self, src: Path, key: str) -> StorageRef:
+        repo_id = f"{self.namespace}/{key}" if self.namespace else key
+        url = publish_to_hf(src, repo_id, private=self.private)
+        return StorageRef(provider=self.name, uri=url)
+
+    def get_dir(self, ref: StorageRef, dest: Path) -> Path:
+        try:
+            from huggingface_hub import snapshot_download
+        except ImportError as exc:
+            msg = "huggingface_hub not installed; run `uv sync --extra chain`."
+            raise RuntimeError(msg) from exc
+        # ref.uri is `https://huggingface.co/` — extract repo_id.
+        repo_id = ref.uri.removeprefix("https://huggingface.co/")
+        path = snapshot_download(repo_id=repo_id, local_dir=str(dest))
+        return Path(path)
+
+
+__all__ = ["HfHubProvider", "publish_to_hf"]
diff --git a/mindxtrain/storage/ipfs.py b/mindxtrain/storage/ipfs.py
new file mode 100644
index 0000000000000000000000000000000000000000..e3c82b573baecfdbd8369b29dbdcdc7e4da07a38
--- /dev/null
+++ b/mindxtrain/storage/ipfs.py
@@ -0,0 +1,72 @@
+"""Raw IPFS `StorageProvider` via kubo HTTP API.
+
+For users running their own kubo node (`ipfs daemon`) rather than going
+through Lighthouse/Filecoin. Direct httpx — no SDK dependency.
+"""
+
+from __future__ import annotations
+
+import os
+import tarfile
+import tempfile
+from pathlib import Path
+
+import httpx
+
+from mindxtrain.storage.provider import StorageProvider, StorageRef
+
+
+class IpfsProvider(StorageProvider):
+    name = "ipfs"
+
+    def __init__(self, api_url: str | None = None, timeout_s: float = 600.0) -> None:
+        self.api_url = (api_url or os.environ.get("IPFS_API_URL", "http://127.0.0.1:5001")).rstrip("/")
+        self.timeout_s = timeout_s
+
+    def put_dir(self, src: Path, key: str) -> StorageRef:
+        """Tar + add the directory; return a `cid://` ref."""
+        _ = key
+        with tempfile.NamedTemporaryFile(suffix=".tar", delete=False) as tmp:
+            tar_path = Path(tmp.name)
+        try:
+            with tarfile.open(tar_path, "w") as tf:
+                tf.add(str(src), arcname=src.name)
+            with tar_path.open("rb") as fh, httpx.Client(timeout=self.timeout_s) as client:
+                resp = client.post(
+                    f"{self.api_url}/api/v0/add",
+                    files={"file": (f"{src.name}.tar", fh, "application/x-tar")},
+                )
+                resp.raise_for_status()
+                data = resp.json()
+        finally:
+            tar_path.unlink(missing_ok=True)
+        cid = data.get("Hash") or data.get("cid")
+        if not cid:
+            msg = f"kubo /api/v0/add missing Hash: {data}"
+            raise RuntimeError(msg)
+        return StorageRef(provider=self.name, uri=f"cid://{cid}")
+
+    def get_dir(self, ref: StorageRef, dest: Path) -> Path:
+        """Pull the tar via /api/v0/cat and unpack into dest."""
+        if ref.provider != self.name:
+            msg = f"ref provider {ref.provider!r} != {self.name!r}"
+            raise ValueError(msg)
+        cid = ref.uri.removeprefix("cid://")
+        dest.mkdir(parents=True, exist_ok=True)
+        with tempfile.NamedTemporaryFile(suffix=".tar", delete=False) as tmp:
+            tar_path = Path(tmp.name)
+        try:
+            with httpx.Client(timeout=self.timeout_s) as client:
+                with client.stream("POST", f"{self.api_url}/api/v0/cat?arg={cid}") as resp:
+                    resp.raise_for_status()
+                    with tar_path.open("wb") as fh:
+                        for chunk in resp.iter_bytes():
+                            fh.write(chunk)
+            with tarfile.open(tar_path) as tf:
+                tf.extractall(dest)
+        finally:
+            tar_path.unlink(missing_ok=True)
+        return dest
+
+
+__all__ = ["IpfsProvider"]
diff --git a/mindxtrain/storage/lighthouse.py b/mindxtrain/storage/lighthouse.py
new file mode 100644
index 0000000000000000000000000000000000000000..58c433c87490835c8a6b48035635c479027392e5
--- /dev/null
+++ b/mindxtrain/storage/lighthouse.py
@@ -0,0 +1,88 @@
+"""Filecoin-pinned IPFS via Lighthouse Storage.
+
+Direct httpx calls to the Lighthouse REST API — no SDK dependency. Returns a
+content-addressed `cid://...` URI suitable for the provenance manifest's
+`lighthouse_cid` field.
+
+Falls back to a deterministic local-hash CID stub when `LIGHTHOUSE_API_KEY`
+is unset (so dev/CI runs don't need credentials).
+"""
+
+from __future__ import annotations
+
+import os
+import tarfile
+import tempfile
+from pathlib import Path
+
+import httpx
+
+from mindxtrain.provenance.hashing import blake3_dir
+from mindxtrain.storage.provider import StorageProvider, StorageRef
+
+
+def _stub_cid(checkpoint_dir: Path) -> str:
+    digest = blake3_dir(checkpoint_dir)
+    return f"cid://stub-{digest[:32]}"
+
+
+def publish_to_lighthouse(
+    checkpoint_dir: Path,
+    *,
+    api_key: str | None = None,
+    base_url: str | None = None,
+    timeout_s: float = 600.0,
+) -> str:
+    """Tar + pin `checkpoint_dir` to Lighthouse; return a `cid://...` URI.
+
+    If `LIGHTHOUSE_API_KEY` is unset we return a deterministic stub CID
+    derived from BLAKE3, so dev runs still produce a valid manifest.
+    """
+    api_key = api_key or os.environ.get("LIGHTHOUSE_API_KEY", "")
+    if not api_key:
+        return _stub_cid(checkpoint_dir)
+    base_url = (base_url or os.environ.get("LIGHTHOUSE_BASE_URL", "https://node.lighthouse.storage")).rstrip("/")
+
+    # Tar the directory to a temp file so we upload one binary blob.
+    with tempfile.NamedTemporaryFile(suffix=".tar", delete=False) as tmp:
+        tar_path = Path(tmp.name)
+    try:
+        with tarfile.open(tar_path, "w") as tf:
+            tf.add(str(checkpoint_dir), arcname=checkpoint_dir.name)
+        with tar_path.open("rb") as fh, httpx.Client(timeout=timeout_s) as client:
+            resp = client.post(
+                f"{base_url}/api/v0/add",
+                files={"file": (f"{checkpoint_dir.name}.tar", fh, "application/x-tar")},
+                headers={"Authorization": f"Bearer {api_key}"},
+            )
+            resp.raise_for_status()
+            data = resp.json()
+    finally:
+        tar_path.unlink(missing_ok=True)
+
+    cid = data.get("Hash") or data.get("cid")
+    if not cid:
+        msg = f"Lighthouse response missing CID: {data}"
+        raise RuntimeError(msg)
+    return f"cid://{cid}"
+
+
+class LighthouseProvider(StorageProvider):
+    """`StorageProvider` adapter over `publish_to_lighthouse`."""
+
+    name = "lighthouse"
+
+    def put_dir(self, src: Path, key: str) -> StorageRef:
+        _ = key
+        cid = publish_to_lighthouse(src)
+        return StorageRef(provider=self.name, uri=cid)
+
+    def get_dir(self, ref: StorageRef, dest: Path) -> Path:
+        # Lighthouse fetch goes through any IPFS gateway; not implemented here.
+        # The user can `ipfs get ` from a kubo node or use the
+        # `mindxtrain.storage.ipfs` provider for downloads.
+        msg = "LighthouseProvider.get_dir — fetch via mindxtrain.storage.ipfs or kubo CLI."
+        raise NotImplementedError(msg)
+
+
+__all__ = ["LighthouseProvider", "publish_to_lighthouse"]
diff --git a/mindxtrain/storage/local_fs.py b/mindxtrain/storage/local_fs.py
new file mode 100644
index 0000000000000000000000000000000000000000..85267866c87988473dd2da36249c7a174d5b75d3
--- /dev/null
+++ b/mindxtrain/storage/local_fs.py
@@ -0,0 +1,37 @@
+"""Always-available local filesystem `StorageProvider` implementation.
+
+Canonical mindxtrain2.md §Part 4 `storage.local_fs`. Acts as the dev/CI fallback
+when no remote provider is configured.
+"""
+
+from __future__ import annotations
+
+import shutil
+from pathlib import Path
+
+from mindxtrain.storage.provider import StorageProvider, StorageRef
+
+
+class LocalFsProvider(StorageProvider):
+    name = "local_fs"
+
+    def __init__(self, root: Path) -> None:
+        self.root = root.resolve()
+        self.root.mkdir(parents=True, exist_ok=True)
+
+    def put_dir(self, src: Path, key: str) -> StorageRef:
+        dest = self.root / key
+        if dest.exists():
+            shutil.rmtree(dest)
+        shutil.copytree(src, dest)
+        return StorageRef(provider=self.name, uri=str(dest))
+
+    def get_dir(self, ref: StorageRef, dest: Path) -> Path:
+        if ref.provider != self.name:
+            msg = f"ref provider {ref.provider!r} != {self.name!r}"
+            raise ValueError(msg)
+        src = Path(ref.uri)
+        if dest.exists():
+            shutil.rmtree(dest)
+        shutil.copytree(src, dest)
+        return dest
diff --git a/mindxtrain/storage/provider.py b/mindxtrain/storage/provider.py
new file mode 100644
index 0000000000000000000000000000000000000000..414bca8141e9add141fc7e47109cecb1ad7fa252
--- /dev/null
+++ b/mindxtrain/storage/provider.py
@@ -0,0 +1,32 @@
+"""StorageProvider — uniform interface across local fs / HF Hub / Lighthouse / IPFS.
+
+Canonical mindxtrain2.md §Part 4 `storage.provider`. Concrete implementations
+live alongside (`local_fs`, `hf_hub`, `lighthouse`, `ipfs`). Selected at
+runtime via `cfg.publish.storage_provider`.
+"""
+
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from pathlib import Path
+
+from pydantic import BaseModel, ConfigDict
+
+
+class StorageRef(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    provider: str
+    uri: str
+
+
+class StorageProvider(ABC):
+    """Pluggable storage backend."""
+
+    name: str
+
+    @abstractmethod
+    def put_dir(self, src: Path, key: str) -> StorageRef: ...
+
+    @abstractmethod
+    def get_dir(self, ref: StorageRef, dest: Path) -> Path: ...
diff --git a/mindxtrain/train/__init__.py b/mindxtrain/train/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6865d28e4a993823f14e0d70338a405ff61db386
--- /dev/null
+++ b/mindxtrain/train/__init__.py
@@ -0,0 +1,15 @@
+"""Training subpackage.
+
+Re-exports the public API symbols expected by the rest of the codebase and the
+test suite (`mindxtrain.train.compile_axolotl_yaml`,
+`mindxtrain.train.autotune_overrides_summary`, ...).
+"""
+
+from mindxtrain.train.axolotl_compile import autotune_overrides_summary, compile_axolotl_yaml
+from mindxtrain.train.dispatch import dispatch_training
+
+__all__ = [
+    "autotune_overrides_summary",
+    "compile_axolotl_yaml",
+    "dispatch_training",
+]
diff --git a/mindxtrain/train/axolotl_compile.py b/mindxtrain/train/axolotl_compile.py
new file mode 100644
index 0000000000000000000000000000000000000000..e7747cb7ba8746d04a8ea76e6b73bb5748d84bdf
--- /dev/null
+++ b/mindxtrain/train/axolotl_compile.py
@@ -0,0 +1,187 @@
+"""Compile XTrainConfig + AutotunePlan into an Axolotl YAML dict.
+
+Pure Python — no GPU, no torch. The output is what gets written to
+`out//axolotl.yaml` and consumed by `accelerate launch -m axolotl.cli.train`.
+
+Apply order (later overrides earlier):
+    1. base from XTrainConfig (model / data / train / eval).
+    2. AutotunePlan overrides (attention_backend, suggested_micro_batch_size, etc.).
+    3. cfg.train.env merged with plan-derived env additions.
+"""
+
+from __future__ import annotations
+
+from typing import Any
+
+from mindxtrain.autotune.plan import AutotunePlan
+from mindxtrain.config.schema import (
+    CptMethod,
+    DpoMethod,
+    FullMethod,
+    GrpoMethod,
+    GspoMethod,
+    KtoMethod,
+    LoraMethod,
+    OrpoMethod,
+    QLoraMethod,
+    XTrainConfig,
+)
+
+
+def _method_block(cfg: XTrainConfig) -> dict[str, Any]:
+    """Translate cfg.train.method into Axolotl-flavored fields."""
+    m = cfg.train.method
+    if isinstance(m, FullMethod):
+        return {"adapter": None}
+    if isinstance(m, LoraMethod):
+        return {
+            "adapter": "lora",
+            "lora_r": m.r,
+            "lora_alpha": m.alpha,
+            "lora_dropout": m.dropout,
+            "lora_target_modules": list(m.target_modules),
+        }
+    if isinstance(m, QLoraMethod):
+        return {
+            "adapter": "qlora",
+            "lora_r": m.r,
+            "lora_alpha": m.alpha,
+            "lora_dropout": m.dropout,
+            "lora_target_modules": list(m.target_modules),
+            "load_in_4bit": m.quant_bits == 4,
+            "load_in_8bit": m.quant_bits == 8,
+        }
+    if isinstance(m, DpoMethod):
+        return {"rl": "dpo", "rl_beta": m.beta}
+    if isinstance(m, OrpoMethod):
+        return {"rl": "orpo", "rl_beta": m.beta}
+    if isinstance(m, GrpoMethod):
+        return {"rl": "grpo", "rl_num_generations": m.num_generations, "rl_kl_coef": m.kl_coef}
+    if isinstance(m, GspoMethod):
+        return {"rl": "gspo", "rl_num_generations": m.num_generations}
+    if isinstance(m, KtoMethod):
+        return {"rl": "kto", "rl_beta": m.beta}
+    if isinstance(m, CptMethod):
+        return {"adapter": None, "pretraining": True}
+    msg = f"unhandled method kind: {m!r}"
+    raise ValueError(msg)
+
+
+def _attention_field(plan_backend: str) -> dict[str, Any]:
+    """Map autotune plan's attention_backend onto Axolotl's flash_attention flags."""
+    if plan_backend == "ck":
+        return {"flash_attention": True, "flash_attn_backend": "ck"}
+    if plan_backend == "triton":
+        return {"flash_attention": True, "flash_attn_backend": "triton"}
+    if plan_backend == "aiter":
+        return {"flash_attention": True, "flash_attn_backend": "aiter"}
+    msg = f"unknown attention backend in plan: {plan_backend!r}"
+    raise ValueError(msg)
+
+
+def _plan_env(cfg: XTrainConfig, plan: AutotunePlan) -> dict[str, str]:
+    """Merge cfg.train.env with plan-derived additions; plan wins on conflict."""
+    env = dict(cfg.train.env)
+    env["PYTORCH_ROCM_ARCH"] = plan.gpu_arch
+    if plan.rccl_config == "8gpu_xgmi":
+        env["NCCL_MIN_NCHANNELS"] = "112"
+        env["GPU_MAX_HW_QUEUES"] = "1"
+    if plan.attention_backend == "triton":
+        # Force Triton path; otherwise CK is default.
+        env["VLLM_USE_TRITON_FLASH_ATTN"] = "1"
+    return env
+
+
+def compile_axolotl_yaml(cfg: XTrainConfig, plan: AutotunePlan) -> dict[str, Any]:
+    """Return an Axolotl-compatible YAML dict.
+
+    The caller writes this to disk via `yaml.safe_dump`.
+    """
+    micro_batch = min(cfg.train.batch.per_device, plan.suggested_micro_batch_size)
+
+    out: dict[str, Any] = {
+        # --- base / model ----------------------------------------------------
+        "base_model": cfg.model.name,
+        "model_type": "AutoModelForCausalLM",
+        "tokenizer_type": "AutoTokenizer",
+        "trust_remote_code": cfg.model.trust_remote_code,
+        "torch_dtype": cfg.model.torch_dtype,
+
+        # --- precision -------------------------------------------------------
+        "bf16": cfg.train.precision == "bfloat16",
+        "fp16": cfg.train.precision == "float16",
+        "gradient_checkpointing": cfg.train.gradient_checkpointing,
+
+        # --- data ------------------------------------------------------------
+        "datasets": [
+            {
+                "path": cfg.data.hf_id,
+                "type": "alpaca",  # Axolotl format hint; recipe-overridable.
+                "split": cfg.data.split,
+            },
+        ],
+        "sequence_len": cfg.data.seq_len,
+        "sample_packing": cfg.data.packing,
+        "streaming": cfg.data.streaming,
+
+        # --- batch -----------------------------------------------------------
+        "micro_batch_size": micro_batch,
+        "gradient_accumulation_steps": cfg.train.batch.grad_accum,
+
+        # --- optimizer / schedule -------------------------------------------
+        "optimizer": cfg.train.optimizer.name,
+        "learning_rate": cfg.train.optimizer.lr,
+        "adam_beta1": cfg.train.optimizer.betas[0],
+        "adam_beta2": cfg.train.optimizer.betas[1],
+        "weight_decay": cfg.train.optimizer.weight_decay,
+        "max_grad_norm": cfg.train.optimizer.grad_clip,
+        "lr_scheduler": cfg.train.schedule.type,
+        "warmup_ratio": cfg.train.schedule.warmup_ratio,
+        "num_epochs": cfg.train.schedule.epochs,
+
+        # --- run identity ---------------------------------------------------
+        "seed": cfg.meta.seed,
+        "wandb_project": cfg.meta.project,
+        "wandb_run_id": cfg.meta.run_name,
+        "output_dir": f"./runs/{cfg.meta.run_name}/checkpoint",
+    }
+
+    # --- method (LoRA / QLoRA / DPO / GRPO / etc.) --------------------------
+    out.update(_method_block(cfg))
+
+    # --- attention backend (autotune plan overrides cfg) --------------------
+    out.update(_attention_field(plan.attention_backend))
+
+    # --- FSDP ---------------------------------------------------------------
+    if cfg.train.fsdp.enabled:
+        out["fsdp"] = "full_shard auto_wrap" if cfg.train.fsdp.auto_wrap else "full_shard"
+        out["fsdp_config"] = {
+            "fsdp_offload_params": False,
+            "fsdp_state_dict_type": "FULL_STATE_DICT",
+        }
+
+    # --- env (set in subprocess before launching) ---------------------------
+    out["env"] = _plan_env(cfg, plan)
+
+    # --- max_samples cap (truncate streaming dataset) -----------------------
+    if cfg.data.max_samples is not None:
+        out["max_samples"] = cfg.data.max_samples
+
+    return out
+
+
+def autotune_overrides_summary(plan: AutotunePlan) -> list[str]:
+    """Human-readable summary of how the plan modified the cfg.
+
+    Used by `mindxtrain train` to print why the run is different from the
+    raw YAML the user wrote.
+    """
+    summary: list[str] = []
+    summary.append(f"attention_backend={plan.attention_backend} (autotune-selected)")
+    summary.append(f"gemm_heuristic={plan.gemm_heuristic}")
+    summary.append(f"rccl_config={plan.rccl_config}")
+    if plan.suggested_micro_batch_size:
+        summary.append(f"suggested_micro_batch_size={plan.suggested_micro_batch_size}")
+    if plan.suggested_lora_rank:
+        summary.append(f"suggested_lora_rank={plan.suggested_lora_rank}")
+    return summary
diff --git a/mindxtrain/train/backend_primus.py b/mindxtrain/train/backend_primus.py
new file mode 100644
index 0000000000000000000000000000000000000000..525510ddd0449ced6b29b68d18662ac78a4d33f8
--- /dev/null
+++ b/mindxtrain/train/backend_primus.py
@@ -0,0 +1,65 @@
+"""Primus / Primus-Turbo backend — pretraining-scale (>70 B params).
+
+Subprocess wrapper that expects the rocm/primus:v26.2 container to be active
+(Primus, AITER, Composable Kernel, hipBLASLt, FP8 transformer-engine support
+all live there). Lazy availability check.
+"""
+
+from __future__ import annotations
+
+import importlib.util
+import os
+import shutil
+import subprocess
+import sys
+from pathlib import Path
+
+import yaml
+
+from mindxtrain.autotune.plan import AutotunePlan
+from mindxtrain.config.schema import XTrainConfig
+from mindxtrain.train.axolotl_compile import compile_axolotl_yaml
+
+
+def _primus_available() -> bool:
+    return (
+        importlib.util.find_spec("primus_turbo") is not None
+        or shutil.which("primus") is not None
+        or os.environ.get("PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32") is not None
+    )
+
+
+def run_primus(cfg: XTrainConfig, plan: AutotunePlan, out_dir: Path) -> Path:
+    """Run a Primus pretraining job; return the checkpoint directory."""
+    if not _primus_available():
+        msg = (
+            "primus_turbo not installed and not running inside rocm/primus:v26.2; "
+            "see ops/containerfiles/containerfile_train and pull the container."
+        )
+        raise RuntimeError(msg)
+
+    out_dir = Path(out_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    yaml_payload = compile_axolotl_yaml(cfg, plan)
+    yaml_path = out_dir / f"{cfg.meta.run_name}.primus.yaml"
+    yaml_path.write_text(yaml.safe_dump(yaml_payload, sort_keys=False))
+
+    log_path = out_dir / "train.log"
+    env = dict(os.environ)
+    env["PYTORCH_ROCM_ARCH"] = "gfx942"
+    env["PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32"] = "1"
+    if plan.rccl_config == "8gpu_xgmi":
+        env["NCCL_MIN_NCHANNELS"] = "112"
+
+    cmd = ["python", "-m", "primus_turbo.train", "--config", str(yaml_path)]
+    with log_path.open("w") as log:
+        proc = subprocess.run(cmd, stdout=log, stderr=subprocess.STDOUT, env=env, check=False)
+    if proc.returncode != 0:
+        sys.stderr.write(f"primus returned {proc.returncode}; see {log_path}\n")
+        raise SystemExit(proc.returncode)
+
+    return out_dir / yaml_payload.get("output_dir", "checkpoint")
+
+
+__all__ = ["run_primus"]
diff --git a/mindxtrain/train/backend_torchtune.py b/mindxtrain/train/backend_torchtune.py
new file mode 100644
index 0000000000000000000000000000000000000000..039af0022084d2d8c5d81f7c2f69bb66f189a934
--- /dev/null
+++ b/mindxtrain/train/backend_torchtune.py
@@ -0,0 +1,57 @@
+"""torchtune backend — modular reference recipes.
+
+Subprocess wrapper around `tune run`. Lazy availability check; opt-in via
+`uv pip install torchtune`.
+"""
+
+from __future__ import annotations
+
+import importlib.util
+import os
+import shutil
+import subprocess
+import sys
+from pathlib import Path
+
+import yaml
+
+from mindxtrain.autotune.plan import AutotunePlan
+from mindxtrain.config.schema import XTrainConfig
+from mindxtrain.train.axolotl_compile import compile_axolotl_yaml
+
+
+def _torchtune_available() -> bool:
+    return importlib.util.find_spec("torchtune") is not None or shutil.which("tune") is not None
+
+
+def run_torchtune(cfg: XTrainConfig, plan: AutotunePlan, out_dir: Path) -> Path:
+    """Run a torchtune training job; return the checkpoint directory."""
+    if not _torchtune_available():
+        msg = (
+            "torchtune not installed; `uv pip install torchtune`. Note: Qwen3 "
+            "recipes are not yet upstream — ship a small PR as a side deliverable."
+        )
+        raise RuntimeError(msg)
+
+    out_dir = Path(out_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    yaml_payload = compile_axolotl_yaml(cfg, plan)
+    yaml_path = out_dir / f"{cfg.meta.run_name}.torchtune.yaml"
+    yaml_path.write_text(yaml.safe_dump(yaml_payload, sort_keys=False))
+
+    log_path = out_dir / "train.log"
+    env = dict(os.environ)
+    env["PYTORCH_ROCM_ARCH"] = "gfx942"
+
+    cmd = ["tune", "run", "--config", str(yaml_path)]
+    with log_path.open("w") as log:
+        proc = subprocess.run(cmd, stdout=log, stderr=subprocess.STDOUT, env=env, check=False)
+    if proc.returncode != 0:
+        sys.stderr.write(f"torchtune returned {proc.returncode}; see {log_path}\n")
+        raise SystemExit(proc.returncode)
+
+    return out_dir / yaml_payload.get("output_dir", "checkpoint")
+
+
+__all__ = ["run_torchtune"]
diff --git a/mindxtrain/train/backend_trl_cpu.py b/mindxtrain/train/backend_trl_cpu.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee7bf980f98419384ab5f511699e3bc414909f89
--- /dev/null
+++ b/mindxtrain/train/backend_trl_cpu.py
@@ -0,0 +1,534 @@
+"""CPU training backend — real SFT/LoRA via TRL, no GPU required.
+
+The closed-loop case: mindX produces a small JSONL dataset (dream cycle),
+mindXtrain needs to fine-tune a tiny base model on it locally without
+provisioning a MI300X droplet. This backend exists so that mindX agents can
+trigger self-training on commodity hardware; it is also the smoke lane for
+any new recipe before burning AMD credits.
+
+Slow but produces a *real* checkpoint and is compatible with the rest of the
+pipeline (`mindxtrain quantize`, `mindxtrain receipt`, `publish` — all
+expect a HF-format checkpoint directory).
+
+This module follows the project lazy-import contract: importing the module
+must succeed on a base install, but calling `run_trl_cpu` requires
+`uv sync --extra ml`.
+"""
+
+from __future__ import annotations
+
+import os
+from collections.abc import Callable, Iterator
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+from mindxtrain.autotune.plan import AutotunePlan
+from mindxtrain.config.schema import (
+    LoraMethod,
+    QLoraMethod,
+    XTrainConfig,
+    resolve_thread_count,
+)
+
+
+def _apply_cpu_throttle(cfg: XTrainConfig, sink: Callable[[str], None]) -> int:
+    """Apply the recipe's `cfg.train.cpu_throttle` to the current process.
+
+    Resolves percent → thread count against the host's actual core count,
+    sets every thread-pool env var the downstream stack respects (torch,
+    OpenMP, MKL, OpenBLAS), optionally pins OpenMP threads to cores via
+    OMP_PROC_BIND=close + OMP_PLACES=cores (Ryzen-friendly: keeps threads
+    on the same CCX chiplet, reduces cross-CCX cache traffic for the
+    small matmuls CPU training does), and shifts the process's POSIX nice
+    level so the rest of the laptop stays usable.
+
+    Must be called BEFORE torch is imported / first used, otherwise the
+    thread-pool size is locked at whatever torch saw on first init.
+
+    Returns the resolved thread count for logging.
+    """
+    throttle = cfg.train.cpu_throttle
+    total_cores = os.cpu_count() or 1
+    threads = resolve_thread_count(throttle.percent, total_cores)
+
+    # Set every thread-pool env var the downstream stack reads. PyTorch's
+    # ATen reads OMP_NUM_THREADS at first init; MKL and OpenBLAS each
+    # have their own knob. All must agree to actually cap the workload.
+    os.environ["OMP_NUM_THREADS"] = str(threads)
+    os.environ["MKL_NUM_THREADS"] = str(threads)
+    os.environ["OPENBLAS_NUM_THREADS"] = str(threads)
+    os.environ["NUMEXPR_NUM_THREADS"] = str(threads)
+    # tokenizers (HF Rust lib) has its own parallelism that BLAS doesn't
+    # cap. Disable it during throttled runs — for a 135M smoke on 1-2
+    # threads, the tokenizer-side parallelism only thrashes the cache.
+    os.environ.setdefault("TOKENIZERS_PARALLELISM", "false" if threads <= 2 else "true")
+
+    if throttle.omp_proc_bind:
+        # CCX-aware pinning. `close` = threads adjacent to the master;
+        # `cores` = one thread per physical core. Both safe on non-AMD
+        # CPUs; ignored by OpenMP runtimes that don't honor them.
+        os.environ["OMP_PROC_BIND"] = "close"
+        os.environ["OMP_PLACES"] = "cores"
+
+    if throttle.nice_level != 0:
+        try:
+            os.nice(throttle.nice_level)
+            sink(f"[trl_cpu] nice level set to {throttle.nice_level}")
+        except (PermissionError, OSError) as exc:
+            # Negative nice needs CAP_SYS_NICE. Surface, don't fail.
+            sink(
+                f"[trl_cpu] nice({throttle.nice_level}) refused "
+                f"({type(exc).__name__}: {exc}) — continuing without it",
+            )
+
+    sink(
+        f"[trl_cpu] throttle: {throttle.percent}% of {total_cores} cores "
+        f"→ {threads} thread(s); OMP_PROC_BIND={'close' if throttle.omp_proc_bind else 'off'}",
+    )
+    return threads
+
+
+def _require_ml_deps() -> dict[str, Any]:
+    """Import TRL + transformers + peft + datasets eagerly; surface a single message."""
+    missing: list[str] = []
+    try:
+        from datasets import Dataset  # type: ignore
+    except ImportError:
+        missing.append("datasets")
+        Dataset = None  # type: ignore
+    try:
+        from transformers import AutoModelForCausalLM, AutoTokenizer  # type: ignore
+    except ImportError:
+        missing.append("transformers")
+        AutoModelForCausalLM = AutoTokenizer = None  # type: ignore
+    try:
+        from trl import SFTConfig, SFTTrainer  # type: ignore
+    except ImportError:
+        missing.append("trl")
+        SFTConfig = SFTTrainer = None  # type: ignore
+    try:
+        from peft import LoraConfig  # type: ignore
+    except ImportError:
+        # peft only required for LoRA/QLoRA — kept optional here
+        LoraConfig = None  # type: ignore
+
+    if missing:
+        msg = (
+            f"CPU training backend requires {', '.join(missing)} — "
+            "run `uv sync --extra ml`."
+        )
+        raise RuntimeError(msg)
+
+    return {
+        "Dataset": Dataset,
+        "AutoModelForCausalLM": AutoModelForCausalLM,
+        "AutoTokenizer": AutoTokenizer,
+        "SFTConfig": SFTConfig,
+        "SFTTrainer": SFTTrainer,
+        "LoraConfig": LoraConfig,
+    }
+
+
+def _stream_dataset_rows(cfg: XTrainConfig) -> Iterator[dict[str, Any]]:
+    """Yield raw dataset rows for the configured DataCfg.source."""
+    from mindxtrain.data.curate import load_streaming_dataset
+
+    yield from load_streaming_dataset(cfg.data)
+
+
+def _materialize_dataset(cfg: XTrainConfig, Dataset: Any) -> Any:
+    """Pull the stream into an in-memory `datasets.Dataset` for TRL.
+
+    CPU corpora are small by construction; we don't try to stream into TRL
+    here since `SFTTrainer` wants a `Dataset` with `__len__`. Cap at
+    `cfg.data.max_samples or 50000` to keep memory bounded.
+    """
+    cap = cfg.data.max_samples if cfg.data.max_samples is not None else 50_000
+    rows: list[dict[str, Any]] = []
+    for row in _stream_dataset_rows(cfg):
+        rows.append(row)
+        if len(rows) >= cap:
+            break
+    if not rows:
+        msg = (
+            f"data.source={cfg.data.source!r} yielded zero examples — "
+            "check the `path` / `hf_id` and that the source has data."
+        )
+        raise RuntimeError(msg)
+    return Dataset.from_list(rows)
+
+
+def _build_lora_config(cfg: XTrainConfig, LoraConfig: Any) -> Any | None:
+    method = cfg.train.method
+    if isinstance(method, LoraMethod) or isinstance(method, QLoraMethod):
+        if LoraConfig is None:
+            msg = "peft not installed; `uv sync --extra ml` or drop method.kind to 'full'."
+            raise RuntimeError(msg)
+        return LoraConfig(
+            r=method.r,
+            lora_alpha=method.alpha,
+            lora_dropout=method.dropout,
+            target_modules=list(method.target_modules),
+            bias="none",
+            task_type="CAUSAL_LM",
+        )
+    return None
+
+
+def _build_event_callback(
+    on_event: Callable[[dict[str, Any]], None],
+    sink: Callable[[str], None],
+) -> Any:
+    """Return a TrainerCallback that fires `on_event(dict)` per HF Trainer log.
+
+    Bridges in-process Trainer logs into the operator's SSE event stream
+    without going through stdout regex parsing. Each `on_log` call carries
+    a `{loss, learning_rate, grad_norm, …}` dict — we lift that into the
+    same shape `StepEvent` expects (step / loss / lr / grad_norm), and
+    emit `eval` + `status` events on the other Trainer hooks.
+
+    Lazy-imported `transformers.TrainerCallback` so this helper is only
+    realised when the backend actually runs (consistent with the rest of
+    this module).
+    """
+    from transformers import TrainerCallback  # type: ignore
+
+    class _CB(TrainerCallback):  # type: ignore[misc, valid-type]
+        def on_log(
+            self, args: Any, state: Any, control: Any,
+            logs: dict[str, float] | None = None, **_kw: Any,
+        ) -> None:
+            if not logs:
+                return
+            # HF Trainer emits multiple kinds of log: train step (has 'loss'),
+            # final summary (has 'train_loss'), and eval (has 'eval_loss').
+            # Map step-level logs to StepEvent.
+            if "loss" in logs:
+                on_event({
+                    "kind": "step",
+                    "step": int(state.global_step),
+                    "loss": float(logs["loss"]),
+                    "lr": float(logs["learning_rate"]) if "learning_rate" in logs else None,
+                    "grad_norm": float(logs["grad_norm"]) if "grad_norm" in logs else None,
+                    "tokens_per_s": None,
+                    # Realtime-feedback fields for the Coach progress bar +
+                    # "is it learning" chart. state.max_steps is the
+                    # authoritative total HF resolved after packing.
+                    "total_steps": (
+                        int(state.max_steps)
+                        if getattr(state, "max_steps", 0)
+                        else None
+                    ),
+                    "mean_token_accuracy": (
+                        float(logs["mean_token_accuracy"])
+                        if "mean_token_accuracy" in logs
+                        else None
+                    ),
+                    "entropy": (
+                        float(logs["entropy"]) if "entropy" in logs else None
+                    ),
+                })
+                sink(
+                    f"[trl_cpu] step={state.global_step} loss={logs['loss']:.4f}"
+                    + (f" lr={logs['learning_rate']:.2e}" if "learning_rate" in logs else "")
+                    + (f" grad_norm={logs['grad_norm']:.3f}" if "grad_norm" in logs else ""),
+                )
+
+        def on_evaluate(
+            self, args: Any, state: Any, control: Any,
+            metrics: dict[str, float] | None = None, **_kw: Any,
+        ) -> None:
+            if not metrics:
+                return
+            clean = {k: float(v) for k, v in metrics.items() if isinstance(v, (int, float))}
+            if clean:
+                on_event({
+                    "kind": "eval",
+                    "step": int(state.global_step),
+                    "suite": "mid_train",
+                    "metrics": clean,
+                })
+
+    return _CB()
+
+
+def _env_force_cpu() -> bool:
+    """`MINDXTRAIN_FORCE_CPU` forces the CPU fallback even when a GPU exists.
+
+    The escape hatch for parity testing and for pinning the deterministic CPU
+    path on a box that happens to have an accelerator.
+    """
+    return os.environ.get("MINDXTRAIN_FORCE_CPU", "").strip().lower() in {
+        "1", "true", "yes", "on",
+    }
+
+
+@dataclass(frozen=True)
+class _DevicePlan:
+    """Resolved device/dtype decisions for one in-process training run."""
+
+    label: str                  # human tag e.g. "cuda (bfloat16)" / "cpu (float32)"
+    device_map: dict[str, str]  # passed to from_pretrained
+    torch_dtype: Any            # a torch.dtype
+    attn_impl: str              # "sdpa" on GPU, "eager" on CPU
+    bf16: bool
+    fp16: bool
+    apply_cpu_throttle: bool    # only the CPU path throttles threads
+    batch_cap: int | None       # cap per-device batch (CPU=2); None = uncapped
+    grad_checkpointing: bool
+
+
+def _resolve_device(cfg: XTrainConfig, torch_mod: Any, *, force_cpu: bool) -> _DevicePlan:
+    """Pick the device/dtype for a run.
+
+    GPU when one is visible (ROCm exposes itself through `torch.cuda`) and not
+    forced off; otherwise the exact CPU defaults the legacy `trl_cpu` lane used.
+    """
+    if not force_cpu and torch_mod.cuda.is_available():
+        bf16 = bool(torch_mod.cuda.is_bf16_supported())
+        return _DevicePlan(
+            label=f"cuda ({'bfloat16' if bf16 else 'float16'})",
+            device_map={"": "cuda"},
+            torch_dtype=torch_mod.bfloat16 if bf16 else torch_mod.float16,
+            attn_impl="sdpa",
+            bf16=bf16,
+            fp16=not bf16,
+            apply_cpu_throttle=False,
+            batch_cap=None,
+            grad_checkpointing=cfg.train.gradient_checkpointing,
+        )
+    return _DevicePlan(
+        label="cpu (float32)",
+        device_map={"": "cpu"},
+        torch_dtype=torch_mod.float32,
+        attn_impl="eager",
+        bf16=False,
+        fp16=False,
+        apply_cpu_throttle=True,
+        batch_cap=2,
+        grad_checkpointing=False,  # CPU + checkpointing is pathologically slow
+    )
+
+
+def _capped_batch(per_device: int, cap: int | None) -> int:
+    """Clamp the per-device batch to `cap` (CPU=2); `None` leaves it as-is."""
+    return max(1, per_device if cap is None else min(per_device, cap))
+
+
+def run_trl_cpu(
+    cfg: XTrainConfig,
+    plan: AutotunePlan,
+    out_dir: Path,
+    *,
+    on_line: Callable[[str], None] | None = None,
+    on_event: Callable[[dict[str, Any]], None] | None = None,
+) -> Path:
+    """Force-CPU wrapper around `run_trl_local` (the mindX self-training lane).
+
+    Preserves the deterministic CPU behaviour the dream-cycle loop depends on:
+    float32, eager attention, CPU throttle, batch cap 2, no gradient checkpointing.
+    """
+    return run_trl_local(
+        cfg, plan, out_dir, force_cpu=True, on_line=on_line, on_event=on_event,
+    )
+
+
+def run_trl_local(
+    cfg: XTrainConfig,
+    plan: AutotunePlan,
+    out_dir: Path,
+    *,
+    force_cpu: bool = False,
+    on_line: Callable[[str], None] | None = None,
+    on_event: Callable[[dict[str, Any]], None] | None = None,
+) -> Path:
+    """Run a TRL SFT job on the local device; return the checkpoint directory.
+
+    Auto-detects an accelerator: uses the GPU (CUDA or ROCm, bf16/fp16) when one
+    is visible, otherwise falls back to CPU (float32) with a logged warning. Set
+    `force_cpu=True` (or `MINDXTRAIN_FORCE_CPU=1`) to pin the CPU path.
+
+    Same `on_line` / `on_event` streaming contract as the other lanes so the
+    Coach UI's live log + loss curve work identically on GPU and CPU.
+
+    `on_line` mirrors the axolotl backend signature so the Coach UI can
+    stream log lines uniformly across lanes. TRL doesn't emit one-line-per-
+    step by default, but we forward `transformers` log records via a tiny
+    handler so the streaming surface stays consistent.
+
+    `on_event` is the *structured* counterpart: each HF Trainer log fires
+    a dict with `{kind: "step", step, loss, lr, grad_norm, ...}` so the
+    Coach UI can populate its Chart.js loss curve directly, without
+    stdout-parsing the way the axolotl subprocess path does. When
+    provided, the loss chart fills in in real time during training.
+
+    On the CPU path, applies `cfg.train.cpu_throttle` before torch initializes
+    so the thread-pool size actually caps the workload (BLAS layers snapshot
+    their thread count on first use). The GPU path skips throttling entirely.
+    """
+    sink = on_line if on_line is not None else (lambda _line: None)
+    forced_cpu = force_cpu or _env_force_cpu()
+    tag = "trl_cpu" if forced_cpu else "trl_local"
+
+    # When the CPU path is known up front (force_cpu), throttle BEFORE importing
+    # the ML stack — exactly the legacy ordering the dream-loop relies on.
+    threads: int | None = None
+    if forced_cpu:
+        threads = _apply_cpu_throttle(cfg, sink)
+
+    deps = _require_ml_deps()
+    Dataset = deps["Dataset"]
+    AutoModelForCausalLM = deps["AutoModelForCausalLM"]
+    AutoTokenizer = deps["AutoTokenizer"]
+    SFTConfig = deps["SFTConfig"]
+    SFTTrainer = deps["SFTTrainer"]
+    LoraConfig = deps["LoraConfig"]
+
+    import torch  # type: ignore
+
+    device = _resolve_device(cfg, torch, force_cpu=forced_cpu)
+    if device.apply_cpu_throttle:
+        if threads is None:
+            # Auto-detected CPU fallback (no accelerator visible). Throttle now;
+            # `set_num_threads` still caps at runtime even post-import.
+            if not forced_cpu:
+                sink(f"[{tag}] no accelerator detected → CPU fallback (float32)")
+            threads = _apply_cpu_throttle(cfg, sink)
+        # ATen reads OMP_NUM_THREADS at init, but `set_num_threads` is the
+        # canonical knob — call both belt-and-suspenders.
+        torch.set_num_threads(threads)
+        try:
+            torch.set_num_interop_threads(threads)
+        except RuntimeError:
+            # Must be called before any aten op runs; OMP env var is the fallback.
+            pass
+    else:
+        sink(f"[{tag}] accelerator detected → {device.label}")
+
+    out_dir = Path(out_dir)
+    checkpoint_dir = out_dir / "checkpoint"
+    checkpoint_dir.mkdir(parents=True, exist_ok=True)
+
+    sink(f"[{tag}] base={cfg.model.name} method={cfg.train.method.kind}")
+
+    tokenizer = AutoTokenizer.from_pretrained(cfg.model.name, use_fast=True)
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+    # Some base tokenizers (e.g., SmolLM2-135M) don't ship a chat
+    # template. The dream corpus is ChatML-shaped already, so we set
+    # ChatML explicitly when missing — matches what mindX's machine_
+    # dreaming phase 5b emits. Qwen / Llama base models keep their own
+    # template untouched.
+    if getattr(tokenizer, "chat_template", None) is None:
+        tokenizer.chat_template = (
+            "{% for message in messages %}"
+            "<|im_start|>{{ message['role'] }}\n{{ message['content'] }}<|im_end|>\n"
+            "{% endfor %}"
+            "{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
+        )
+        sink(f"[{tag}] tokenizer had no chat_template; set ChatML default.")
+
+    sink(f"[{tag}] materializing dataset (in-memory)")
+    full_dataset = _materialize_dataset(cfg, Dataset)
+    sink(f"[{tag}] dataset size={len(full_dataset)}")
+
+    # Optional train/eval split — deterministic via meta.seed so the same
+    # recipe always carves the same held-out rows. When eval_split is
+    # None we pass the full dataset to SFTTrainer (legacy behaviour);
+    # otherwise we split, pass train_dataset + eval_dataset, and turn on
+    # step-based evaluation so eval_loss appears in log_history.
+    train_dataset = full_dataset
+    eval_dataset = None
+    if cfg.data.eval_split is not None:
+        n = len(full_dataset)
+        if n < 4:
+            sink(
+                f"[{tag}] dataset too small ({n} rows) for eval_split="
+                f"{cfg.data.eval_split}; skipping held-out split",
+            )
+        else:
+            split = full_dataset.train_test_split(
+                test_size=cfg.data.eval_split, seed=cfg.meta.seed,
+            )
+            train_dataset, eval_dataset = split["train"], split["test"]
+            sink(
+                f"[{tag}] split: train={len(train_dataset)} "
+                f"eval={len(eval_dataset)} (seed={cfg.meta.seed})",
+            )
+
+    sink(f"[{tag}] loading base model on {device.label}")
+    model = AutoModelForCausalLM.from_pretrained(
+        cfg.model.name,
+        torch_dtype=device.torch_dtype,
+        device_map=device.device_map,
+        attn_implementation=device.attn_impl,
+    )
+
+    peft_config = _build_lora_config(cfg, LoraConfig)
+
+    # Estimate max_steps so we can floor logging_steps + eval_steps for
+    # short runs. HF Trainer computes max_steps as
+    # num_epochs * (len(train_dataset) // (batch * grad_accum)). When
+    # packing is on, sample count drops post-tokenization — this estimate
+    # is a lower bound but good enough for the cadence floor.
+    per_device_batch = _capped_batch(cfg.train.batch.per_device, device.batch_cap)
+    eff_batch = per_device_batch * cfg.train.batch.grad_accum
+    est_max_steps = max(1, (len(train_dataset) // eff_batch) * cfg.train.schedule.epochs)
+    effective_logging_steps = max(1, min(cfg.train.logging_steps, max(1, est_max_steps // 4)))
+    effective_eval_steps = max(1, est_max_steps // 4)
+    sink(
+        f"[{tag}] est_max_steps={est_max_steps} "
+        f"logging_steps={effective_logging_steps} "
+        f"eval_steps={effective_eval_steps if eval_dataset is not None else 'off'}",
+    )
+
+    sft_kwargs: dict[str, Any] = dict(
+        output_dir=str(checkpoint_dir),
+        num_train_epochs=cfg.train.schedule.epochs,
+        per_device_train_batch_size=per_device_batch,
+        gradient_accumulation_steps=cfg.train.batch.grad_accum,
+        learning_rate=cfg.train.optimizer.lr,
+        warmup_ratio=cfg.train.schedule.warmup_ratio,
+        max_length=cfg.data.seq_len,
+        packing=cfg.data.packing,
+        logging_steps=effective_logging_steps,
+        save_strategy="epoch",
+        report_to="none",
+        bf16=device.bf16,
+        fp16=device.fp16,
+        gradient_checkpointing=device.grad_checkpointing,
+        seed=cfg.meta.seed,
+    )
+    if eval_dataset is not None:
+        sft_kwargs["eval_strategy"] = "steps"
+        sft_kwargs["eval_steps"] = effective_eval_steps
+        sft_kwargs["per_device_eval_batch_size"] = per_device_batch
+    sft_args = SFTConfig(**sft_kwargs)
+
+    trainer_kwargs: dict[str, Any] = dict(
+        model=model,
+        args=sft_args,
+        train_dataset=train_dataset,
+        peft_config=peft_config,
+        processing_class=tokenizer,
+    )
+    if eval_dataset is not None:
+        trainer_kwargs["eval_dataset"] = eval_dataset
+    trainer = SFTTrainer(**trainer_kwargs)
+    # Wire the structured event callback if the caller wants per-step
+    # telemetry (Coach UI's loss chart). When `on_event` is None this is
+    # a no-op — the CLI path doesn't need it.
+    if on_event is not None:
+        trainer.add_callback(_build_event_callback(on_event, sink))
+
+    sink(f"[{tag}] starting trainer.train()")
+    trainer.train()
+    sink(f"[{tag}] training complete, saving checkpoint")
+    trainer.save_model(str(checkpoint_dir))
+    tokenizer.save_pretrained(str(checkpoint_dir))
+    sink(f"[{tag}] checkpoint at {checkpoint_dir}")
+    return checkpoint_dir
+
+
+__all__ = ["run_trl_cpu", "run_trl_local"]
diff --git a/mindxtrain/train/backend_unsloth.py b/mindxtrain/train/backend_unsloth.py
new file mode 100644
index 0000000000000000000000000000000000000000..750413719663187bf979346fdd13330ffc091828
--- /dev/null
+++ b/mindxtrain/train/backend_unsloth.py
@@ -0,0 +1,67 @@
+"""Unsloth backend — fastest single-MI300X LoRA path.
+
+Subprocess wrapper around `unsloth.cli.train`. Lazy availability check;
+opt-in via `uv add unsloth` (the OneClickAMD partnership wheel).
+"""
+
+from __future__ import annotations
+
+import importlib.util
+import os
+import shutil
+import subprocess
+import sys
+from pathlib import Path
+
+import yaml
+
+from mindxtrain.autotune.plan import AutotunePlan
+from mindxtrain.config.schema import XTrainConfig
+from mindxtrain.train.axolotl_compile import compile_axolotl_yaml
+
+
+def _unsloth_available() -> bool:
+    return importlib.util.find_spec("unsloth") is not None
+
+
+def run_unsloth(cfg: XTrainConfig, plan: AutotunePlan, out_dir: Path) -> Path:
+    """Run an Unsloth LoRA training job; return the checkpoint directory."""
+    if not _unsloth_available():
+        msg = (
+            "unsloth not installed; install the OneClickAMD wheel per "
+            "https://github.com/unslothai/unsloth (ROCm support is opt-in)."
+        )
+        raise RuntimeError(msg)
+
+    out_dir = Path(out_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    # Reuse the Axolotl YAML as a portable trainer config — Unsloth consumes a
+    # near-identical schema.
+    yaml_payload = compile_axolotl_yaml(cfg, plan)
+    yaml_path = out_dir / f"{cfg.meta.run_name}.unsloth.yaml"
+    yaml_path.write_text(yaml.safe_dump(yaml_payload, sort_keys=False))
+
+    log_path = out_dir / "train.log"
+    env = dict(os.environ)
+    env["PYTORCH_ROCM_ARCH"] = "gfx942"
+
+    # Unsloth ships its own `unsloth-cli` entry; fall back to python -m if missing.
+    if shutil.which("unsloth-cli"):
+        cmd = ["unsloth-cli", "train", str(yaml_path)]
+    else:
+        cmd = ["python", "-m", "unsloth.cli.train", str(yaml_path)]
+
+    with log_path.open("w") as log:
+        log.write(f"# cmd: {' '.join(cmd)}\n\n")
+        log.flush()
+        proc = subprocess.run(cmd, stdout=log, stderr=subprocess.STDOUT, env=env, check=False)
+
+    if proc.returncode != 0:
+        sys.stderr.write(f"unsloth returned {proc.returncode}; see {log_path}\n")
+        raise SystemExit(proc.returncode)
+
+    return out_dir / yaml_payload.get("output_dir", "checkpoint")
+
+
+__all__ = ["run_unsloth"]
diff --git a/mindxtrain/train/callbacks.py b/mindxtrain/train/callbacks.py
new file mode 100644
index 0000000000000000000000000000000000000000..0adad176654eeda9b47dfb64dd8bbe1124bdddc0
--- /dev/null
+++ b/mindxtrain/train/callbacks.py
@@ -0,0 +1,208 @@
+"""Training callbacks — eval-during-training + checkpoint mgmt + UI stream.
+
+Subclasses `transformers.TrainerCallback` (lazy import). Returned as
+configuration objects whose `.callback()` method materializes the
+TrainerCallback when the trainer actually constructs.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Callable
+from pathlib import Path
+from typing import Any, Literal
+
+import httpx
+from pydantic import BaseModel, ConfigDict
+
+
+def _ensure_transformers() -> Any:
+    try:
+        from transformers import TrainerCallback
+
+        return TrainerCallback
+    except ImportError as exc:
+        msg = "transformers not installed; run `uv sync --extra ml`."
+        raise RuntimeError(msg) from exc
+
+
+class EvalDuringTraining(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    every_n_steps: int = 200
+    suite: Literal["mmlu", "gsm8k", "bfcl"] = "mmlu"
+
+    def callback(self, eval_fn: Callable[[int], dict[str, float]]) -> Any:
+        TrainerCallback = _ensure_transformers()
+        every = self.every_n_steps
+
+        class _CB(TrainerCallback):  # type: ignore[misc, valid-type]
+            def on_step_end(self, args: Any, state: Any, control: Any, **_kw: Any) -> None:
+                if state.global_step % every == 0 and state.global_step > 0:
+                    metrics = eval_fn(state.global_step)
+                    for k, v in metrics.items():
+                        state.log_history.append({"step": state.global_step, k: v})
+
+        return _CB()
+
+
+class BestCheckpointKeeper(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    out_dir: Path
+    metric: str = "eval_loss"
+    keep: int = 3
+    minimize: bool = True
+
+    def callback(self) -> Any:
+        TrainerCallback = _ensure_transformers()
+        out_dir = self.out_dir
+        metric = self.metric
+        keep = self.keep
+        minimize = self.minimize
+
+        class _CB(TrainerCallback):  # type: ignore[misc, valid-type]
+            def on_evaluate(self, args: Any, state: Any, control: Any, metrics: dict[str, float] | None = None, **_kw: Any) -> None:
+                if not metrics or metric not in metrics:
+                    return
+                # Naive top-k: keep `keep` checkpoints with the best metric.
+                ckpts: list[tuple[float, Path]] = []
+                for p in sorted(out_dir.glob("checkpoint-*")):
+                    log = p / "trainer_state.json"
+                    if not log.exists():
+                        continue
+                    # Load the most recent metric value for this checkpoint.
+                    try:
+                        import json
+
+                        st = json.loads(log.read_text())
+                        last = next(
+                            (h.get(metric) for h in reversed(st.get("log_history", [])) if metric in h),
+                            None,
+                        )
+                        if last is None:
+                            continue
+                        ckpts.append((float(last), p))
+                    except (OSError, json.JSONDecodeError, ValueError):
+                        continue
+                ckpts.sort(reverse=not minimize)
+                for _, path in ckpts[keep:]:
+                    import shutil
+
+                    shutil.rmtree(path, ignore_errors=True)
+
+        return _CB()
+
+
+class StreamCallback(BaseModel):
+    """Push step + eval events to the operator's loopback ingest endpoint.
+
+    Lives next to the other two callbacks; like them, the actual
+    `TrainerCallback` subclass is materialized lazily so this module
+    imports without `--extra ml`. The ingest endpoint is bound to
+    127.0.0.1 by `mindxtrain.operator.runs.is_loopback`, which is why
+    the default `sink_url` host is loopback and not configurable beyond it.
+    """
+
+    model_config = ConfigDict(extra="forbid")
+
+    run_id: str
+    sink_url: str = "http://127.0.0.1:8080/coach/api/runs/{run_id}/ingest"
+    timeout_s: float = 2.0
+    suite: Literal["mmlu", "gsm8k", "bfcl"] = "mmlu"
+
+    def _post(self, event: dict[str, Any]) -> None:
+        url = self.sink_url.format(run_id=self.run_id)
+        try:
+            with httpx.Client(timeout=self.timeout_s) as client:
+                client.post(url, json=event)
+        except httpx.HTTPError:
+            # Best-effort: a failed ingest never blocks training.
+            pass
+
+    def callback(self) -> Any:
+        TrainerCallback = _ensure_transformers()
+        run_id = self.run_id
+        post = self._post
+        suite = self.suite
+
+        class _CB(TrainerCallback):  # type: ignore[misc, valid-type]
+            def on_log(
+                self,
+                args: Any,
+                state: Any,
+                control: Any,
+                logs: dict[str, float] | None = None,
+                **_kw: Any,
+            ) -> None:
+                if not logs or "loss" not in logs:
+                    return
+                post(
+                    {
+                        "kind": "step",
+                        "run_id": run_id,
+                        "step": int(state.global_step),
+                        "loss": float(logs["loss"]),
+                        "lr": float(logs["learning_rate"]) if "learning_rate" in logs else None,
+                        "grad_norm": float(logs["grad_norm"]) if "grad_norm" in logs else None,
+                        "tokens_per_s": None,
+                    }
+                )
+
+            def on_evaluate(
+                self,
+                args: Any,
+                state: Any,
+                control: Any,
+                metrics: dict[str, float] | None = None,
+                **_kw: Any,
+            ) -> None:
+                if not metrics:
+                    return
+                clean = {k: float(v) for k, v in metrics.items() if isinstance(v, (int, float))}
+                if not clean:
+                    return
+                post(
+                    {
+                        "kind": "eval",
+                        "run_id": run_id,
+                        "step": int(state.global_step),
+                        "suite": suite,
+                        "metrics": clean,
+                    }
+                )
+
+            def on_train_end(self, args: Any, state: Any, control: Any, **_kw: Any) -> None:
+                post(
+                    {
+                        "kind": "status",
+                        "run_id": run_id,
+                        "status": "succeeded",
+                        "message": f"step={state.global_step}",
+                    }
+                )
+
+        return _CB()
+
+
+def eval_during_training(every_n_steps: int = 200, suite: Literal["mmlu", "gsm8k", "bfcl"] = "mmlu") -> EvalDuringTraining:
+    return EvalDuringTraining(every_n_steps=every_n_steps, suite=suite)
+
+
+def best_checkpoint_keeper(out_dir: Path, metric: str = "eval_loss", keep: int = 3) -> BestCheckpointKeeper:
+    return BestCheckpointKeeper(out_dir=out_dir, metric=metric, keep=keep)
+
+
+def stream_callback(run_id: str, sink_url: str | None = None) -> StreamCallback:
+    if sink_url is None:
+        return StreamCallback(run_id=run_id)
+    return StreamCallback(run_id=run_id, sink_url=sink_url)
+
+
+__all__ = [
+    "BestCheckpointKeeper",
+    "EvalDuringTraining",
+    "StreamCallback",
+    "best_checkpoint_keeper",
+    "eval_during_training",
+    "stream_callback",
+]
diff --git a/mindxtrain/train/dispatch.py b/mindxtrain/train/dispatch.py
new file mode 100644
index 0000000000000000000000000000000000000000..027c405cb27d98d8c6712402632f4f7b203a1f90
--- /dev/null
+++ b/mindxtrain/train/dispatch.py
@@ -0,0 +1,64 @@
+"""Training backend dispatch.
+
+Lane selection happens at `cfg.train.backend`:
+
+- `axolotl` — GPU SFT/LoRA via Axolotl subprocess (default for MI300X recipes).
+- `unsloth` — GPU SFT via Unsloth.
+- `torchtune` — GPU SFT via torchtune.
+- `primus` — AMD's training stack.
+- `trl_cpu` — CPU SFT/LoRA via TRL in-process. Real checkpoints, slow.
+  Use for: mindX self-training, smoke-testing a recipe before burning AMD
+  credits, anywhere a MI300X droplet isn't available.
+- `trl_local` — same in-process TRL trainer, but device-aware: uses a local
+  consumer GPU (CUDA or ROCm Radeon, bf16/fp16) when one is visible, else falls
+  back to CPU. One recipe runs on a laptop or a gaming GPU unchanged.
+
+The CPU lane is paired with `hardware.gpus: 0` in the recipe. The dispatcher
+itself does not enforce that pairing — the recipe is the source of truth —
+but the schema's `Literal[0, 1, 8]` constrains the GPU count.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+from mindxtrain.autotune.plan import AutotunePlan
+from mindxtrain.config.schema import XTrainConfig
+
+
+def dispatch_training(
+    cfg: XTrainConfig,
+    plan: AutotunePlan,
+    out_dir: Path,
+) -> Path:
+    """Dispatch a training run to the configured backend.
+
+    Returns the path to the produced checkpoint directory.
+    """
+    backend = cfg.train.backend
+    if backend == "axolotl":
+        from mindxtrain.train.sft import run_axolotl
+
+        return run_axolotl(cfg, plan, out_dir)
+    if backend == "unsloth":
+        from mindxtrain.train.backend_unsloth import run_unsloth
+
+        return run_unsloth(cfg, plan, out_dir)
+    if backend == "torchtune":
+        from mindxtrain.train.backend_torchtune import run_torchtune
+
+        return run_torchtune(cfg, plan, out_dir)
+    if backend == "primus":
+        from mindxtrain.train.backend_primus import run_primus
+
+        return run_primus(cfg, plan, out_dir)
+    if backend == "trl_cpu":
+        from mindxtrain.train.backend_trl_cpu import run_trl_cpu
+
+        return run_trl_cpu(cfg, plan, out_dir)
+    if backend == "trl_local":
+        from mindxtrain.train.backend_trl_cpu import run_trl_local
+
+        return run_trl_local(cfg, plan, out_dir)
+    msg = f"unknown backend {backend!r}"
+    raise ValueError(msg)
diff --git a/mindxtrain/train/distributed.py b/mindxtrain/train/distributed.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea1aef7c1821e15c340e49b980d7ba4e6d419622
--- /dev/null
+++ b/mindxtrain/train/distributed.py
@@ -0,0 +1,88 @@
+"""Distributed-training config builders — Accelerate FSDP + DeepSpeed ZeRO.
+
+Pure-Python: returns plain dicts that the caller writes to YAML/JSON for
+`accelerate launch --config_file ` or
+`deepspeed --deepspeed_config `.
+
+Hard invariant: MI300X xGMI permits only 1- or 8-GPU FSDP. The 2/4-GPU
+configurations have a known bandwidth bug; this module rejects them.
+"""
+
+from __future__ import annotations
+
+from typing import Literal
+
+
+def build_fsdp_config(
+    num_gpus: Literal[1, 8],
+    *,
+    shard_size: Literal["FULL_SHARD", "SHARD_GRAD_OP", "NO_SHARD"] = "FULL_SHARD",
+    transformer_layer_class: str = "Qwen3DecoderLayer",
+    mixed_precision: Literal["no", "fp16", "bf16"] = "bf16",
+) -> dict[str, object]:
+    """Return an Accelerate FSDP config dict for `num_gpus`."""
+    if num_gpus not in (1, 8):
+        msg = f"MI300X xGMI permits only 1 or 8 GPUs; got {num_gpus}."
+        raise ValueError(msg)
+    return {
+        "compute_environment": "LOCAL_MACHINE",
+        "distributed_type": "FSDP" if num_gpus > 1 else "NO",
+        "downcast_bf16": "no",
+        "machine_rank": 0,
+        "main_training_function": "main",
+        "mixed_precision": mixed_precision,
+        "num_machines": 1,
+        "num_processes": num_gpus,
+        "rdzv_backend": "static",
+        "same_network": True,
+        "tpu_env": [],
+        "tpu_use_cluster": False,
+        "tpu_use_sudo": False,
+        "use_cpu": False,
+        "fsdp_config": {
+            "fsdp_auto_wrap_policy": "TRANSFORMER_BASED_WRAP",
+            "fsdp_backward_prefetch_policy": "BACKWARD_PRE",
+            "fsdp_forward_prefetch": False,
+            "fsdp_offload_params": False,
+            "fsdp_sharding_strategy": shard_size,
+            "fsdp_state_dict_type": "FULL_STATE_DICT",
+            "fsdp_sync_module_states": True,
+            "fsdp_transformer_layer_cls_to_wrap": transformer_layer_class,
+            "fsdp_use_orig_params": True,
+        },
+    }
+
+
+def build_deepspeed_config(
+    *,
+    zero_stage: Literal[1, 2, 3] = 3,
+    offload_optimizer: bool = False,
+    offload_param: bool = False,
+    overlap_comm: bool = True,
+) -> dict[str, object]:
+    """Return a DeepSpeed ZeRO config dict at the given stage."""
+    if zero_stage not in (1, 2, 3):
+        msg = f"zero_stage must be 1/2/3; got {zero_stage}"
+        raise ValueError(msg)
+    return {
+        "bf16": {"enabled": True},
+        "zero_optimization": {
+            "stage": zero_stage,
+            "offload_optimizer": {"device": "cpu" if offload_optimizer else "none"},
+            "offload_param": {"device": "cpu" if offload_param else "none"},
+            "overlap_comm": overlap_comm,
+            "contiguous_gradients": True,
+            "reduce_bucket_size": "auto",
+            "stage3_prefetch_bucket_size": "auto",
+            "stage3_param_persistence_threshold": "auto",
+        },
+        "gradient_accumulation_steps": "auto",
+        "gradient_clipping": "auto",
+        "steps_per_print": 100,
+        "train_batch_size": "auto",
+        "train_micro_batch_size_per_gpu": "auto",
+        "wall_clock_breakdown": False,
+    }
+
+
+__all__ = ["build_deepspeed_config", "build_fsdp_config"]
diff --git a/mindxtrain/train/dpo.py b/mindxtrain/train/dpo.py
new file mode 100644
index 0000000000000000000000000000000000000000..a25a75e9cd8155eae5bb53b2b1d701e585e960d0
--- /dev/null
+++ b/mindxtrain/train/dpo.py
@@ -0,0 +1,60 @@
+"""DPO trainer — TRL DPOTrainer wrapper.
+
+Lazy `import trl` so users without `--extra ml` get a clean error.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+from mindxtrain.config.schema import XTrainConfig
+
+
+def run_dpo(cfg: XTrainConfig, out_dir: Path) -> Path:
+    """Run a DPO fine-tune; return the checkpoint directory."""
+    try:
+        from datasets import load_dataset
+        from transformers import AutoModelForCausalLM, AutoTokenizer
+        from trl import DPOConfig, DPOTrainer
+    except ImportError as exc:
+        msg = "TRL stack not installed; run `uv sync --extra ml`."
+        raise RuntimeError(msg) from exc
+
+    method: Any = cfg.train.method
+    if getattr(method, "kind", "") != "dpo":
+        msg = f"run_dpo expects train.method.kind == 'dpo'; got {getattr(method, 'kind', None)!r}"
+        raise ValueError(msg)
+
+    out_dir = Path(out_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    tokenizer = AutoTokenizer.from_pretrained(cfg.model.name)
+    model = AutoModelForCausalLM.from_pretrained(cfg.model.name)
+    ref_model = AutoModelForCausalLM.from_pretrained(cfg.model.name)
+
+    train_ds = load_dataset(cfg.data.hf_id, split=getattr(cfg.data, "split", "train"))
+
+    dpo_cfg = DPOConfig(
+        output_dir=str(out_dir),
+        beta=getattr(method, "beta", 0.1),
+        learning_rate=cfg.train.optim.learning_rate,
+        per_device_train_batch_size=cfg.train.micro_batch_size,
+        gradient_accumulation_steps=cfg.train.gradient_accumulation_steps,
+        num_train_epochs=cfg.train.num_epochs,
+        max_length=cfg.data.seq_len,
+        logging_steps=10,
+    )
+    trainer = DPOTrainer(
+        model=model,
+        ref_model=ref_model,
+        args=dpo_cfg,
+        train_dataset=train_ds,
+        processing_class=tokenizer,
+    )
+    trainer.train()
+    trainer.save_model(str(out_dir))
+    return out_dir
+
+
+__all__ = ["run_dpo"]
diff --git a/mindxtrain/train/grpo.py b/mindxtrain/train/grpo.py
new file mode 100644
index 0000000000000000000000000000000000000000..98b6f98b64c017dc2ac95d00a8bbd920bf4cb516
--- /dev/null
+++ b/mindxtrain/train/grpo.py
@@ -0,0 +1,59 @@
+"""GRPO trainer — TRL GRPOTrainer wrapper.
+
+Lazy `import trl` so users without `--extra ml` get a clean error.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+from mindxtrain.config.schema import XTrainConfig
+
+
+def run_grpo(cfg: XTrainConfig, out_dir: Path) -> Path:
+    """Run a GRPO fine-tune; return the checkpoint directory."""
+    try:
+        from datasets import load_dataset
+        from transformers import AutoModelForCausalLM, AutoTokenizer
+        from trl import GRPOConfig, GRPOTrainer
+    except ImportError as exc:
+        msg = "TRL stack not installed; run `uv sync --extra ml`."
+        raise RuntimeError(msg) from exc
+
+    method: Any = cfg.train.method
+    if getattr(method, "kind", "") != "grpo":
+        msg = f"run_grpo expects train.method.kind == 'grpo'; got {getattr(method, 'kind', None)!r}"
+        raise ValueError(msg)
+
+    out_dir = Path(out_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    tokenizer = AutoTokenizer.from_pretrained(cfg.model.name)
+    model = AutoModelForCausalLM.from_pretrained(cfg.model.name)
+
+    train_ds = load_dataset(cfg.data.hf_id, split=getattr(cfg.data, "split", "train"))
+
+    grpo_cfg = GRPOConfig(
+        output_dir=str(out_dir),
+        learning_rate=cfg.train.optim.learning_rate,
+        per_device_train_batch_size=cfg.train.micro_batch_size,
+        gradient_accumulation_steps=cfg.train.gradient_accumulation_steps,
+        num_train_epochs=cfg.train.num_epochs,
+        max_completion_length=cfg.data.seq_len,
+        num_generations=getattr(method, "num_generations", 4),
+        logging_steps=10,
+    )
+    trainer = GRPOTrainer(
+        model=model,
+        args=grpo_cfg,
+        train_dataset=train_ds,
+        processing_class=tokenizer,
+        reward_funcs=[lambda **_: 0.0],  # caller wires real reward
+    )
+    trainer.train()
+    trainer.save_model(str(out_dir))
+    return out_dir
+
+
+__all__ = ["run_grpo"]
diff --git a/mindxtrain/train/recipes/__init__.py b/mindxtrain/train/recipes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/mindxtrain/train/recipes/instella_3b_lora.yaml b/mindxtrain/train/recipes/instella_3b_lora.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f81010b4a1fd150528e3644941a7dcd5c1749940
--- /dev/null
+++ b/mindxtrain/train/recipes/instella_3b_lora.yaml
@@ -0,0 +1,67 @@
+# SPDX-License-Identifier: Apache-2.0
+# Secondary track: amd/Instella-3B-Instruct LoRA on tatsu-lab/alpaca[:5000].
+# AMD-on-AMD narrative; runs in ~30 min on a single MI300X for ~$1 of credit.
+meta:
+  project: mindxtrain_demo
+  run_name: instella_3b_alpaca_lora
+  seed: 2048
+  license: apache-2.0
+
+hardware:
+  name: mi300x
+  gfx_arch: gfx942
+  gpus: 1
+  expected_hbm_gb: 192
+
+autotune:
+  enabled: true
+  budget_seconds: 60
+  policy: aot_only
+
+model:
+  name: amd/Instella-3B-Instruct
+  attn_implementation: flash_attention_2
+  torch_dtype: bfloat16
+
+data:
+  source: hf
+  hf_id: tatsu-lab/alpaca
+  split: train
+  streaming: true
+  max_samples: 5000
+  seq_len: 2048
+  packing: true
+
+train:
+  backend: axolotl
+  method:
+    kind: lora
+    r: 16
+    alpha: 32
+    dropout: 0.05
+    target_modules: [q_proj, k_proj, v_proj, o_proj]
+  optimizer:
+    name: adamw_torch_fused
+    lr: 2.0e-4
+  schedule:
+    type: cosine
+    warmup_ratio: 0.03
+    epochs: 3
+  batch:
+    per_device: 4
+    grad_accum: 4
+  precision: bfloat16
+  gradient_checkpointing: true
+  fsdp:
+    enabled: false
+
+quantize:
+  enabled: true
+  scheme: quark_fp8
+  ptpc: true
+
+serve:
+  backend: vllm-rocm
+  reasoning_parser: none
+  tool_call_parser: none
+  max_model_len: 4096
diff --git a/mindxtrain/train/recipes/mindx_fallback_qwen3_1_5b_cpu_real.yaml b/mindxtrain/train/recipes/mindx_fallback_qwen3_1_5b_cpu_real.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1232ec030801135ac8aad806926e9597429f9acc
--- /dev/null
+++ b/mindxtrain/train/recipes/mindx_fallback_qwen3_1_5b_cpu_real.yaml
@@ -0,0 +1,133 @@
+# SPDX-License-Identifier: Apache-2.0
+# CPU "real" variant of the mindX local-fallback recipe.
+#
+# Purpose: actually adapt the base model to the mindX dream corpus on a
+# CPU-only laptop. Unlike the `_smoke` recipe (which produces a real
+# checkpoint but on too few samples to learn), this one trains long
+# enough to move loss measurably and (via eval_split) lets you see if
+# the adapter is generalising.
+#
+# Wall time: ~1.5–3 hr on a 4-core Ryzen at cpu_throttle.percent=50.
+# Override with `mindxtrain train ... --cpu-percent N` if you want to
+# give it more or fewer cores.
+#
+# Verify after with: `mindxtrain eval-checkpoint ` →
+# the trained adapter should report `adapter_loss < base_loss` on
+# the held-out dream slice.
+meta:
+  project: mindx_self_training
+  run_name: mindx_fallback_qwen3_1_5b_cpu_real
+  seed: 2048
+  license: apache-2.0
+  description: >-
+    Full CPU fine-tune of SmolLM2-135M on the mindX dream + evolution
+    corpus. Produces the local-fallback adapter mindX will swap in via
+    PATCH /v1/config/fallback-model when --register-as-fallback is set.
+
+hardware:
+  name: mi300x
+  gfx_arch: gfx942
+  gpus: 0
+  expected_hbm_gb: 192
+
+autotune:
+  enabled: false
+  budget_seconds: 60
+  policy: aot_only
+
+model:
+  name: HuggingFaceTB/SmolLM2-135M
+  attn_implementation: eager
+  torch_dtype: float32
+  trust_remote_code: false
+
+data:
+  source: mindx_dreams
+  path: /home/hacker/mindX/data/memory
+  # 512 samples × seq_len 512 with packing = ~64 packed sequences; 2
+  # epochs at effective batch 8 ≈ 16 optimizer steps. Plenty for the
+  # adapter to learn the dream distribution; tractable on a 6 GB Ryzen.
+  max_samples: 512
+  seq_len: 512
+  packing: true
+  # Hold out 10% (~50 examples) for validation. The trl_cpu backend
+  # splits deterministically by meta.seed and emits eval_loss every
+  # max_steps//4 steps so the Coach loss chart shows train + eval.
+  eval_split: 0.1
+  # Also pull *_evolutions.jsonl from mindX phase 5c, not just the
+  # phase-5b consolidation rows — gives the model both flavours of
+  # mindX output to learn from.
+  include_evolutions: true
+
+train:
+  backend: trl_cpu
+  logging_steps: 1
+  method:
+    kind: lora
+    # Bigger rank than smoke (r=8) so the adapter has capacity to
+    # actually capture dream phrasing patterns. Still small enough to
+    # fit in RAM with float32 activations.
+    r: 16
+    alpha: 32
+    dropout: 0.0
+    target_modules: [q_proj, k_proj, v_proj, o_proj]
+  optimizer:
+    name: adamw_torch
+    lr: 1.0e-4
+  schedule:
+    type: cosine
+    warmup_ratio: 0.03
+    epochs: 2
+  batch:
+    # Effective batch = per_device * grad_accum = 8. Keeps per-step
+    # RAM low (one sample at a time forward/back) while still giving
+    # the optimizer a reasonable batch-averaged gradient.
+    per_device: 1
+    grad_accum: 8
+  precision: float32
+  gradient_checkpointing: false
+  flash_attention:
+    backend: ck
+  fsdp:
+    enabled: false
+  cpu_throttle:
+    # Half the host's cores by default. Bigger than smoke's 50 because
+    # the real run is the one the user wants to throw cycles at.
+    # Override at runtime with `--cpu-percent N`.
+    percent: 50
+    nice_level: 10
+    omp_proc_bind: true
+  env:
+    PYTORCH_ROCM_ARCH: "gfx942"
+    HSA_NO_SCRATCH_RECLAIM: "1"
+    HIP_FORCE_DEV_KERNARG: "1"
+
+eval:
+  harness:
+    tasks: [mmlu]
+    fewshot: 0
+  regression:
+    baseline: ""
+    threshold_pct: -100.0
+
+quantize:
+  enabled: false
+  scheme: none
+  ptpc: false
+
+serve:
+  backend: vllm-rocm
+  reasoning_parser: none
+  tool_call_parser: none
+  tensor_parallel: 1
+  max_model_len: 2048
+  port: 8000
+
+publish:
+  enabled: false
+
+receipt:
+  output: ./out/receipt.json
+  include:
+    - yaml_hash
+    - dataset_cids
diff --git a/mindxtrain/train/recipes/mindx_fallback_qwen3_1_5b_cpu_smoke.yaml b/mindxtrain/train/recipes/mindx_fallback_qwen3_1_5b_cpu_smoke.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b88a7afcc79fc3d27491dab6911dbe4a142a6826
--- /dev/null
+++ b/mindxtrain/train/recipes/mindx_fallback_qwen3_1_5b_cpu_smoke.yaml
@@ -0,0 +1,117 @@
+# SPDX-License-Identifier: Apache-2.0
+# CPU smoke variant of the mindX local-fallback recipe.
+#
+# Purpose: verify the dream-corpus → tokenize → SFT → checkpoint → manifest
+# loop closes on a CPU-only laptop before burning AMD credits on the real
+# MI300X run. Same dataset, much smaller base, shorter sequence, one epoch.
+#
+# Wall time: 10-30 min depending on host. Produces a real (low-quality) HF-
+# format checkpoint suitable for `mindxtrain receipt` round-trip.
+meta:
+  project: mindx_self_training
+  run_name: mindx_fallback_qwen3_1_5b_cpu_smoke
+  seed: 2048
+  license: apache-2.0
+  description: >-
+    CPU smoke run of the dream-corpus loop. Not for production serving;
+    exists to keep the end-to-end pipeline green without GPU.
+
+hardware:
+  name: mi300x
+  gfx_arch: gfx942
+  gpus: 0
+  expected_hbm_gb: 192
+
+autotune:
+  enabled: false
+  budget_seconds: 60
+  policy: aot_only
+
+model:
+  name: HuggingFaceTB/SmolLM2-135M
+  attn_implementation: eager
+  torch_dtype: float32
+  trust_remote_code: false
+
+data:
+  source: mindx_dreams
+  path: /home/hacker/mindX/data/memory
+  # Conservative defaults sized to fit a 4-core Ryzen laptop with 6 GB RAM.
+  # Override with `mindxtrain train --cpu-percent N` and a recipe edit for
+  # bigger smoke runs on hosts with more headroom. Total RSS observed
+  # ~1.2 GB at these settings on SmolLM2-135M (float32 + LoRA + tokenized
+  # dataset + torch overhead).
+  max_samples: 32
+  seq_len: 256
+  packing: false
+
+train:
+  backend: trl_cpu
+  # Log every step so the smoke run produces a real (small) loss curve
+  # instead of trainer_state.json -> log_history: [].
+  logging_steps: 1
+  method:
+    kind: lora
+    r: 8
+    alpha: 16
+    dropout: 0.0
+    target_modules: [q_proj, k_proj, v_proj, o_proj]
+  optimizer:
+    name: adamw_torch
+    lr: 1.0e-4
+  schedule:
+    type: cosine
+    warmup_ratio: 0.03
+    epochs: 1
+  batch:
+    per_device: 1
+    grad_accum: 4
+  precision: float32
+  gradient_checkpointing: false
+  flash_attention:
+    backend: ck
+  fsdp:
+    enabled: false
+  cpu_throttle:
+    # 25% of the host's cores, de-prioritised. On a 4-core Ryzen laptop
+    # `resolve_thread_count(25, 4)` floors to 1 thread — one core fully
+    # utilised, the other three left free so the rest of the system
+    # (and the Coach UI streaming this run) stays responsive. Override
+    # at runtime with `mindxtrain train ... --cpu-percent N --cpu-nice M`.
+    percent: 25
+    nice_level: 10
+    omp_proc_bind: true
+  env:
+    PYTORCH_ROCM_ARCH: "gfx942"
+    HSA_NO_SCRATCH_RECLAIM: "1"
+    HIP_FORCE_DEV_KERNARG: "1"
+
+eval:
+  harness:
+    tasks: [mmlu]
+    fewshot: 0
+  regression:
+    baseline: ""
+    threshold_pct: -100.0
+
+quantize:
+  enabled: false
+  scheme: none
+  ptpc: false
+
+serve:
+  backend: vllm-rocm
+  reasoning_parser: none
+  tool_call_parser: none
+  tensor_parallel: 1
+  max_model_len: 2048
+  port: 8000
+
+publish:
+  enabled: false
+
+receipt:
+  output: ./out/receipt.json
+  include:
+    - yaml_hash
+    - dataset_cids
diff --git a/mindxtrain/train/recipes/mindx_fallback_qwen3_1_5b_local.yaml b/mindxtrain/train/recipes/mindx_fallback_qwen3_1_5b_local.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ed5bb0e22a32b4a0c8e9334204cd958ed8626f60
--- /dev/null
+++ b/mindxtrain/train/recipes/mindx_fallback_qwen3_1_5b_local.yaml
@@ -0,0 +1,118 @@
+# SPDX-License-Identifier: Apache-2.0
+# Local-GPU variant of the mindX self-training recipe.
+#
+# Purpose: train on a consumer-level accelerator when one is present. The
+# `trl_local` backend is device-aware — it uses a local GPU (NVIDIA CUDA or a
+# ROCm-supported Radeon, bf16/fp16) when one is visible, and falls back to CPU
+# (float32) otherwise. The SAME recipe therefore runs on a gaming desktop, a
+# ROCm workstation, or a CPU-only laptop unchanged.
+#
+# Note: integrated Vega/RDNA APUs (e.g. Ryzen "Raven"/gfx90c) are NOT supported
+# by ROCm and will fall back to CPU. A discrete RX 6800/7900 (gfx1030/gfx1100)
+# or any NVIDIA RTX card is the intended GPU target.
+#
+# Confirm the device that will be used:
+#   python -c "import torch; print(torch.cuda.is_available(), torch.version.hip)"
+meta:
+  project: mindx_self_training
+  run_name: mindx_fallback_qwen3_1_5b_local
+  seed: 2048
+  license: apache-2.0
+  description: >-
+    Device-aware fine-tune of SmolLM2-135M on the mindX dream + evolution
+    corpus. Uses a local consumer GPU when available, else CPU. Produces the
+    same local-fallback adapter as the CPU recipe.
+
+hardware:
+  name: consumer_gpu
+  gfx_arch: gfx1100        # representative discrete Radeon (RX 7900); descriptive only
+  gpus: 1
+  expected_hbm_gb: 16      # typical consumer VRAM; not used to gate the in-process lane
+
+autotune:
+  enabled: false
+  budget_seconds: 60
+  policy: aot_only
+
+model:
+  name: HuggingFaceTB/SmolLM2-135M
+  # On GPU the backend overrides these with sdpa + bf16/fp16; on CPU fallback
+  # it uses eager + float32. The recipe values are the CPU-safe baseline.
+  attn_implementation: eager
+  torch_dtype: float32
+  trust_remote_code: false
+
+data:
+  source: mindx_dreams
+  path: /home/hacker/mindX/data/memory
+  max_samples: 512
+  seq_len: 512
+  packing: true
+  eval_split: 0.1
+  include_evolutions: true
+
+train:
+  backend: trl_local
+  logging_steps: 1
+  method:
+    kind: lora
+    r: 16
+    alpha: 32
+    dropout: 0.0
+    target_modules: [q_proj, k_proj, v_proj, o_proj]
+  optimizer:
+    name: adamw_torch
+    lr: 1.0e-4
+  schedule:
+    type: cosine
+    warmup_ratio: 0.03
+    epochs: 2
+  batch:
+    # On a GPU the per-device batch runs uncapped; on CPU fallback the backend
+    # clamps it to 2 automatically. grad_accum keeps the effective batch steady.
+    per_device: 4
+    grad_accum: 2
+  precision: bfloat16
+  # On GPU this is honoured (trades compute for memory); on CPU fallback the
+  # backend forces it off (checkpointing is pathologically slow on CPU).
+  gradient_checkpointing: true
+  flash_attention:
+    backend: ck
+  fsdp:
+    enabled: false
+  # cpu_throttle still applies on CPU fallback; ignored on the GPU path.
+  cpu_throttle:
+    percent: 50
+    nice_level: 10
+    omp_proc_bind: true
+  env: {}
+
+eval:
+  harness:
+    tasks: [mmlu]
+    fewshot: 0
+  regression:
+    baseline: ""
+    threshold_pct: -100.0
+
+quantize:
+  enabled: false
+  scheme: none
+  ptpc: false
+
+serve:
+  backend: vllm-rocm
+  reasoning_parser: none
+  tool_call_parser: none
+  tensor_parallel: 1
+  max_model_len: 2048
+  port: 8000
+
+publish:
+  enabled: false
+
+receipt:
+  output: ./out/receipt.json
+  include:
+    - yaml_hash
+    - dataset_cids
diff --git a/mindxtrain/train/recipes/mindx_fallback_qwen3_1_5b_sft_lora.yaml b/mindxtrain/train/recipes/mindx_fallback_qwen3_1_5b_sft_lora.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f56b7c830e3c42f96ec62babb4aa8e3f0aca327e
--- /dev/null
+++ b/mindxtrain/train/recipes/mindx_fallback_qwen3_1_5b_sft_lora.yaml
@@ -0,0 +1,146 @@
+# SPDX-License-Identifier: Apache-2.0
+# mindX local-fallback recipe — Qwen3-1.5B LoRA on the dream-cycle corpus.
+#
+# The closed loop:
+#   mindX's machine_dreaming agent emits per-agent JSONL training data on every
+#   ~8h dream cycle. This recipe pulls that corpus directly off disk, runs a
+#   LoRA on Qwen3-1.5B on a single MI300X (~3-5 GPU-hrs for one epoch over
+#   ~2k examples), and publishes the result to HF Hub. The published model is
+#   then registered with mindX as a local fallback for when frontier APIs are
+#   unreachable.
+#
+# Burn estimate: ~$10-15 of the $100 AMD credit envelope. Smoke-test first with
+# `mindx_fallback_qwen3_1_5b_cpu_smoke.yaml` to verify the pipeline closes locally.
+meta:
+  project: mindx_self_training
+  run_name: mindx_fallback_qwen3_1_5b
+  seed: 2048
+  license: apache-2.0
+  description: >-
+    LoRA fine-tune of Qwen3-1.5B on mindX dream-cycle insights, intended as
+    the local-fallback model when mindX cannot reach a frontier API.
+
+hardware:
+  name: mi300x
+  gfx_arch: gfx942
+  gpus: 1
+  expected_hbm_gb: 192
+
+autotune:
+  enabled: true
+  plan_path: ./out/mindxtrain.tuned.yaml
+  budget_seconds: 60
+  policy: aot_only
+
+model:
+  name: Qwen/Qwen3-1.5B
+  attn_implementation: flash_attention_2
+  torch_dtype: bfloat16
+  trust_remote_code: false
+
+data:
+  source: mindx_dreams
+  path: /home/hacker/mindX/data/memory
+  # Pull both *_training.jsonl (consolidation) and *_evolutions.jsonl
+  # (proposals) from each dream cycle so the fallback model learns BOTH
+  # the STM → insight distillation AND the insights → evolution mapping.
+  include_evolutions: true
+  seq_len: 2048
+  packing: true
+  dedupe:
+    minhash:
+      threshold: 0.85
+  shard:
+    num_shards: 1
+
+train:
+  backend: axolotl
+  method:
+    kind: lora
+    r: 16
+    alpha: 32
+    dropout: 0.05
+    target_modules: [q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj]
+  optimizer:
+    name: adamw_torch_fused
+    lr: 1.0e-4
+    betas: [0.9, 0.95]
+    weight_decay: 0.1
+    grad_clip: 1.0
+  schedule:
+    type: cosine
+    warmup_ratio: 0.03
+    epochs: 3
+  batch:
+    per_device: 8
+    grad_accum: 4
+  precision: bfloat16
+  gradient_checkpointing: true
+  flash_attention:
+    backend: ck
+  fsdp:
+    enabled: false
+  env:
+    HSA_NO_SCRATCH_RECLAIM: "1"
+    NVTE_CK_USES_BWD_V3: "1"
+    NVTE_CK_IS_V3_ATOMIC_FP32: "1"
+    PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32: "1"
+    NCCL_MIN_NCHANNELS: "112"
+    HIP_FORCE_DEV_KERNARG: "1"
+    PYTORCH_ROCM_ARCH: "gfx942"
+
+eval:
+  harness:
+    tasks: [mmlu, gsm8k, ifeval]
+    fewshot: 5
+  regression:
+    baseline: Qwen/Qwen3-1.5B
+    threshold_pct: -2.0
+
+quantize:
+  enabled: true
+  scheme: quark_fp8
+  ptpc: true
+
+serve:
+  backend: vllm-rocm
+  reasoning_parser: qwen3
+  tool_call_parser: hermes
+  tensor_parallel: 1
+  max_model_len: 4096
+  port: 8000
+
+publish:
+  enabled: true
+  hf:
+    repo: pythai/mindx-fallback-qwen3-1.5b
+    private: false
+  lighthouse:
+    api_key_env: LIGHTHOUSE_API_KEY
+  mindx:
+    api_url: https://mindx.pythai.net/v1/agents
+    register_as_capability: true
+  agenticplace:
+    api_url: https://agenticplace.pythai.net/v1/listings
+    chain_map_url: https://agenticplace.pythai.net/allchain.html
+  bankon:
+    ens_parent: bankon.eth
+    subname: mindx-fallback-qwen3-1.5b
+  billing:
+    x402:
+      network: algorand
+      asset: USDC
+      receiver_via: parsec_wallet
+      price_per_1k_tokens: 0.0002
+
+receipt:
+  output: ./out/receipt.json
+  include:
+    - rocm_version
+    - gfx_arch
+    - container_digest
+    - all_git_shas
+    - yaml_hash
+    - dataset_cids
+    - eval_report
+    - energy_kwh
diff --git a/mindxtrain/train/recipes/mindx_persona_imprint_local.yaml b/mindxtrain/train/recipes/mindx_persona_imprint_local.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..447c432ad4c102b31472b46e8a349b96e9cad691
--- /dev/null
+++ b/mindxtrain/train/recipes/mindx_persona_imprint_local.yaml
@@ -0,0 +1,115 @@
+# SPDX-License-Identifier: Apache-2.0
+# Persona-imprint recipe — the minimal "production test 1" loop.
+#
+# Model = actor. Persona = identity/voice. Script = the local JSONL you author
+# (in Coach: "Create script", or via mindxtrain.data.scripts.author_script).
+# Training imprints the persona onto the tiny actor; measure the imprint with
+# `mindxtrain.eval.imprint` (recall before vs after).
+#
+# Author a script first, then point this recipe at it:
+#   - Coach "Create script" writes ./out/datasets//script.jsonl
+#   - set data.path below to that file (or a directory of *.jsonl)
+#
+# Device-aware (trl_local): uses a local GPU if present, else CPU. Tiny by
+# design so a laptop can imprint in minutes — this is a smoke/production test,
+# not a full fine-tune.
+meta:
+  project: mindx_persona_imprint
+  run_name: mindx_persona_imprint_local
+  seed: 2048
+  license: apache-2.0
+  description: >-
+    Imprint a persona onto SmolLM2-135M from a hand-authored local script.
+    The first end-to-end production test of the create-dataset → imprint →
+    measure loop.
+
+hardware:
+  name: consumer_gpu
+  gfx_arch: gfx1100
+  gpus: 1
+  expected_hbm_gb: 16
+
+autotune:
+  enabled: false
+  budget_seconds: 60
+  policy: aot_only
+
+model:
+  name: HuggingFaceTB/SmolLM2-135M
+  attn_implementation: eager
+  torch_dtype: float32
+  trust_remote_code: false
+
+data:
+  source: local
+  # Point at the script Coach authored (file or directory of *.jsonl).
+  path: ./out/datasets/codephreak/script.jsonl
+  max_samples: 64
+  seq_len: 256
+  packing: false
+  eval_split: null
+
+train:
+  backend: trl_local
+  logging_steps: 1
+  method:
+    kind: lora
+    # Imprinting a persona onto a tiny actor is deliberate memorization — a
+    # bigger rank + more epochs + grad_accum 1 (so a few-row script still does
+    # many optimizer steps) is what makes the imprint actually take and register
+    # in the recall measurement.
+    r: 16
+    alpha: 32
+    dropout: 0.0
+    target_modules: [q_proj, k_proj, v_proj, o_proj]
+  optimizer:
+    name: adamw_torch
+    lr: 3.0e-4
+  schedule:
+    type: cosine
+    warmup_ratio: 0.03
+    epochs: 12
+  batch:
+    per_device: 1
+    grad_accum: 1
+  precision: bfloat16
+  gradient_checkpointing: false
+  flash_attention:
+    backend: ck
+  fsdp:
+    enabled: false
+  cpu_throttle:
+    percent: 50
+    nice_level: 10
+    omp_proc_bind: true
+  env: {}
+
+eval:
+  harness:
+    tasks: [mmlu]
+    fewshot: 0
+  regression:
+    baseline: ""
+    threshold_pct: -100.0
+
+quantize:
+  enabled: false
+  scheme: none
+  ptpc: false
+
+serve:
+  backend: vllm-rocm
+  reasoning_parser: none
+  tool_call_parser: none
+  tensor_parallel: 1
+  max_model_len: 2048
+  port: 8000
+
+publish:
+  enabled: false
+
+receipt:
+  output: ./out/receipt.json
+  include:
+    - yaml_hash
+    - dataset_cids
diff --git a/mindxtrain/train/recipes/qwen3_30b_a3b_lora.yaml b/mindxtrain/train/recipes/qwen3_30b_a3b_lora.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..60bee799553be8e258d4973521f0aa154d8121e7
--- /dev/null
+++ b/mindxtrain/train/recipes/qwen3_30b_a3b_lora.yaml
@@ -0,0 +1,52 @@
+# SPDX-License-Identifier: Apache-2.0
+# Qwen3-30B-A3B (MoE) LoRA. Experts-only adapters, gate frozen — non-negotiable rule.
+meta:
+  project: mindxtrain_demo
+  run_name: qwen3_30b_a3b_lora
+  seed: 2048
+
+hardware:
+  name: mi300x
+  gfx_arch: gfx942
+  gpus: 8
+
+autotune:
+  enabled: true
+  budget_seconds: 90  # MoE expert imbalance benefits from longer probe.
+
+model:
+  name: Qwen/Qwen3-30B-A3B
+  attn_implementation: flash_attention_2
+  torch_dtype: bfloat16
+  trust_remote_code: false
+
+data:
+  source: hf
+  hf_id: HuggingFaceH4/ultrachat_200k
+  split: train_sft
+  seq_len: 4096
+  packing: true
+
+train:
+  backend: axolotl
+  method:
+    kind: lora
+    r: 32
+    alpha: 64
+    dropout: 0.0
+    # NOTE: experts only — gate frozen. up_proj/down_proj are per-expert in MoE layers.
+    target_modules: [q_proj, k_proj, v_proj, o_proj, up_proj, down_proj]
+  optimizer:
+    name: adamw_torch_fused
+    lr: 1.0e-4
+  schedule:
+    type: cosine
+    warmup_ratio: 0.03
+    epochs: 2
+  batch:
+    per_device: 2
+    grad_accum: 8
+  precision: bfloat16
+  gradient_checkpointing: true
+  fsdp:
+    enabled: true
diff --git a/mindxtrain/train/recipes/qwen3_32b_dpo.yaml b/mindxtrain/train/recipes/qwen3_32b_dpo.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9748ced4755dd8fea8a2d0c175e96504b259a39e
--- /dev/null
+++ b/mindxtrain/train/recipes/qwen3_32b_dpo.yaml
@@ -0,0 +1,44 @@
+# SPDX-License-Identifier: Apache-2.0
+# Qwen3-32B DPO preference optimization on 8x MI300X.
+meta:
+  project: mindxtrain_demo
+  run_name: qwen3_32b_dpo
+  seed: 2048
+
+hardware:
+  name: mi300x
+  gfx_arch: gfx942
+  gpus: 8
+
+autotune:
+  enabled: true
+
+model:
+  name: Qwen/Qwen3-32B
+  torch_dtype: bfloat16
+
+data:
+  source: hf
+  hf_id: HuggingFaceH4/ultrafeedback_binarized
+  split: train_prefs
+  seq_len: 4096
+  packing: false
+
+train:
+  backend: axolotl
+  method:
+    kind: dpo
+    beta: 0.1
+  optimizer:
+    name: adamw_torch_fused
+    lr: 5.0e-7
+  schedule:
+    type: cosine
+    warmup_ratio: 0.1
+    epochs: 1
+  batch:
+    per_device: 1
+    grad_accum: 8
+  precision: bfloat16
+  fsdp:
+    enabled: true
diff --git a/mindxtrain/train/recipes/qwen3_32b_full_fsdp.yaml b/mindxtrain/train/recipes/qwen3_32b_full_fsdp.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..31b62f246af9d9633b40108967ceb2690748ef46
--- /dev/null
+++ b/mindxtrain/train/recipes/qwen3_32b_full_fsdp.yaml
@@ -0,0 +1,65 @@
+# SPDX-License-Identifier: Apache-2.0
+# Qwen3-32B full FT on 8x MI300X with FSDP2. ~5-9 hours per 1B tokens at FP8 with TE-CK.
+# CRITICAL: 8 GPUs only — 2/4 GPU FSDP groups hit MI300X xGMI bandwidth asymmetry.
+meta:
+  project: mindxtrain_demo
+  run_name: qwen3_32b_full_fsdp
+  seed: 2048
+  license: apache-2.0
+
+hardware:
+  name: mi300x
+  gfx_arch: gfx942
+  gpus: 8
+  expected_hbm_gb: 192
+
+autotune:
+  enabled: true
+  budget_seconds: 90
+  policy: aot_only
+
+model:
+  name: Qwen/Qwen3-32B
+  attn_implementation: flash_attention_2
+  torch_dtype: bfloat16
+
+data:
+  source: hf
+  hf_id: HuggingFaceH4/ultrachat_200k
+  split: train_sft
+  seq_len: 4096
+  packing: true
+
+train:
+  backend: primus
+  method:
+    kind: full
+  optimizer:
+    name: adamw_torch_fused
+    lr: 3.0e-6
+    betas: [0.9, 0.95]
+    weight_decay: 0.1
+    grad_clip: 1.0
+  schedule:
+    type: cosine
+    warmup_ratio: 0.03
+    epochs: 1
+  batch:
+    per_device: 2
+    grad_accum: 4
+  precision: bfloat16
+  gradient_checkpointing: true
+  flash_attention:
+    backend: ck
+  fsdp:
+    enabled: true
+    auto_wrap: true
+  env:
+    HSA_NO_SCRATCH_RECLAIM: "1"
+    NVTE_CK_USES_BWD_V3: "1"
+    NVTE_CK_IS_V3_ATOMIC_FP32: "1"
+    PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32: "1"
+    NCCL_MIN_NCHANNELS: "112"
+    HIP_FORCE_DEV_KERNARG: "1"
+    PYTORCH_ROCM_ARCH: "gfx942"
+    GPU_MAX_HW_QUEUES: "1"
diff --git a/mindxtrain/train/recipes/qwen3_32b_grpo.yaml b/mindxtrain/train/recipes/qwen3_32b_grpo.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5016143b57af1f24814fc03273f8b36b054c4429
--- /dev/null
+++ b/mindxtrain/train/recipes/qwen3_32b_grpo.yaml
@@ -0,0 +1,44 @@
+# SPDX-License-Identifier: Apache-2.0
+# Qwen3-32B GRPO on GSM8K. Mirrors AMD's published TRL+vLLM+DeepSpeed GRPO recipe.
+meta:
+  project: mindxtrain_demo
+  run_name: qwen3_32b_grpo_gsm8k
+  seed: 2048
+
+hardware:
+  name: mi300x
+  gfx_arch: gfx942
+  gpus: 8
+
+autotune:
+  enabled: true
+
+model:
+  name: Qwen/Qwen3-32B
+  torch_dtype: bfloat16
+
+data:
+  source: hf
+  hf_id: openai/gsm8k
+  split: train
+  seq_len: 2048
+
+train:
+  backend: axolotl
+  method:
+    kind: grpo
+    num_generations: 4
+    kl_coef: 0.04
+  optimizer:
+    name: adamw_torch_fused
+    lr: 5.0e-7
+  schedule:
+    type: cosine
+    warmup_ratio: 0.03
+    epochs: 1
+  batch:
+    per_device: 1
+    grad_accum: 8
+  precision: bfloat16
+  fsdp:
+    enabled: true
diff --git a/mindxtrain/train/recipes/qwen3_32b_orpo.yaml b/mindxtrain/train/recipes/qwen3_32b_orpo.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0ebfab9b4b05ca39f7e5dcadd7edb2143b5fa5fd
--- /dev/null
+++ b/mindxtrain/train/recipes/qwen3_32b_orpo.yaml
@@ -0,0 +1,43 @@
+# SPDX-License-Identifier: Apache-2.0
+# Qwen3-32B ORPO (Odds Ratio Preference Optimization) — single-stage SFT+RLHF.
+meta:
+  project: mindxtrain_demo
+  run_name: qwen3_32b_orpo
+  seed: 2048
+
+hardware:
+  name: mi300x
+  gfx_arch: gfx942
+  gpus: 8
+
+autotune:
+  enabled: true
+
+model:
+  name: Qwen/Qwen3-32B
+  torch_dtype: bfloat16
+
+data:
+  source: hf
+  hf_id: HuggingFaceH4/ultrafeedback_binarized
+  split: train_prefs
+  seq_len: 4096
+
+train:
+  backend: axolotl
+  method:
+    kind: orpo
+    beta: 0.1
+  optimizer:
+    name: adamw_torch_fused
+    lr: 5.0e-6
+  schedule:
+    type: cosine
+    warmup_ratio: 0.03
+    epochs: 1
+  batch:
+    per_device: 1
+    grad_accum: 8
+  precision: bfloat16
+  fsdp:
+    enabled: true
diff --git a/mindxtrain/train/recipes/qwen3_6_27b_lora.yaml b/mindxtrain/train/recipes/qwen3_6_27b_lora.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3871be3b10218e77d73fbb2d7f87960b2b722614
--- /dev/null
+++ b/mindxtrain/train/recipes/qwen3_6_27b_lora.yaml
@@ -0,0 +1,49 @@
+# SPDX-License-Identifier: Apache-2.0
+# Qwen3.6-27B (dense, released April 22 2026) LoRA on 8x MI300X.
+# 262,144 native context, default-thinking with `chat_template_kwargs={"enable_thinking": False}`.
+meta:
+  project: mindxtrain_demo
+  run_name: qwen3_6_27b_lora
+  seed: 2048
+
+hardware:
+  name: mi300x
+  gfx_arch: gfx942
+  gpus: 8
+
+autotune:
+  enabled: true
+
+model:
+  name: Qwen/Qwen3.6-27B
+  attn_implementation: flash_attention_2
+  torch_dtype: bfloat16
+
+data:
+  source: hf
+  hf_id: HuggingFaceH4/ultrachat_200k
+  split: train_sft
+  seq_len: 8192
+  packing: true
+
+train:
+  backend: axolotl
+  method:
+    kind: lora
+    r: 32
+    alpha: 64
+    target_modules: [q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj]
+  optimizer:
+    name: adamw_torch_fused
+    lr: 1.0e-4
+  schedule:
+    type: cosine
+    warmup_ratio: 0.03
+    epochs: 2
+  batch:
+    per_device: 2
+    grad_accum: 8
+  precision: bfloat16
+  gradient_checkpointing: true
+  fsdp:
+    enabled: true
diff --git a/mindxtrain/train/recipes/qwen3_6_35b_a3b_lora.yaml b/mindxtrain/train/recipes/qwen3_6_35b_a3b_lora.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ddba1f2507aeaa02158fba9aea325ae0095484f7
--- /dev/null
+++ b/mindxtrain/train/recipes/qwen3_6_35b_a3b_lora.yaml
@@ -0,0 +1,51 @@
+# SPDX-License-Identifier: Apache-2.0
+# Qwen3.6-35B-A3B MoE LoRA (latest open-weight checkpoint, released April 16 2026).
+# Hybrid Gated DeltaNet layers require flash-linear-attention + causal-conv1d wheels.
+meta:
+  project: mindxtrain_demo
+  run_name: qwen3_6_35b_a3b_lora
+  seed: 2048
+
+hardware:
+  name: mi300x
+  gfx_arch: gfx942
+  gpus: 8
+
+autotune:
+  enabled: true
+  budget_seconds: 120  # Hybrid GDN + MoE benefits from longer probe.
+
+model:
+  name: Qwen/Qwen3.6-35B-A3B
+  attn_implementation: flash_attention_2
+  torch_dtype: bfloat16
+
+data:
+  source: hf
+  hf_id: HuggingFaceH4/ultrachat_200k
+  split: train_sft
+  seq_len: 8192
+  packing: true
+
+train:
+  backend: axolotl
+  method:
+    kind: lora
+    r: 32
+    alpha: 64
+    # Experts only; gate frozen.
+    target_modules: [q_proj, k_proj, v_proj, o_proj, up_proj, down_proj]
+  optimizer:
+    name: adamw_torch_fused
+    lr: 1.0e-4
+  schedule:
+    type: cosine
+    warmup_ratio: 0.03
+    epochs: 2
+  batch:
+    per_device: 1
+    grad_accum: 16
+  precision: bfloat16
+  gradient_checkpointing: true
+  fsdp:
+    enabled: true
diff --git a/mindxtrain/train/recipes/qwen3_8b_cpt.yaml b/mindxtrain/train/recipes/qwen3_8b_cpt.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..efb02ab28ae7677bfd89d20b91734210c6363e5f
--- /dev/null
+++ b/mindxtrain/train/recipes/qwen3_8b_cpt.yaml
@@ -0,0 +1,44 @@
+# SPDX-License-Identifier: Apache-2.0
+# Qwen3-8B continued pretraining (CPT) on a domain corpus.
+meta:
+  project: mindxtrain_demo
+  run_name: qwen3_8b_cpt
+  seed: 2048
+
+hardware:
+  name: mi300x
+  gfx_arch: gfx942
+  gpus: 8
+
+autotune:
+  enabled: true
+
+model:
+  name: Qwen/Qwen3-8B
+  torch_dtype: bfloat16
+
+data:
+  source: hf
+  hf_id: HuggingFaceFW/fineweb-edu
+  split: train
+  streaming: true
+  seq_len: 8192
+  packing: true
+
+train:
+  backend: primus
+  method:
+    kind: cpt
+  optimizer:
+    name: adamw_torch_fused
+    lr: 1.0e-5
+  schedule:
+    type: wsd
+    warmup_ratio: 0.01
+    epochs: 1
+  batch:
+    per_device: 4
+    grad_accum: 4
+  precision: bfloat16
+  fsdp:
+    enabled: true
diff --git a/mindxtrain/train/recipes/qwen3_8b_sft_full.yaml b/mindxtrain/train/recipes/qwen3_8b_sft_full.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f13931922bd484ab7a5f5f7c71196be731f7498c
--- /dev/null
+++ b/mindxtrain/train/recipes/qwen3_8b_sft_full.yaml
@@ -0,0 +1,72 @@
+# SPDX-License-Identifier: Apache-2.0
+# Qwen3-8B full SFT on a single MI300X (no LoRA). 192 GB HBM3 makes this trivially possible.
+meta:
+  project: mindxtrain_demo
+  run_name: qwen3_8b_sft_full
+  seed: 2048
+  license: apache-2.0
+
+hardware:
+  name: mi300x
+  gfx_arch: gfx942
+  gpus: 1
+  expected_hbm_gb: 192
+
+autotune:
+  enabled: true
+  budget_seconds: 60
+  policy: aot_only
+
+model:
+  name: Qwen/Qwen3-8B
+  attn_implementation: flash_attention_2
+  torch_dtype: bfloat16
+
+data:
+  source: hf
+  hf_id: HuggingFaceH4/ultrachat_200k
+  split: train_sft
+  seq_len: 4096
+  packing: true
+
+train:
+  backend: axolotl
+  method:
+    kind: full
+  optimizer:
+    name: adamw_torch_fused
+    lr: 5.0e-6
+    betas: [0.9, 0.95]
+    weight_decay: 0.1
+    grad_clip: 1.0
+  schedule:
+    type: cosine
+    warmup_ratio: 0.03
+    epochs: 1
+  batch:
+    per_device: 4
+    grad_accum: 8
+  precision: bfloat16
+  gradient_checkpointing: true
+  flash_attention:
+    backend: ck
+  fsdp:
+    enabled: false
+
+eval:
+  harness:
+    tasks: [mmlu, gsm8k, ifeval, humaneval]
+    fewshot: 5
+  regression:
+    baseline: Qwen/Qwen3-8B
+    threshold_pct: -1.0
+
+quantize:
+  enabled: true
+  scheme: quark_fp8
+  ptpc: true
+
+serve:
+  backend: vllm-rocm
+  reasoning_parser: qwen3
+  tool_call_parser: hermes
diff --git a/mindxtrain/train/recipes/qwen3_8b_sft_lora.yaml b/mindxtrain/train/recipes/qwen3_8b_sft_lora.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d21855c83af9d4e142c5bebbb2988c6201d423a8
--- /dev/null
+++ b/mindxtrain/train/recipes/qwen3_8b_sft_lora.yaml
@@ -0,0 +1,134 @@
+# SPDX-License-Identifier: Apache-2.0
+# Hero recipe: Qwen3-8B SFT with LoRA on a single MI300X.
+# Fits one card with bs=8 seq=4096, BF16, AdamW; ~80 GB peak; 12-20k tok/s on Primus-Turbo.
+meta:
+  project: mindxtrain_demo
+  run_name: qwen3_8b_sft_lora
+  seed: 2048
+  license: apache-2.0
+
+hardware:
+  name: mi300x
+  gfx_arch: gfx942
+  gpus: 1
+  expected_hbm_gb: 192
+
+autotune:
+  enabled: true
+  plan_path: ./out/mindxtrain.tuned.yaml
+  budget_seconds: 60
+  policy: aot_only
+
+model:
+  name: Qwen/Qwen3-8B
+  attn_implementation: flash_attention_2
+  torch_dtype: bfloat16
+  trust_remote_code: false
+
+data:
+  source: hf
+  hf_id: HuggingFaceH4/ultrachat_200k
+  split: train_sft
+  seq_len: 4096
+  packing: true
+  dedupe:
+    minhash:
+      threshold: 0.85
+    semdedup:
+      threshold: 0.95
+      model: sentence-transformers/all-MiniLM-L6-v2
+  shard:
+    num_shards: 1
+
+train:
+  backend: axolotl
+  method:
+    kind: lora
+    r: 16
+    alpha: 32
+    dropout: 0.0
+    target_modules: [q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj]
+  optimizer:
+    name: adamw_torch_fused
+    lr: 1.0e-4
+    betas: [0.9, 0.95]
+    weight_decay: 0.1
+    grad_clip: 1.0
+  schedule:
+    type: cosine
+    warmup_ratio: 0.03
+    epochs: 3
+  batch:
+    per_device: 8
+    grad_accum: 4
+  precision: bfloat16
+  gradient_checkpointing: true
+  flash_attention:
+    backend: ck
+  fsdp:
+    enabled: false
+    auto_wrap: true
+  env:
+    HSA_NO_SCRATCH_RECLAIM: "1"
+    NVTE_CK_USES_BWD_V3: "1"
+    NVTE_CK_IS_V3_ATOMIC_FP32: "1"
+    PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32: "1"
+    NCCL_MIN_NCHANNELS: "112"
+    HIP_FORCE_DEV_KERNARG: "1"
+    PYTORCH_ROCM_ARCH: "gfx942"
+
+eval:
+  harness:
+    tasks: [mmlu, gsm8k, ifeval, humaneval]
+    fewshot: 5
+  regression:
+    baseline: Qwen/Qwen3-8B
+    threshold_pct: -1.0
+
+quantize:
+  enabled: true
+  scheme: quark_fp8
+  ptpc: true
+
+serve:
+  backend: vllm-rocm
+  reasoning_parser: qwen3
+  tool_call_parser: hermes
+  tensor_parallel: 1
+  max_model_len: 8192
+  port: 8000
+
+publish:
+  enabled: true
+  hf:
+    repo: lablab-ai-amd-developer-hackathon/mindxtrain-qwen3-8b-demo
+    private: false
+  lighthouse:
+    api_key_env: LIGHTHOUSE_API_KEY
+  mindx:
+    api_url: https://mindx.pythai.net/v1/agents
+    register_as_capability: true
+  agenticplace:
+    api_url: https://agenticplace.pythai.net/v1/listings
+    chain_map_url: https://agenticplace.pythai.net/allchain.html
+  bankon:
+    ens_parent: bankon.eth
+    subname: qwen3-8b-mindxtrain-demo
+  billing:
+    x402:
+      network: algorand
+      asset: USDC
+      receiver_via: parsec_wallet
+      price_per_1k_tokens: 0.0002
+
+receipt:
+  output: ./out/receipt.json
+  include:
+    - rocm_version
+    - gfx_arch
+    - container_digest
+    - all_git_shas
+    - yaml_hash
+    - dataset_cids
+    - eval_report
+    - energy_kwh
diff --git a/mindxtrain/train/recipes/qwen3_vl_8b_sft.yaml b/mindxtrain/train/recipes/qwen3_vl_8b_sft.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6988a130f3af9aa94acf2debc9e51c32075807b7
--- /dev/null
+++ b/mindxtrain/train/recipes/qwen3_vl_8b_sft.yaml
@@ -0,0 +1,47 @@
+# SPDX-License-Identifier: Apache-2.0
+# Qwen3-VL-8B vision-language SFT — Vision/Multimodal track entry.
+meta:
+  project: mindxtrain_demo
+  run_name: qwen3_vl_8b_sft
+  seed: 2048
+
+hardware:
+  name: mi300x
+  gfx_arch: gfx942
+  gpus: 1
+  expected_hbm_gb: 192
+
+autotune:
+  enabled: true
+
+model:
+  name: Qwen/Qwen3-VL-8B-Instruct
+  attn_implementation: flash_attention_2
+  torch_dtype: bfloat16
+
+data:
+  source: hf
+  hf_id: liuhaotian/LLaVA-Instruct-150K
+  split: train
+  seq_len: 4096
+  packing: false
+
+train:
+  backend: axolotl
+  method:
+    kind: lora
+    r: 16
+    alpha: 32
+    target_modules: [q_proj, k_proj, v_proj, o_proj]
+  optimizer:
+    name: adamw_torch_fused
+    lr: 1.0e-4
+  schedule:
+    type: cosine
+    warmup_ratio: 0.03
+    epochs: 2
+  batch:
+    per_device: 4
+    grad_accum: 4
+  precision: bfloat16
+  gradient_checkpointing: true
diff --git a/mindxtrain/train/rlhf.py b/mindxtrain/train/rlhf.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb0f7e7c33c57eade7e1c4b16588e6045f81768c
--- /dev/null
+++ b/mindxtrain/train/rlhf.py
@@ -0,0 +1,66 @@
+"""RLHF (PPO) trainer — TRL PPOTrainer wrapper.
+
+Online preference optimization with a learned reward model. Less efficient
+than DPO for offline pairs; kept here as the canonical surface for online
+RLHF runs.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+from mindxtrain.config.schema import XTrainConfig
+
+
+def run_rlhf(cfg: XTrainConfig, out_dir: Path) -> Path:
+    """Run a PPO/RLHF fine-tune; return the checkpoint directory."""
+    try:
+        from datasets import load_dataset
+        from transformers import AutoModelForCausalLM, AutoTokenizer
+        from trl import PPOConfig, PPOTrainer
+    except ImportError as exc:
+        msg = "TRL stack not installed; run `uv sync --extra ml`."
+        raise RuntimeError(msg) from exc
+
+    method: Any = cfg.train.method
+    if getattr(method, "kind", "") != "rlhf":
+        msg = f"run_rlhf expects train.method.kind == 'rlhf'; got {getattr(method, 'kind', None)!r}"
+        raise ValueError(msg)
+
+    reward_model_path = getattr(method, "reward_model_path", "")
+    if not reward_model_path:
+        msg = "rlhf requires train.method.reward_model_path"
+        raise ValueError(msg)
+
+    out_dir = Path(out_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    tokenizer = AutoTokenizer.from_pretrained(cfg.model.name)
+    policy = AutoModelForCausalLM.from_pretrained(cfg.model.name)
+    ref_policy = AutoModelForCausalLM.from_pretrained(cfg.model.name)
+    reward_model = AutoModelForCausalLM.from_pretrained(reward_model_path)
+
+    train_ds = load_dataset(cfg.data.hf_id, split=getattr(cfg.data, "split", "train"))
+
+    ppo_cfg = PPOConfig(
+        output_dir=str(out_dir),
+        learning_rate=cfg.train.optim.learning_rate,
+        per_device_train_batch_size=cfg.train.micro_batch_size,
+        gradient_accumulation_steps=cfg.train.gradient_accumulation_steps,
+        num_train_epochs=cfg.train.num_epochs,
+    )
+    trainer = PPOTrainer(
+        args=ppo_cfg,
+        model=policy,
+        ref_model=ref_policy,
+        reward_model=reward_model,
+        train_dataset=train_ds,
+        processing_class=tokenizer,
+    )
+    trainer.train()
+    trainer.save_model(str(out_dir))
+    return out_dir
+
+
+__all__ = ["run_rlhf"]
diff --git a/mindxtrain/train/sft.py b/mindxtrain/train/sft.py
new file mode 100644
index 0000000000000000000000000000000000000000..1e792febe581586a479deb2e3363ab53ca2f1f43
--- /dev/null
+++ b/mindxtrain/train/sft.py
@@ -0,0 +1,171 @@
+"""Axolotl SFT/LoRA backend.
+
+Real subprocess wrapper around `accelerate launch -m axolotl.cli.train`. Steps:
+    1. Compile `XTrainConfig` + `AutotunePlan` to an Axolotl YAML via
+       `mindxtrain.train.axolotl_compile.compile_axolotl_yaml`.
+    2. Inject MI300X env vars from the autotune plan.
+    3. Spawn the subprocess; tee stdout/stderr to `runs//train.log`.
+    4. Return the checkpoint directory.
+
+Two entry points share the same prep helpers (`prepare_run`):
+
+- `run_axolotl(cfg, plan, out_dir)` — synchronous; blocks until completion;
+  used by the `mindxtrain train` CLI verb.
+- `prepare_run(cfg, plan, out_dir)` — returns the cmd + env + paths so the
+  Coach UI's streaming launch path can hand them to
+  `mindxtrain.operator.runs.spawn_subprocess_streaming`.
+"""
+
+from __future__ import annotations
+
+import os
+import shutil
+import subprocess
+import sys
+from collections.abc import Callable
+from dataclasses import dataclass
+from pathlib import Path
+
+import yaml
+
+from mindxtrain.autotune.plan import AutotunePlan
+from mindxtrain.config.schema import XTrainConfig
+from mindxtrain.train.axolotl_compile import compile_axolotl_yaml
+
+_BASE_ENV = {
+    "PYTORCH_ROCM_ARCH": "gfx942",
+    "HSA_NO_SCRATCH_RECLAIM": "1",
+    "HIP_FORCE_DEV_KERNARG": "1",
+    "GPU_MAX_HW_QUEUES": "1",
+}
+
+
+def _accelerate_available() -> bool:
+    return shutil.which("accelerate") is not None
+
+
+def _plan_env(plan: AutotunePlan) -> dict[str, str]:
+    env: dict[str, str] = dict(_BASE_ENV)
+    if plan.rccl_config == "8gpu_xgmi":
+        env["NCCL_MIN_NCHANNELS"] = "112"
+    if plan.attention_backend == "ck":
+        env["NVTE_CK_USES_BWD_V3"] = "1"
+        env["NVTE_CK_IS_V3_ATOMIC_FP32"] = "1"
+        env["PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32"] = "1"
+    return env
+
+
+@dataclass(frozen=True)
+class PreparedRun:
+    """Resolved cmd + env + paths for an Axolotl run.
+
+    The streaming launch path consumes this directly; the synchronous
+    `run_axolotl` does too.
+    """
+
+    cmd: list[str]
+    env: dict[str, str]
+    yaml_path: Path
+    log_path: Path
+    checkpoint_dir: Path
+
+
+def prepare_run(cfg: XTrainConfig, plan: AutotunePlan, out_dir: Path) -> PreparedRun:
+    """Compile YAML, materialize on disk, and return the cmd/env/paths.
+
+    Does NOT spawn the subprocess. Raises `RuntimeError` if `accelerate` is
+    not on PATH (the same condition the synchronous wrapper checks).
+    """
+    if not _accelerate_available():
+        msg = (
+            "accelerate not found on PATH; install with `uv sync --extra ml` "
+            "and ensure the `axolotl` package is reachable in the same venv."
+        )
+        raise RuntimeError(msg)
+
+    out_dir = Path(out_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    yaml_payload = compile_axolotl_yaml(cfg, plan)
+    yaml_path = out_dir / f"{cfg.meta.run_name}.axolotl.yaml"
+    yaml_path.write_text(yaml.safe_dump(yaml_payload, sort_keys=False))
+
+    log_path = out_dir / "train.log"
+
+    env = dict(os.environ)
+    env.update(_plan_env(plan))
+
+    cmd = [
+        "accelerate",
+        "launch",
+        "-m",
+        "axolotl.cli.train",
+        str(yaml_path),
+    ]
+
+    return PreparedRun(
+        cmd=cmd,
+        env=env,
+        yaml_path=yaml_path,
+        log_path=log_path,
+        checkpoint_dir=out_dir / yaml_payload.get("output_dir", "checkpoint"),
+    )
+
+
+def _run_streaming(
+    *,
+    cmd: list[str],
+    env: dict[str, str],
+    log_path: Path,
+    on_line: Callable[[str], None],
+) -> int:
+    """Run `cmd`, tee each stdout line to `log_path` and call `on_line(line)`.
+
+    Returns the subprocess return code. Used by the synchronous CLI path;
+    the Coach UI streaming launch path uses
+    `mindxtrain.operator.runs.spawn_subprocess_streaming` instead, which
+    runs the reader in a thread.
+    """
+    log_path.parent.mkdir(parents=True, exist_ok=True)
+    with log_path.open("w", buffering=1) as log:
+        log.write(f"# cmd: {' '.join(cmd)}\n\n")
+        log.flush()
+        proc = subprocess.Popen(
+            cmd,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+            env=env,
+            text=True,
+            bufsize=1,
+        )
+        assert proc.stdout is not None
+        for raw in proc.stdout:
+            log.write(raw)
+            log.flush()
+            on_line(raw.rstrip("\n"))
+        return proc.wait()
+
+
+def run_axolotl(
+    cfg: XTrainConfig,
+    plan: AutotunePlan,
+    out_dir: Path,
+    *,
+    on_line: Callable[[str], None] | None = None,
+) -> Path:
+    """Run an Axolotl training job and return the checkpoint directory.
+
+    Blocks until completion. If `on_line` is provided, it is called once per
+    stdout line (the same lines that get written to `train.log`); the CLI
+    path passes `None`.
+    """
+    prepared = prepare_run(cfg, plan, out_dir)
+    sink = on_line if on_line is not None else (lambda _line: None)
+    rc = _run_streaming(cmd=prepared.cmd, env=prepared.env, log_path=prepared.log_path, on_line=sink)
+    if rc != 0:
+        sys.stderr.write(f"axolotl returned {rc}; see {prepared.log_path}\n")
+        raise SystemExit(rc)
+    return prepared.checkpoint_dir
+
+
+__all__ = ["PreparedRun", "prepare_run", "run_axolotl"]
diff --git a/mindxtrain/train/tool_use.py b/mindxtrain/train/tool_use.py
new file mode 100644
index 0000000000000000000000000000000000000000..703573127dcc58db5474a6ba4f9fefbd6c57406d
--- /dev/null
+++ b/mindxtrain/train/tool_use.py
@@ -0,0 +1,54 @@
+"""BFCL-style tool-trajectory training — supervised on multi-turn tool calls.
+
+Uses TRL's SFTTrainer with a tool-call dataset format compatible with the
+Berkeley Function-Calling Leaderboard schema (single-turn function calls,
+multi-turn trajectories, error-recovery turns).
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+from mindxtrain.config.schema import XTrainConfig
+
+
+def run_tool_use(cfg: XTrainConfig, out_dir: Path) -> Path:
+    """Run a tool-use SFT pass; return the checkpoint directory."""
+    try:
+        from datasets import load_dataset
+        from transformers import AutoModelForCausalLM, AutoTokenizer
+        from trl import SFTConfig, SFTTrainer
+    except ImportError as exc:
+        msg = "TRL + transformers + datasets not installed; run `uv sync --extra ml`."
+        raise RuntimeError(msg) from exc
+
+    out_dir = Path(out_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    tokenizer = AutoTokenizer.from_pretrained(cfg.model.name)
+    model = AutoModelForCausalLM.from_pretrained(cfg.model.name)
+
+    train_ds = load_dataset(cfg.data.hf_id, split=getattr(cfg.data, "split", "train"))
+
+    sft_cfg = SFTConfig(
+        output_dir=str(out_dir),
+        learning_rate=cfg.train.optim.learning_rate,
+        per_device_train_batch_size=cfg.train.micro_batch_size,
+        gradient_accumulation_steps=cfg.train.gradient_accumulation_steps,
+        num_train_epochs=cfg.train.num_epochs,
+        max_seq_length=cfg.data.seq_len,
+        packing=cfg.data.packing,
+        logging_steps=10,
+    )
+    trainer = SFTTrainer(
+        model=model,
+        args=sft_cfg,
+        train_dataset=train_ds,
+        processing_class=tokenizer,
+    )
+    trainer.train()
+    trainer.save_model(str(out_dir))
+    return out_dir
+
+
+__all__ = ["run_tool_use"]
diff --git a/mindxtrain/ui/__init__.py b/mindxtrain/ui/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..535cd24c5ec4bdb3c5b576aafd5d35704004e48d
--- /dev/null
+++ b/mindxtrain/ui/__init__.py
@@ -0,0 +1,9 @@
+"""mindxtrain.ui — the framework as one Gradio surface (Basic · Advanced · Scientific).
+
+    from mindxtrain.ui import build, main
+    main(port=7862)                     # or: mindxtrain ui
+"""
+from .app import VERSION, build, main  # noqa: F401
+from .metrics import RunMetrics, parse_log  # noqa: F401
+
+__all__ = ["build", "main", "VERSION", "RunMetrics", "parse_log"]
diff --git a/mindxtrain/ui/app.py b/mindxtrain/ui/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1af1618c7d2c38b7d38a14fa87918611f2f429d
--- /dev/null
+++ b/mindxtrain/ui/app.py
@@ -0,0 +1,569 @@
+"""The mindXtrain UI — the whole framework as one Gradio surface.
+
+One idea runs through it: **complexity is a dial, not a wall.** Every room has the same three
+tiers, chosen once at the top and remembered:
+
+- **Basic** — pick a recipe, press start, watch it train, read the verdict.
+- **Advanced** — the knobs an operator actually turns: LoRA shape, schedule, batch, throttle,
+  packing, eval split, the gate's floor, where it publishes.
+- **Scientific** — the run as an experiment: every metric the trainer emits with its units and
+  where it came from, the eval harness, the autotune plan, the imprint's before/after with its
+  null, provenance hashes, and the receipt.
+
+Nothing here re-implements training. Every action shells out to the real CLI (`mindxtrain …`) and
+every number is parsed from what the trainer actually wrote — the log is the source of truth, so the
+UI can never claim a step that did not happen.
+
+    mindxtrain ui                      # http://127.0.0.1:7862
+    mindxtrain ui --share              # a public gradio.live link
+    python -m mindxtrain.ui.app
+"""
+from __future__ import annotations
+
+import json
+import os
+import re
+import shlex
+import signal
+import subprocess
+import threading
+import time
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple
+
+import gradio as gr
+
+from .metrics import RunMetrics, parse_log  # noqa: F401  (parse_log re-exported for tests)
+from .theme import CSS, theme
+
+VERSION = "1.0.0"
+HOME = Path(os.environ.get("MINDXTRAIN_HOME") or Path(__file__).resolve().parents[2])
+RECIPES = HOME / "mindxtrain" / "train" / "recipes"
+TIERS = ["Basic", "Advanced", "Scientific"]
+
+
+# ── running the real CLI ──────────────────────────────────────────────────────
+def cli_prefix() -> List[str]:
+    """`uv run --project  mindxtrain` when uv is how this checkout runs, else `mindxtrain`."""
+    if (HOME / "pyproject.toml").is_file() and _which("uv"):
+        return ["uv", "run", "--project", str(HOME), "mindxtrain"]
+    return ["mindxtrain"]
+
+
+def _which(prog: str) -> Optional[str]:
+    from shutil import which
+    return which(prog)
+
+
+class Job:
+    """One CLI invocation, streamed to a log file so the UI can follow it and survive a reload."""
+
+    def __init__(self, args: List[str], log: Path, cwd: Optional[Path] = None):
+        self.args, self.log, self.cwd = args, log, cwd or HOME
+        self.proc: Optional[subprocess.Popen] = None
+        self.started = self.ended = None
+
+    def start(self) -> Dict[str, Any]:
+        self.log.parent.mkdir(parents=True, exist_ok=True)
+        fh = self.log.open("w", encoding="utf-8", errors="replace")
+        self.started = time.time()
+        try:
+            self.proc = subprocess.Popen(self.args, cwd=str(self.cwd), stdout=fh, stderr=subprocess.STDOUT,
+                                         text=True, start_new_session=True)
+        except Exception as e:  # noqa: BLE001
+            fh.write(f"[ui] failed to start: {type(e).__name__}: {e}\n"); fh.close()
+            return {"ok": False, "reason": f"{type(e).__name__}: {e}"}
+        return {"ok": True, "pid": self.proc.pid, "cmd": " ".join(shlex.quote(a) for a in self.args), "log": str(self.log)}
+
+    @property
+    def running(self) -> bool:
+        return bool(self.proc and self.proc.poll() is None)
+
+    def stop(self) -> Dict[str, Any]:
+        if not self.running:
+            return {"ok": False, "reason": "not running"}
+        try:
+            os.killpg(os.getpgid(self.proc.pid), signal.SIGTERM)
+        except Exception:  # noqa: BLE001
+            self.proc.terminate()
+        return {"ok": True, "stopped": self.proc.pid}
+
+
+JOBS: Dict[str, Job] = {}
+RUNS = HOME / "out" / "ui"
+
+
+def launch(kind: str, args: List[str]) -> Dict[str, Any]:
+    if JOBS.get(kind) and JOBS[kind].running:
+        return {"ok": False, "reason": f"a {kind} job is already running (pid {JOBS[kind].proc.pid})"}
+    log = RUNS / f"{kind}-{time.strftime('%Y%m%d-%H%M%S')}.log"
+    j = Job(cli_prefix() + args, log)
+    r = j.start()
+    if r.get("ok"):
+        JOBS[kind] = j
+    return r
+
+
+def run_sync(args: List[str], timeout: float = 120) -> Tuple[int, str]:
+    try:
+        p = subprocess.run(cli_prefix() + args, cwd=str(HOME), capture_output=True, text=True, timeout=timeout)
+        return p.returncode, (p.stdout or "") + (p.stderr or "")
+    except Exception as e:  # noqa: BLE001
+        return 1, f"{type(e).__name__}: {e}"
+
+
+# ── recipes ───────────────────────────────────────────────────────────────────
+def recipe_names() -> List[str]:
+    return sorted(p.stem for p in RECIPES.glob("*.yaml")) if RECIPES.is_dir() else []
+
+
+def read_recipe(name: str) -> str:
+    p = RECIPES / f"{name}.yaml"
+    return p.read_text() if p.is_file() else f"# no recipe named {name}"
+
+
+def recipe_summary(name: str) -> str:
+    """The five numbers that decide what a run costs, pulled from the recipe itself."""
+    try:
+        import yaml
+        c = yaml.safe_load(read_recipe(name)) or {}
+    except Exception as e:  # noqa: BLE001
+        return f"unreadable: {e}"
+    m, d, t = c.get("model") or {}, c.get("data") or {}, c.get("train") or {}
+    meth, sch, bat = t.get("method") or {}, t.get("schedule") or {}, t.get("batch") or {}
+    rows = [("base", m.get("name")), ("precision", t.get("precision") or m.get("torch_dtype")),
+            ("method", f"{meth.get('kind')} r={meth.get('r')} α={meth.get('alpha')} → {', '.join(meth.get('target_modules') or [])}"),
+            ("data", f"{d.get('source')} · seq {d.get('seq_len')} · packing {d.get('packing')} · eval split {d.get('eval_split')}"),
+            ("schedule", f"{sch.get('epochs')} epochs · {sch.get('type')} · warmup {sch.get('warmup_ratio')} · lr {(t.get('optimizer') or {}).get('lr')}"),
+            ("batch", f"per-device {bat.get('per_device')} × grad-accum {bat.get('grad_accum')}"),
+            ("throttle", json.dumps(t.get("cpu_throttle")) if t.get("cpu_throttle") else "—")]
+    return "\n".join(f"**{k}** · {v}" for k, v in rows if v)
+
+
+# ── the surface ───────────────────────────────────────────────────────────────
+def build() -> gr.Blocks:
+    import inspect
+    blocks_takes_theme = "theme" in inspect.signature(gr.Blocks.__init__).parameters
+    bk = {"theme": theme(), "css": CSS} if blocks_takes_theme else {}
+
+    def tier_vis(tier: str) -> Tuple[Any, Any]:
+        return gr.update(visible=tier in ("Advanced", "Scientific")), gr.update(visible=tier == "Scientific")
+
+    with gr.Blocks(title="mindXtrain", fill_height=True, **bk) as demo:
+        gr.HTML(f"
    mindXtrain
    the framework as one surface · v{VERSION} · "
+                f"{HOME} · every action runs the real CLI, every number is parsed from the run's own log
    ")
+        tier = gr.Radio(TIERS, value="Basic", label="complexity", info="Basic: press start. Advanced: the knobs. Scientific: the experiment.")
+
+        with gr.Tabs():
+            # ── FORGE ──
+            with gr.Tab("Forge · train"):
+                with gr.Row():
+                    recipe = gr.Dropdown(recipe_names(), value=(recipe_names() or [None])[0], label="recipe", scale=2)
+                    out_dir = gr.Textbox(value="out/runs", label="output", scale=1)
+                    start_btn = gr.Button("start training", variant="primary", scale=1)
+                    stop_btn = gr.Button("stop", scale=1)
+                summary = gr.Markdown()
+                with gr.Group(visible=False) as adv_forge:
+                    gr.Markdown("**Advanced** — written into the run config before the trainer sees it.")
+                    with gr.Row():
+                        lora_r = gr.Slider(1, 128, value=16, step=1, label="LoRA r")
+                        lora_a = gr.Slider(1, 256, value=32, step=1, label="LoRA α")
+                        epochs = gr.Slider(1, 60, value=2, step=1, label="epochs")
+                        lr = gr.Number(value=1e-4, label="learning rate")
+                    with gr.Row():
+                        seq = gr.Slider(128, 8192, value=1024, step=128, label="sequence length")
+                        per_dev = gr.Slider(1, 32, value=1, step=1, label="batch per device")
+                        accum = gr.Slider(1, 64, value=8, step=1, label="grad accumulation")
+                        packing = gr.Checkbox(value=True, label="packing")
+                    with gr.Row():
+                        cpu_pct = gr.Slider(5, 100, value=33, step=1, label="CPU %")
+                        cpu_nice = gr.Slider(0, 19, value=19, step=1, label="nice")
+                        eval_split = gr.Slider(0.0, 0.5, value=0.1, step=0.01, label="held-out eval split")
+                with gr.Group(visible=False) as sci_forge:
+                    gr.Markdown("**Scientific** — the recipe verbatim. What you edit here is what the trainer reads.")
+                    recipe_yaml = gr.Code(label="run.yaml", language="yaml", lines=18, interactive=True)
+                    with gr.Row():
+                        save_as = gr.Textbox(value="run.yaml", label="write to", scale=2)
+                        save_btn = gr.Button("write config", scale=1)
+                    save_state = gr.Markdown()
+                gr.Markdown("### live")
+                kiln = gr.HTML()
+                with gr.Row():
+                    loss_plot = gr.LinePlot(x="step", y="value", color="metric", title="loss · token accuracy · lr (normalised)",
+                                            height=260, container=True)
+                    metrics_tbl = gr.Dataframe(headers=["metric", "value", "unit", "from"], interactive=False, wrap=True)
+                log_box = gr.Code(label="the run's log (tail)", lines=14, interactive=False)
+                with gr.Accordion("diagnostics — the host, and what the log is telling you", open=False):
+                    with gr.Row():
+                        host_md = gr.Markdown()
+                        diag_md = gr.Markdown()
+                    diag_btn = gr.Button("refresh diagnostics")
+
+                ticker = gr.Timer(value=6, active=False)
+
+            # ── GATE ──
+            with gr.Tab("Gate · imprint"):
+                gr.Markdown("**The gate.** Recall of the corpus, measured on the frozen base first and the trained model second. "
+                            "A positive delta is the only thing that makes a run count — and it proves recall, not identity.")
+                with gr.Row():
+                    g_cfg = gr.Textbox(value="run.yaml", label="config", scale=2)
+                    g_max = gr.Slider(1, 64, value=9, step=1, label="inquiries", scale=1)
+                    g_btn = gr.Button("run the gate", variant="primary", scale=1)
+                with gr.Group(visible=False) as adv_gate:
+                    with gr.Row():
+                        g_trigger = gr.Checkbox(value=False, label="trigger a dream first (mindX node)")
+                        g_out = gr.Textbox(value="out/imprint", label="output")
+                with gr.Group(visible=False) as sci_gate:
+                    gr.Markdown("**The null matters.** An untrained random-init adapter imprinted N times is the floor; "
+                                "a delta below it is noise. Decoding is greedy with repetition_penalty 1.3 and no_repeat_ngram_size 3 — "
+                                "change it and the number stops being comparable.")
+                g_out_md = gr.Markdown()
+                g_json = gr.Code(label="verdict", language="json", interactive=False)
+
+            # ── MEASURE ──
+            with gr.Tab("Measure · eval"):
+                with gr.Row():
+                    e_cfg = gr.Textbox(value="run.yaml", label="config", scale=2)
+                    e_ckpt = gr.Textbox(value="", label="checkpoint (blank = the recipe's)", scale=2)
+                    e_btn = gr.Button("eval", variant="primary", scale=1)
+                    e_ce_btn = gr.Button("cross-entropy vs base", scale=1)
+                with gr.Group(visible=False) as adv_eval:
+                    with gr.Row():
+                        e_jsonl = gr.Textbox(value="", label="held-out JSONL (for the CE comparison)")
+                        e_max = gr.Slider(8, 2048, value=128, step=8, label="max samples")
+                with gr.Group(visible=False) as sci_eval:
+                    gr.Markdown("`eval` runs lm-eval-harness; `eval-checkpoint` compares **base vs base+adapter cross-entropy** on "
+                                "rows the model never trained on. The second is the one that cannot be gamed by memorising the corpus.")
+                e_out = gr.Code(label="result", language="json", interactive=False)
+
+            # ── SERVE ──
+            with gr.Tab("Serve"):
+                with gr.Row():
+                    s_cfg = gr.Textbox(value="run.yaml", label="config", scale=2)
+                    s_ckpt = gr.Textbox(value="", label="checkpoint", scale=2)
+                    s_to = gr.Radio(["ollama", "vllm"], value="ollama", label="to", scale=1)
+                    s_tag = gr.Textbox(value="", label="tag", scale=1)
+                    s_btn = gr.Button("serve", variant="primary", scale=1)
+                with gr.Group(visible=False) as adv_serve:
+                    with gr.Row():
+                        s_fallback = gr.Checkbox(value=False, label="register as mindX's fallback model")
+                        s_base = gr.Textbox(value="", label="mindX base URL (for the registration)")
+                with gr.Group(visible=False) as sci_serve:
+                    gr.Markdown("A LoRA has meaning only on the tensors it was trained on. Serving an adapter onto a different "
+                                "architecture succeeds silently and means nothing — the tag's base must match "
+                                "`adapter_config.json:base_model_name_or_path`.")
+                s_out = gr.Code(label="result", language="json", interactive=False)
+
+            # ── HUB ──
+            with gr.Tab("Hub · Hugging Face"):
+                gr.Markdown("The **Hugging Face extension** (`mindxtrain.hf`): who the token is, what it may write, "
+                            "a finished run published with its evidence, and the corpus beside it.")
+                with gr.Row():
+                    who_btn = gr.Button("whoami + write scope", variant="primary")
+                    h_repo = gr.Textbox(value="", label="model repo (org/name)", scale=2)
+                    h_run = gr.Textbox(value="out/runs", label="run dir", scale=2)
+                with gr.Row():
+                    h_dry = gr.Checkbox(value=True, label="dry run (show what would upload)")
+                    h_private = gr.Checkbox(value=False, label="private")
+                    h_pub_btn = gr.Button("publish the run")
+                with gr.Group(visible=False) as adv_hub:
+                    with gr.Row():
+                        h_base = gr.Textbox(value="", label="pull a base before training (model id)")
+                        h_pull_btn = gr.Button("pull base")
+                        h_ds = gr.Textbox(value="", label="corpus → dataset repo")
+                        h_ds_path = gr.Textbox(value="", label="corpus path")
+                        h_ds_btn = gr.Button("push corpus")
+                with gr.Group(visible=False) as sci_hub:
+                    gr.Markdown("Traps this module encodes: membership ≠ write scope · `list_repo_tree` entries carry `.path` "
+                                "(only `repo_info().siblings` carry `.rfilename`) · the Hub checks a Space's ZeroGPU quota **before** "
+                                "existence (402 on re-push) · a Space README `short_description` must be ≤ 60 characters · "
+                                "never put a write-scoped token on a public Space.")
+                    with gr.Row():
+                        h_lineage_repo = gr.Textbox(value="", label="lineage of repo")
+                        h_lineage_btn = gr.Button("read lineage")
+                h_out = gr.Code(label="result", language="json", interactive=False)
+
+
+            # ── COACH ──
+            with gr.Tab("Coach · intuitive training"):
+                gr.Markdown("**The coach turns a measured impression into the next run.** It reads what the last "
+                            "generations actually scored, says what to change, and — when you agree — starts that run here. "
+                            "`bootcamp.impression` is drill → impression; `impression.bootcamp` is impression → the next drill.")
+                with gr.Row():
+                    c_base = gr.Textbox(value=os.environ.get("MINDX_BASE_URL", "https://mindx.pythai.net"),
+                                        label="mindX node (where the coach's measurements live)", scale=3)
+                    c_read = gr.Button("read the coach", variant="primary", scale=1)
+                c_verdict = gr.HTML()
+                with gr.Row():
+                    c_rec = gr.Code(label="the recipe the coach proposes", language="json", interactive=False, scale=2)
+                    c_score = gr.Dataframe(headers=["gen", "identity", "task", "coherence", "influence Δrecall", "runs"],
+                                           label="scorecards", interactive=False, scale=2)
+                with gr.Row():
+                    c_adopt = gr.Button("adopt it into the Forge knobs")
+                    c_start = gr.Button("adopt and start the run", variant="primary")
+                c_state = gr.Markdown()
+                with gr.Group(visible=False) as sci_coach:
+                    gr.Markdown("**What the coach is allowed to conclude.** Influence is always *after − before* on the same "
+                                "probe, with the untouched base answering too. Identity is a scorer, not a vibe. Three runs with "
+                                "no positive influence is `training_stalled` — the drill changes, not the compute. A rung the "
+                                "ladder already rejected is never proposed again.")
+
+            # ── BENCH ──
+            with gr.Tab("Bench · autotune"):
+                gr.Markdown("The 60-second AOT probe: the plan is fixed before the run starts, and JIT autotune is forbidden "
+                            "inside the production loop.")
+                with gr.Row():
+                    b_dry = gr.Checkbox(value=True, label="dry run (no GPU)")
+                    b_out = gr.Textbox(value="autotune_plan.json", label="plan out")
+                    b_btn = gr.Button("bench", variant="primary")
+                b_res = gr.Code(label="plan", language="json", interactive=False)
+
+            # ── RUNS ──
+            with gr.Tab("Runs · receipts"):
+                with gr.Row():
+                    r_refresh = gr.Button("refresh", variant="primary")
+                    r_root = gr.Textbox(value="out/runs", label="runs root", scale=2)
+                r_tbl = gr.Dataframe(headers=["run", "when", "steps", "train loss", "eval loss", "checkpoint", "size"],
+                                     interactive=False, wrap=True)
+                with gr.Group(visible=False) as sci_runs:
+                    gr.Markdown("A **receipt** verifies a provenance manifest's BLAKE3 hashes against what is on disk. "
+                                "A run you cannot re-hash is a story, not a result.")
+                    with gr.Row():
+                        r_manifest = gr.Textbox(value="", label="manifest")
+                        r_receipt_btn = gr.Button("verify receipt")
+                    r_receipt = gr.Code(label="receipt", language="json", interactive=False)
+
+        gr.HTML("
    mindXtrain · "
+                "source · "
+                "orchestration · "
+                "the node that runs it
    ")
+
+        # ── tier wiring ──
+        for adv, sci in ((adv_forge, sci_forge), (adv_gate, sci_gate), (adv_eval, sci_eval),
+                         (adv_serve, sci_serve), (adv_hub, sci_hub), (adv_hub, sci_runs), (adv_hub, sci_coach)):
+            tier.change(tier_vis, [tier], [adv, sci])
+
+        # ── handlers ──
+        recipe.change(lambda n: (recipe_summary(n), read_recipe(n)), [recipe], [summary, recipe_yaml])
+        demo.load(lambda: (recipe_summary(recipe_names()[0]) if recipe_names() else "no recipes found",
+                           read_recipe(recipe_names()[0]) if recipe_names() else ""), None, [summary, recipe_yaml])
+
+        def do_save(text: str, where: str):
+            p = (HOME / where) if not os.path.isabs(where) else Path(where)
+            p.write_text(text)
+            return f"wrote `{p}` ({len(text)} bytes)"
+        save_btn.click(do_save, [recipe_yaml, save_as], [save_state])
+
+        def do_train(rec, outd, t, r_, a_, ep, lr_, sq, pd, ac, pk, cp, cn, es):
+            cfg = HOME / "run.ui.yaml"
+            text = read_recipe(rec)
+            if t in ("Advanced", "Scientific"):
+                try:
+                    import yaml
+                    c = yaml.safe_load(text) or {}
+                    tr = c.setdefault("train", {})
+                    tr.setdefault("method", {}).update({"r": int(r_), "alpha": int(a_)})
+                    tr.setdefault("schedule", {}).update({"epochs": int(ep)})
+                    tr.setdefault("optimizer", {}).update({"lr": float(lr_)})
+                    tr.setdefault("batch", {}).update({"per_device": int(pd), "grad_accum": int(ac)})
+                    tr["cpu_throttle"] = {"percent": int(cp), "nice": int(cn)}
+                    d = c.setdefault("data", {})
+                    d.update({"seq_len": int(sq), "packing": bool(pk), "eval_split": float(es)})
+                    text = yaml.safe_dump(c, sort_keys=False)
+                except Exception as e:  # noqa: BLE001
+                    return f"config edit failed: {e}", gr.Timer(active=False)
+            cfg.write_text(text)
+            r = launch("train", ["train", str(cfg), "--out", outd, "--cpu-percent", str(int(cp)), "--cpu-nice", str(int(cn))])
+            if not r.get("ok"):
+                return f"{r.get('reason')}", gr.Timer(active=False)
+            return (f"started · pid {r['pid']} · {r['cmd']}", gr.Timer(active=True))
+        start_btn.click(do_train, [recipe, out_dir, tier, lora_r, lora_a, epochs, lr, seq, per_dev, accum, packing, cpu_pct, cpu_nice, eval_split],
+                        [kiln, ticker])
+        stop_btn.click(lambda: (json.dumps(JOBS["train"].stop() if JOBS.get("train") else {"ok": False, "reason": "no job"}), gr.Timer(active=False)),
+                       None, [kiln, ticker])
+
+        def tick(t):
+            j = JOBS.get("train")
+            if not j:
+                return "no run yet", [], gr.LinePlot(), "", gr.Timer(active=False)
+            m = parse_log(j.log)
+            head = m.headline(running=j.running, started=j.started)
+            rows = m.rows(scientific=(t == "Scientific"))
+            frame = m.frame()
+            tail = m.tail(j.log, 60)
+            return head, rows, gr.LinePlot(value=frame, x="step", y="value", color="metric"), tail, gr.Timer(active=j.running)
+        ticker.tick(tick, [tier], [kiln, metrics_tbl, loss_plot, log_box, ticker])
+
+        def do_gate(cfg, n, trigger, outp):
+            args = ["imprint", "--config", cfg, "--max-inquiries", str(int(n))]
+            if outp:
+                args += ["--out", outp]
+            if trigger:
+                args += ["--trigger-dream"]
+            code, text = run_sync(args, timeout=3600)
+            verdict = _last_json(text)
+            delta = (verdict or {}).get("delta")
+            md = ("imprinted" if (verdict or {}).get("imprinted") else "not imprinted") \
+                 + (f" · Δ recall **{delta}**" if delta is not None else "")
+            return md, json.dumps(verdict or {"exit": code, "output": text[-1500:]}, indent=1)
+        g_btn.click(do_gate, [g_cfg, g_max, g_trigger, g_out], [g_out_md, g_json])
+
+        def do_eval(cfg, ck):
+            code, text = run_sync(["eval", "--config", cfg] + (["--checkpoint", ck] if ck else []), timeout=3600)
+            return json.dumps(_last_json(text) or {"exit": code, "output": text[-2000:]}, indent=1)
+        e_btn.click(do_eval, [e_cfg, e_ckpt], [e_out])
+
+        def do_ce(cfg, ck, jsonl, mx):
+            args = ["eval-checkpoint", "--config", cfg] + (["--checkpoint", ck] if ck else [])
+            if jsonl:
+                args += ["--jsonl", jsonl]
+            args += ["--max-samples", str(int(mx))]
+            code, text = run_sync(args, timeout=3600)
+            return json.dumps(_last_json(text) or {"exit": code, "output": text[-2000:]}, indent=1)
+        e_ce_btn.click(do_ce, [e_cfg, e_ckpt, e_jsonl, e_max], [e_out])
+
+        def do_serve(cfg, ck, to, tag, fb, base):
+            args = ["serve", "--config", cfg, "--to", to] + (["--checkpoint", ck] if ck else []) + (["--tag", tag] if tag else [])
+            if fb:
+                args += ["--register-as-fallback"]
+            if base:
+                args += ["--mindx-base-url", base]
+            code, text = run_sync(args, timeout=3600)
+            return json.dumps(_last_json(text) or {"exit": code, "output": text[-2000:]}, indent=1)
+        s_btn.click(do_serve, [s_cfg, s_ckpt, s_to, s_tag, s_fallback, s_base], [s_out])
+
+        def do_bench(dry, outp):
+            code, text = run_sync(["bench", "--out", outp] + (["--dry-run"] if dry else []), timeout=1800)
+            return json.dumps(_last_json(text) or {"exit": code, "output": text[-2000:]}, indent=1)
+        b_btn.click(do_bench, [b_dry, b_out], [b_res])
+
+        # Hub
+        def _hf():
+            from mindxtrain import hf as H
+            return H
+        who_btn.click(lambda: json.dumps(_hf().account(), indent=1), None, [h_out])
+        h_pub_btn.click(lambda repo, run, dry, priv: json.dumps(
+            _hf().publish_generation(run, repo, dry_run=bool(dry), private=bool(priv)), indent=1, default=str),
+            [h_repo, h_run, h_dry, h_private], [h_out])
+        h_pull_btn.click(lambda mid: json.dumps(_hf().pull_base(mid), indent=1), [h_base], [h_out])
+        h_ds_btn.click(lambda repo, path: json.dumps(_hf().push_dataset(path, repo), indent=1), [h_ds, h_ds_path], [h_out])
+        h_lineage_btn.click(lambda repo: json.dumps(_hf().lineage(repo), indent=1), [h_lineage_repo], [h_out])
+
+
+        # ── Coach ──
+        def read_coach(base):
+            import urllib.request
+            def get(path, timeout=120):
+                try:
+                    with urllib.request.urlopen(base.rstrip("/") + path, timeout=timeout) as r:
+                        return json.loads(r.read().decode("utf-8"))
+                except Exception as e:  # noqa: BLE001
+                    return {"error": f"{type(e).__name__}: {str(e)[:160]}"}
+            c = get("/insight/hf/coach")
+            if c.get("error"):
+                return f"{c['error']}", "{}", []
+            v = c.get("coach_verdict") or {}
+            sc = (c.get("scorecards") or {}).get("per_generation") or {}
+            rows = [[g, d.get("identity_rate"), d.get("task_score", d.get("task")), d.get("coherence"),
+                     (d.get("influence") or {}).get("recall_delta") if isinstance(d.get("influence"), dict) else d.get("influence"),
+                     d.get("runs")] for g, d in sorted(sc.items(), key=lambda kv: int(kv[0]) if str(kv[0]).isdigit() else 0)
+                    if isinstance(d, dict)]
+            rec = c.get("recommendation") or {}
+            imp = (c.get("iterations") or {})
+            head = (f"{v.get('verdict','—')} over {v.get('n',0)} exchanges · "
+                    f"Δrecall {v.get('recall_delta','—')} · Δcoherence {v.get('coherence_delta','—')} · "
+                    f"Δidentity {v.get('identity_delta','—')} · persona {(c.get('personas') or {}).get('selected','—')}"
+                    + (f" · iterations {imp.get('runs')}" if imp else ""))
+            return head, json.dumps(rec, indent=1)[:3000], rows
+        c_read.click(read_coach, [c_base], [c_verdict, c_rec, c_score])
+
+        def adopt(rec_json):
+            try:
+                r = json.loads(rec_json or "{}")
+            except Exception:  # noqa: BLE001
+                r = {}
+            p = r.get("params") or r.get("recipe") or r
+            if not isinstance(p, dict) or not p:
+                return ("no recipe to adopt — read the coach first",
+                        gr.update(), gr.update(), gr.update(), gr.update())
+            return (f"adopted: {json.dumps(p)[:200]}",
+                    gr.update(value=int(p.get("lora_r", 16))), gr.update(value=int(p.get("lora_alpha", 32))),
+                    gr.update(value=int(p.get("epochs", 2))), gr.update(value=float(p.get("lr", 1e-4))))
+        c_adopt.click(adopt, [c_rec], [c_state, lora_r, lora_a, epochs, lr])
+
+        def adopt_and_start(rec_json, rec_name, outd, t, r_, a_, ep, lr_, sq, pd, ac, pk, cp, cn, es):
+            msg, r_u, a_u, ep_u, lr_u = adopt(rec_json)
+            r_ = r_u.get("value", r_) if isinstance(r_u, dict) else r_
+            a_ = a_u.get("value", a_) if isinstance(a_u, dict) else a_
+            ep = ep_u.get("value", ep) if isinstance(ep_u, dict) else ep
+            lr_ = lr_u.get("value", lr_) if isinstance(lr_u, dict) else lr_
+            head, tick_ = do_train(rec_name, outd, "Advanced", r_, a_, ep, lr_, sq, pd, ac, pk, cp, cn, es)
+            return f"{msg}
    {head}", tick_
+        c_start.click(adopt_and_start,
+                      [c_rec, recipe, out_dir, tier, lora_r, lora_a, epochs, lr, seq, per_dev, accum, packing, cpu_pct, cpu_nice, eval_split],
+                      [c_state, ticker])
+
+        # ── diagnostics ──
+        def diagnostics():
+            host = []
+            try:
+                import psutil
+                vm = psutil.virtual_memory()
+                host = [f"**CPU** {psutil.cpu_percent(interval=0.3):.0f}% of {psutil.cpu_count()} cores · load {', '.join(f'{x:.2f}' for x in os.getloadavg())}",
+                        f"**RAM** {vm.used/1e9:.1f} / {vm.total/1e9:.1f} GB ({vm.percent:.0f}%)",
+                        f"**disk** {psutil.disk_usage(str(HOME)).percent:.0f}% used at {HOME}"]
+            except Exception:  # noqa: BLE001
+                try:
+                    host = [f"**load** {', '.join(f'{x:.2f}' for x in os.getloadavg())}",
+                            "**RAM/CPU** install `psutil` (`uv sync --extra obs`) for the full readout"]
+                except Exception:  # noqa: BLE001
+                    host = ["host telemetry unavailable on this platform"]
+            j = JOBS.get("train")
+            if not j:
+                return "\n\n".join(host), "no run to diagnose yet"
+            m = parse_log(j.log)
+            return "\n\n".join(host), "\n\n".join("· " + d for d in m.diagnose())
+        diag_btn.click(diagnostics, None, [host_md, diag_md])
+
+        # Runs
+        def list_runs(root):
+            base = (HOME / root) if not os.path.isabs(root) else Path(root)
+            rows = []
+            if base.is_dir():
+                for d in sorted(base.iterdir(), key=lambda p: p.stat().st_mtime if p.exists() else 0, reverse=True)[:40]:
+                    if not d.is_dir():
+                        continue
+                    log = next((p for p in (d / "train.log", d.parent / "train.log") if p.is_file()), None)
+                    m = parse_log(log) if log else None
+                    ck = d / "checkpoint"
+                    size = sum(f.stat().st_size for f in d.rglob("*") if f.is_file())
+                    rows.append([d.name, time.strftime("%Y-%m-%d %H:%M", time.localtime(d.stat().st_mtime)),
+                                 (m.last_step if m else None), (m.train_loss if m else None), (m.eval_loss if m else None),
+                                 "✓" if ck.is_dir() else "", f"{size/1e6:.0f} MB"])
+            return rows
+        r_refresh.click(list_runs, [r_root], [r_tbl])
+        r_receipt_btn.click(lambda man: json.dumps(_last_json(run_sync(["receipt", "--manifest", man], 600)[1]) or {}, indent=1),
+                            [r_manifest], [r_receipt])
+
+    demo.mx_launch = {} if blocks_takes_theme else {"theme": theme(), "css": CSS}
+    return demo
+
+
+def _last_json(text: str) -> Optional[Dict[str, Any]]:
+    """The last JSON object a CLI printed — the verbs end with one."""
+    for m in reversed(list(re.finditer(r"\{.*?\}", text or "", re.S))):
+        try:
+            return json.loads(m.group(0))
+        except Exception:  # noqa: BLE001
+            continue
+    return None
+
+
+def main(host: str = "127.0.0.1", port: int = 7862, share: bool = False, mcp: bool = True) -> None:
+    demo = build()
+    demo.queue(default_concurrency_limit=4).launch(server_name=host, server_port=port, share=share,
+                                                   mcp_server=mcp, **getattr(demo, "mx_launch", {}))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/mindxtrain/ui/metrics.py b/mindxtrain/ui/metrics.py
new file mode 100644
index 0000000000000000000000000000000000000000..249326141d22df7432cc991ebb29b118cee31e1f
--- /dev/null
+++ b/mindxtrain/ui/metrics.py
@@ -0,0 +1,293 @@
+"""Diagnostics parsed from what the trainer actually wrote.
+
+The log is the source of truth. Nothing in this module estimates a step that did not happen, and
+every number carries its **unit** and **where it came from**, because a metric without provenance is
+a rumour. It reads three things TRL/transformers emit and one thing mindXtrain does:
+
+1. step dicts  — ``{'loss': .., 'grad_norm': .., 'learning_rate': .., 'entropy': ..,
+                   'num_tokens': .., 'mean_token_accuracy': .., 'epoch': ..}``
+2. eval dicts  — ``{'eval_loss': .., 'eval_runtime': .., 'eval_entropy': .., 'eval_num_tokens': ..,
+                   'eval_mean_token_accuracy': ..}``
+3. the closing dict — ``{'train_runtime': .., 'train_loss': .., 'train_samples_per_second': ..}``
+4. tqdm         — ``24%|███ | 28/116 [14:55<46:01, 31.38s/it]`` (progress, ETA, seconds per step)
+
+and it raises the warnings that actually cost runs: packing without a flash-attention backend,
+a throttle that is not being honoured, a loss that stopped moving, an eval that has drifted above
+train (memorising), and gradient norms that are collapsing or exploding.
+"""
+from __future__ import annotations
+
+import ast
+import math
+import re
+import time
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+_DICT = re.compile(r"\{'(?:loss|eval_loss|train_runtime)'.*?\}")
+_TQDM = re.compile(r"(?:(?P