Switch sandboxes to Harbor hf-sandbox backend; add pinned task prep with docker_image patch; dataset discovery via prompt_rows()
Browse files- README.md +41 -40
- data/test.json +252 -0
- data/train.json +0 -0
- prepare_tasks.py +111 -0
- run_job.sh +14 -7
- serve_env.sh +10 -5
- train_lora.py +16 -7
README.md
CHANGED
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@@ -5,62 +5,64 @@ Reproduction of the OpenCode-only run from
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but training a **LoRA adapter** instead of full weights, so the result is a small,
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distributable adapter on top of `LiquidAI/LFM2.5-2.6B`.
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Stack: OpenEnv (capture proxy + Harbor integration) → Harbor (tasks +
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TRL `AsyncGRPOTrainer` with `HarnessRolloutWorker` → vLLM policy server.
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-
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-
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## Why merged-weight sync (source-verified)
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Generation
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-
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`HarnessRolloutWorker` never gets one
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-
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token when serving an adapter, so merged sync is the better default anyway.
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**Checkpoints still contain only the LoRA adapter** — that is what you distribute.
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## Files
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| 26 |
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| 27 |
| File | Purpose |
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|---|---|
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| 29 |
-
| `train_lora.py` | Worker wiring + `AsyncGRPOConfig` + `LoraConfig` (r=32 on q/k/v/out_proj, w1/2/3) |
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| 30 |
| `serve_policy.sh` | vLLM with capture-proxy flags + NCCL weight transfer |
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| 31 |
-
| `serve_env.sh` | `openenv harbor serve` with production env vars (`MAX_CONCURRENT_ENVS`, pinned OpenCode
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| 32 |
| `run_job.sh` | Single-container entrypoint for HF Jobs (setup + all 3 processes) |
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| `export_adapter.py` | Publish any adapter checkpoint to the Hub (+ optional merged model) |
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| 34 |
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## Launch on HF Jobs
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-
Two runs: a smoke run to validate the stack, then the full run.
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| 38 |
-
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```bash
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| 40 |
# 0. One-time: put this folder on the Hub so the job can pull it
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| 41 |
-
hf repos create YOU/lfm-opencode-lora-rl-code --type model
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hf upload YOU/lfm-opencode-lora-rl-code .
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-
# 1. Smoke run (~
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hf jobs run vllm/vllm-openai:latest --flavor h200x2 --timeout 6h --detach \
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| 46 |
-
--secrets HF_TOKEN="$
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--env ADAPTER_REPO=YOU/LFM2.5-2.6B-opencode-lora-RL MAX_STEPS=20 \
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-- bash -c 'pip -q install huggingface_hub && hf download YOU/lfm-opencode-lora-rl-code --local-dir /app && bash /app/run_job.sh'
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| 50 |
-
# 2. Watch
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#
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-
hf jobs ps
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hf jobs logs <job-id> --follow
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| 54 |
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# 3. Full run (the article's OpenCode-only run took ~32h on 2x H100)
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| 56 |
hf jobs run vllm/vllm-openai:latest --flavor h200x2 --timeout 72h --detach \
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| 57 |
-
--secrets HF_TOKEN="$
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--env ADAPTER_REPO=YOU/LFM2.5-2.6B-opencode-lora-RL MAX_STEPS=1000 \
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-- bash -c 'pip -q install huggingface_hub && hf download YOU/lfm-opencode-lora-rl-code --local-dir /app && bash /app/run_job.sh'
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```
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-
Checkpoints (adapter-only) push to `ADAPTER_REPO`
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-
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## What you distribute
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@@ -77,28 +79,27 @@ Consumers load it with `PeftModel.from_pretrained("LiquidAI/LFM2.5-2.6B", YOU/..
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## Recipe choices carried over from the article (don't regress these)
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-
- `top_p=1.0`, `top_k=0` — truncation biases the policy and breaks logprob agreement
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-
(importance ratio dropped to ~0.99 with top_p<1).
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- Same 4096-token output cap in training and evaluation (`OPENENV_MAX_OUTPUT_TOKENS`
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+ worker `max_tokens`). A Qwen run with 16k training answers collapsed at 4k eval.
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| 84 |
-
- Correctness + <=0.1 tool-efficiency bonus on correct answers
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| 85 |
-
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| 86 |
- `num_generations=8`, one harness per group, so advantage compares actions.
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| 87 |
- `max_tool_calling_iterations=32` on the worker — the loop-owning default would
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| 88 |
silently cap the agent at 8 model calls.
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| 89 |
-
- `max_staleness=4` (config default) matches the article's
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-
optimizer steps old".
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| 91 |
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## Honest caveats
|
| 93 |
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| 94 |
-
- `HarnessRolloutWorker` + `peft_config`
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-
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-
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- Expect the article's single-harness shape: gains concentrate in OpenCode
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(34 → 58% there in their run) and transfer unevenly elsewhere.
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- LoRA r=32 may cap peak accuracy vs full FT; no published comparison on this stack.
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- Costs: h200x2 for ~32–46h is real money, plus ~1000s of Daytona sandbox-hours.
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The smoke run is the cheap insurance.
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- Task data: SmolDataEnvs-harbor-train (1,000 tasks) / -test (250), public,
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non-gated. For the pinned, hash-checked variant see the `prepare.py` flow in the
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[FineEnvs env Space](https://huggingface.co/spaces/FineEnvs/smoldataenv-multi-harness-harbor).
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but training a **LoRA adapter** instead of full weights, so the result is a small,
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distributable adapter on top of `LiquidAI/LFM2.5-2.6B`.
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+
Stack: OpenEnv (capture proxy + Harbor integration) → Harbor (tasks + **HF Sandboxes**) →
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TRL `AsyncGRPOTrainer` with `HarnessRolloutWorker` → vLLM policy server.
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+
Every API here was verified against the released wheels (trl 1.16.0, openenv 0.10.0,
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+
harbor 0.24.0), the `harbor_env` source, and the FineEnvs production env Space.
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+
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+
**Sandboxes are HF Sandboxes** (Harbor's `hf-sandbox` backend, harbor>=0.24): each
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rollout boots an HF Jobs cpu-basic machine running the task's prebuilt image
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+
(`savatar101/env-data-agent-train:base` — the same image every task's Dockerfile is
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a one-line `FROM` of). Only `HF_TOKEN` is needed; no Daytona/E2B account.
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## Why merged-weight sync (source-verified)
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+
Generation goes harness → capture proxy → vLLM, addressed by a **fixed model name**.
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+
TRL's adapter-only sync publishes versioned adapters (`trl-policy-v{N}`) and only the
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+
trainer-built default worker learns the versioned name; an injected
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+
`HarnessRolloutWorker` never gets one. So the server runs *without* `--enable-lora`
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and the trainer merges the LoRA into the base and NCCL-streams full weights each step
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+
(~5.2 GB for 2.6B — trivial on one node; vLLM also serves adapters 1.33–1.39x slower
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+
per token). **Checkpoints still contain only the LoRA adapter** — that is what you ship.
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## Files
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| 29 |
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| File | Purpose |
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| 31 |
|---|---|
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+
| `train_lora.py` | Worker wiring + `AsyncGRPOConfig` + `LoraConfig` (r=32 on q/k/v/out_proj, w1/2/3). Task dataset is discovered from the env server via `HarborSessionFactory.prompt_rows()` — `create()` rejects any prompt the server doesn't serve |
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| `serve_policy.sh` | vLLM with capture-proxy flags + NCCL weight transfer |
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+
| `serve_env.sh` | `openenv harbor serve` with production env vars (`MAX_CONCURRENT_ENVS`, pinned OpenCode 1.18.31, 4096 output cap) |
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| 35 |
+
| `prepare_tasks.py` + `data/` | Materializes the pinned 1,000 train / 250 test tasks (hash-checked, from the FineEnvs Space) and patches `docker_image` into each task.toml — the one change HF Sandboxes require |
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| `run_job.sh` | Single-container entrypoint for HF Jobs (setup + all 3 processes) |
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| `export_adapter.py` | Publish any adapter checkpoint to the Hub (+ optional merged model) |
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## Launch on HF Jobs
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```bash
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# 0. One-time: put this folder on the Hub so the job can pull it
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+
hf repos create YOU/lfm-opencode-lora-rl-code --type model --private
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| 44 |
+
hf upload YOU/lfm-opencode-lora-rl-code . \
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+
--include "*.py" --include "*.sh" --include "*.md" --include "data/*.json"
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| 47 |
+
# 1. Smoke run (~1-2h): validates weight sync, capture contract, sandbox boots
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hf jobs run vllm/vllm-openai:latest --flavor h200x2 --timeout 6h --detach \
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+
--secrets HF_TOKEN="$(hf auth token)" \
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| 50 |
--env ADAPTER_REPO=YOU/LFM2.5-2.6B-opencode-lora-RL MAX_STEPS=20 \
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-- bash -c 'pip -q install huggingface_hub && hf download YOU/lfm-opencode-lora-rl-code --local-dir /app && bash /app/run_job.sh'
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+
# 2. Watch: merged weight transfers (no .vllm_lora/), sandbox Jobs booting under
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+
# your account (hf jobs ps), rollouts returning rewards, no CaptureContractError.
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hf jobs logs <job-id> --follow
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# 3. Full run (the article's OpenCode-only run took ~32h on 2x H100)
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| 58 |
hf jobs run vllm/vllm-openai:latest --flavor h200x2 --timeout 72h --detach \
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| 59 |
+
--secrets HF_TOKEN="$(hf auth token)" \
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| 60 |
--env ADAPTER_REPO=YOU/LFM2.5-2.6B-opencode-lora-RL MAX_STEPS=1000 \
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-- bash -c 'pip -q install huggingface_hub && hf download YOU/lfm-opencode-lora-rl-code --local-dir /app && bash /app/run_job.sh'
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```
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| 63 |
|
| 64 |
+
Checkpoints (adapter-only) push to `ADAPTER_REPO` every 100 steps, so a dead job
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+
loses little. `SANDBOX=daytona`/`e2b` + the matching API key secret still works.
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## What you distribute
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| 68 |
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| 79 |
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| 80 |
## Recipe choices carried over from the article (don't regress these)
|
| 81 |
|
| 82 |
+
- `top_p=1.0`, `top_k=0` — truncation biases the policy and breaks logprob agreement.
|
|
|
|
| 83 |
- Same 4096-token output cap in training and evaluation (`OPENENV_MAX_OUTPUT_TOKENS`
|
| 84 |
+ worker `max_tokens`). A Qwen run with 16k training answers collapsed at 4k eval.
|
| 85 |
+
- Correctness + <=0.1 tool-efficiency bonus on correct answers (else all-correct
|
| 86 |
+
GRPO groups carry no signal and tool calls creep up).
|
| 87 |
- `num_generations=8`, one harness per group, so advantage compares actions.
|
| 88 |
- `max_tool_calling_iterations=32` on the worker — the loop-owning default would
|
| 89 |
silently cap the agent at 8 model calls.
|
| 90 |
+
- `max_staleness=4` (config default) matches the article's async setup.
|
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|
| 91 |
|
| 92 |
## Honest caveats
|
| 93 |
|
| 94 |
+
- `HarnessRolloutWorker` + `peft_config` and `sandbox="hf-sandbox"` are both
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| 95 |
+
untested-in-print combinations on this stack. All pieces are released and the
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| 96 |
+
interfaces line up (verified in source) — the smoke run exists to prove it before
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| 97 |
+
you spend 32+ GPU-hours.
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| 98 |
+
- HF Sandboxes are experimental (per HF docs) and each rollout is an HF Job under
|
| 99 |
+
your token: mind your Jobs concurrency quota (`MAX_CONCURRENT_ENVS=8` bounds it)
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| 100 |
+
and per-minute billing of thousands of short cpu-basic jobs over a full run.
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| 101 |
+
- Sandbox boot time (job scheduling + first image pull) adds latency per rollout
|
| 102 |
+
vs Daytona/E2B's warm pools; expect slower wall-clock than the article's 32h.
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| 103 |
- Expect the article's single-harness shape: gains concentrate in OpenCode
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| 104 |
(34 → 58% there in their run) and transfer unevenly elsewhere.
|
| 105 |
- LoRA r=32 may cap peak accuracy vs full FT; no published comparison on this stack.
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data/test.json
ADDED
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| 1 |
+
[
|
| 2 |
+
{"name": "0000_324_324293_qa_2", "source": "0000/324/324293.ipynb_qa_2", "notebook": "0000/324/324293.ipynb", "difficulty": "medium", "instruction_sha256": "03bd60bb664c4cc96b3ab85db3e3a18039dcfbfee06d3935696f002a2bb26bc5"},
|
| 3 |
+
{"name": "0000_340_340599_qa_1", "source": "0000/340/340599.ipynb_qa_1", "notebook": "0000/340/340599.ipynb", "difficulty": "medium", "instruction_sha256": "dd7911b3032aa469ef4f7cd9c71748c62cb560dcfc2bcc897e6111f4197f0383"},
|
| 4 |
+
{"name": "0000_340_340599_qa_3", "source": "0000/340/340599.ipynb_qa_3", "notebook": "0000/340/340599.ipynb", "difficulty": "easy", "instruction_sha256": "dac579f315382a7411af51143da96cfddafa1a68a0271d8e80e7da09a2e49d8c"},
|
| 5 |
+
{"name": "0000_347_347102_qa_1", "source": "0000/347/347102.ipynb_qa_1", "notebook": "0000/347/347102.ipynb", "difficulty": "medium", "instruction_sha256": "75ecfd8d28ed2da9ab44452168e9d8a84edcf7f24ca532ae0bc5f1c122cd5868"},
|
| 6 |
+
{"name": "0000_376_376301_qa_1", "source": "0000/376/376301.ipynb_qa_1", "notebook": "0000/376/376301.ipynb", "difficulty": "hard", "instruction_sha256": "9fbf3484b5169e2dcdcdc52b8022796ae1186c5a6f82d0116726a1311a86c556"},
|
| 7 |
+
{"name": "0000_407_407872_qa_4", "source": "0000/407/407872.ipynb_qa_4", "notebook": "0000/407/407872.ipynb", "difficulty": "medium", "instruction_sha256": "5e9308a8d05b4c1ee0e80a6d875574f2a7a63f18d1e299920bbc111aa78ad9a5"},
|
| 8 |
+
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| 212 |
+
{"name": "0001_875_1875067_qa_3", "source": "0001/875/1875067.ipynb_qa_3", "notebook": "0001/875/1875067.ipynb", "difficulty": "hard", "instruction_sha256": "0a7f4c30e394b577f8b0222c643047494a68d82bd0b17d859f9924a4139539c5"},
|
| 213 |
+
{"name": "0001_892_1892658_qa_3", "source": "0001/892/1892658.ipynb_qa_3", "notebook": "0001/892/1892658.ipynb", "difficulty": "hard", "instruction_sha256": "fc99f18b9f27e2c5aa143bed56acffe6e6897b68f5435c78659e456c24e0640e"},
|
| 214 |
+
{"name": "0001_893_1893978_qa_5", "source": "0001/893/1893978.ipynb_qa_5", "notebook": "0001/893/1893978.ipynb", "difficulty": "hard", "instruction_sha256": "73b861c118c1bdf4a9bf5cac14f52422f69f4412de02e3c9338552542689e31a"},
|
| 215 |
+
{"name": "0001_895_1895893_qa_2", "source": "0001/895/1895893.ipynb_qa_2", "notebook": "0001/895/1895893.ipynb", "difficulty": "hard", "instruction_sha256": "6132d4a5dcb443f53cb6d53348a1a76d56d95b9af841f88f42f9f847f934f92b"},
|
| 216 |
+
{"name": "0001_903_1903634_qa_1", "source": "0001/903/1903634.ipynb_qa_1", "notebook": "0001/903/1903634.ipynb", "difficulty": "hard", "instruction_sha256": "9943c21454817f59a1b0a64a64802d7d7d3d876a63e3bb9a8ef7a17fdcedfc80"},
|
| 217 |
+
{"name": "0001_903_1903634_qa_4", "source": "0001/903/1903634.ipynb_qa_4", "notebook": "0001/903/1903634.ipynb", "difficulty": "hard", "instruction_sha256": "bc69bbc99fe61207b27eda417f0304091e95c82f18bd8c3d69505ce0e6203456"},
|
| 218 |
+
{"name": "0001_903_1903634_qa_5", "source": "0001/903/1903634.ipynb_qa_5", "notebook": "0001/903/1903634.ipynb", "difficulty": "hard", "instruction_sha256": "dfd05c52775022a3a62f8506c5124e61d2264900a31bc742d6969f5c8da7d1a7"},
|
| 219 |
+
{"name": "0001_904_1904638_qa_1", "source": "0001/904/1904638.ipynb_qa_1", "notebook": "0001/904/1904638.ipynb", "difficulty": "hard", "instruction_sha256": "47a1a9cfe9326e2f6bfc7592637a473eb2c195fc31f0e722f3a176d60be32baf"},
|
| 220 |
+
{"name": "0001_904_1904638_qa_2", "source": "0001/904/1904638.ipynb_qa_2", "notebook": "0001/904/1904638.ipynb", "difficulty": "hard", "instruction_sha256": "69c8eedddd4580a3cb1ac73d6b93911b7e3be20aea7d8d7079fac7dae148785d"},
|
| 221 |
+
{"name": "0001_912_1912867_qa_2", "source": "0001/912/1912867.ipynb_qa_2", "notebook": "0001/912/1912867.ipynb", "difficulty": "hard", "instruction_sha256": "7d7d5fe2651b4ab7cb0f28d7f5b07fd249e4d64c136e68a4f91bd30923042925"},
|
| 222 |
+
{"name": "0001_912_1912867_qa_5", "source": "0001/912/1912867.ipynb_qa_5", "notebook": "0001/912/1912867.ipynb", "difficulty": "hard", "instruction_sha256": "03e34fcaeab8293895ab6769b24729fceae98e25c019963214c9cd56b1514de8"},
|
| 223 |
+
{"name": "0001_918_1918012_qa_1", "source": "0001/918/1918012.ipynb_qa_1", "notebook": "0001/918/1918012.ipynb", "difficulty": "hard", "instruction_sha256": "413ec54174c4fee088e32ea8fa2304eab6383ead00a0ad8b649d02c8ff19d060"},
|
| 224 |
+
{"name": "0001_922_1922748_qa_4", "source": "0001/922/1922748.ipynb_qa_4", "notebook": "0001/922/1922748.ipynb", "difficulty": "hard", "instruction_sha256": "1d0234376e455d67f95f2e112ab1e9be75814ecf85182614d60292529cca4cec"},
|
| 225 |
+
{"name": "0001_991_1991000_qa_3", "source": "0001/991/1991000.ipynb_qa_3", "notebook": "0001/991/1991000.ipynb", "difficulty": "hard", "instruction_sha256": "1cb0f2552b0eff9384273ded32b00f1a609f5840dd1ff516ee6b2820f434b55f"},
|
| 226 |
+
{"name": "0001_991_1991376_qa_1", "source": "0001/991/1991376.ipynb_qa_1", "notebook": "0001/991/1991376.ipynb", "difficulty": "hard", "instruction_sha256": "3e7e7c3a681602573090f8446187bbe6615ee1c5a043733c59b7231b075468ad"},
|
| 227 |
+
{"name": "0001_991_1991376_qa_5", "source": "0001/991/1991376.ipynb_qa_5", "notebook": "0001/991/1991376.ipynb", "difficulty": "hard", "instruction_sha256": "a0aba7aa773ec3c3352f677b70131c33083b135e3d0bd776d525a3f81ecfb435"},
|
| 228 |
+
{"name": "0002_134_2134700_qa_5", "source": "0002/134/2134700.ipynb_qa_5", "notebook": "0002/134/2134700.ipynb", "difficulty": "hard", "instruction_sha256": "d2bd38963709deb5780643df0b05fd1bb8491da390a3c0b400ae35fc8d16db54"},
|
| 229 |
+
{"name": "0002_169_2169008_qa_1", "source": "0002/169/2169008.ipynb_qa_1", "notebook": "0002/169/2169008.ipynb", "difficulty": "hard", "instruction_sha256": "1ec8adf67ef5d0bbf5bc364bbc7c51a06815f721736560d9a664997f756bf100"},
|
| 230 |
+
{"name": "0002_187_2187459_qa_3", "source": "0002/187/2187459.ipynb_qa_3", "notebook": "0002/187/2187459.ipynb", "difficulty": "hard", "instruction_sha256": "70b17eebda3078b92ef0b110633ced726155d4722af943a520dcdf021b09e645"},
|
| 231 |
+
{"name": "0002_189_2189266_qa_2", "source": "0002/189/2189266.ipynb_qa_2", "notebook": "0002/189/2189266.ipynb", "difficulty": "hard", "instruction_sha256": "57caf0bf7a5e06ccbf95d0749c7456e092cb472e32ea7d54e3ddc29795b22548"},
|
| 232 |
+
{"name": "0014_516_14516985_qa_4", "source": "0014/516/14516985.ipynb_qa_4", "notebook": "0014/516/14516985.ipynb", "difficulty": "hard", "instruction_sha256": "6097e844a6f616ec28fc8c8ddcd5defd6d794cd01c729d66ada1c2aa4a30812e"},
|
| 233 |
+
{"name": "0032_521_32521554_qa_3", "source": "0032/521/32521554.ipynb_qa_3", "notebook": "0032/521/32521554.ipynb", "difficulty": "hard", "instruction_sha256": "67a31e3f106b773fd6c1225f700f30be451668cac4a0aebebc57e2f261bb9571"},
|
| 234 |
+
{"name": "0036_284_36284344_qa_5", "source": "0036/284/36284344.ipynb_qa_5", "notebook": "0036/284/36284344.ipynb", "difficulty": "hard", "instruction_sha256": "dc64eec5ba515a04b10f692ec9a102389309c2d2015995e5de59f61645fcb49b"},
|
| 235 |
+
{"name": "0041_214_41214590_qa_4", "source": "0041/214/41214590.ipynb_qa_4", "notebook": "0041/214/41214590.ipynb", "difficulty": "hard", "instruction_sha256": "1b60982b46ee4257d89781b0d8411fd5e9de960223680028457bce8f002f2316"},
|
| 236 |
+
{"name": "0048_996_48996091_qa_5", "source": "0048/996/48996091.ipynb_qa_5", "notebook": "0048/996/48996091.ipynb", "difficulty": "hard", "instruction_sha256": "adf2ebed947e03a15c108ed522b720b068371f65629c7c9f719e2a6c877ba745"},
|
| 237 |
+
{"name": "0049_302_49302290_qa_3", "source": "0049/302/49302290.ipynb_qa_3", "notebook": "0049/302/49302290.ipynb", "difficulty": "hard", "instruction_sha256": "ba6cf04cb5182218a7bdffb662e3384bfc9cb9c38b367c37849a9eb2b42fa3b6"},
|
| 238 |
+
{"name": "0051_018_51018932_qa_4", "source": "0051/018/51018932.ipynb_qa_4", "notebook": "0051/018/51018932.ipynb", "difficulty": "hard", "instruction_sha256": "299852a89a08120ce05b91762ce1e13467107d00c3eb23ffd76c24fc5a774612"},
|
| 239 |
+
{"name": "0060_085_60085489_qa_4", "source": "0060/085/60085489.ipynb_qa_4", "notebook": "0060/085/60085489.ipynb", "difficulty": "hard", "instruction_sha256": "db2a6a0d4db73ddfce498cb4bae9542c73849ccb39fa6d43862bbee0457858c9"},
|
| 240 |
+
{"name": "0064_089_64089732_qa_5", "source": "0064/089/64089732.ipynb_qa_5", "notebook": "0064/089/64089732.ipynb", "difficulty": "hard", "instruction_sha256": "01459031a14193b4fbe987f4e4c1466488f33b4283451476e04dda4d5906aab1"},
|
| 241 |
+
{"name": "0065_262_65262139_qa_5", "source": "0065/262/65262139.ipynb_qa_5", "notebook": "0065/262/65262139.ipynb", "difficulty": "hard", "instruction_sha256": "0ab09f6bd0d928239f60bcde97931436721925aa77348c2d91ca342518cbb68e"},
|
| 242 |
+
{"name": "0075_374_75374659_qa_2", "source": "0075/374/75374659.ipynb_qa_2", "notebook": "0075/374/75374659.ipynb", "difficulty": "hard", "instruction_sha256": "3323a5edef46c2ffe235d7a7a215cfecda1601808e48ae91eabecffa13f65ab0"},
|
| 243 |
+
{"name": "0092_637_92637915_qa_2", "source": "0092/637/92637915.ipynb_qa_2", "notebook": "0092/637/92637915.ipynb", "difficulty": "hard", "instruction_sha256": "902721593434f0b3ed7435c7ac9c0c2d09799697e1aa03e4f33f3b7ab141ba53"},
|
| 244 |
+
{"name": "0096_776_96776427_qa_5", "source": "0096/776/96776427.ipynb_qa_5", "notebook": "0096/776/96776427.ipynb", "difficulty": "hard", "instruction_sha256": "cb45bd02629e73b9fc7ea3629d1300a521ec74b9bcb89392ae59e8792cc1356d"},
|
| 245 |
+
{"name": "0099_089_99089980_qa_5", "source": "0099/089/99089980.ipynb_qa_5", "notebook": "0099/089/99089980.ipynb", "difficulty": "hard", "instruction_sha256": "e934a92dd25e11ac99781af75ab5433c4c8804cfb8a4fd4ead47b9b24b954227"},
|
| 246 |
+
{"name": "0104_079_104079493_qa_4", "source": "0104/079/104079493.ipynb_qa_4", "notebook": "0104/079/104079493.ipynb", "difficulty": "hard", "instruction_sha256": "906e1a55aec2a70fa6c5310a87f36d0d6121fbce5c3ac32a4641f1c765d0c06d"},
|
| 247 |
+
{"name": "0115_735_115735943_qa_1", "source": "0115/735/115735943.ipynb_qa_1", "notebook": "0115/735/115735943.ipynb", "difficulty": "hard", "instruction_sha256": "dbfafc2ca0f02cbc4b1e07e68b62efde9450653099780475121f1304c465b269"},
|
| 248 |
+
{"name": "0117_122_117122383_qa_3", "source": "0117/122/117122383.ipynb_qa_3", "notebook": "0117/122/117122383.ipynb", "difficulty": "hard", "instruction_sha256": "bd17eea23c6cb3ae28e35ede16102611d27634015e351807154ff37fe43cb4b1"},
|
| 249 |
+
{"name": "0122_938_122938717_qa_4", "source": "0122/938/122938717.ipynb_qa_4", "notebook": "0122/938/122938717.ipynb", "difficulty": "hard", "instruction_sha256": "ba8b1221265a98d6ced0f68c49dd8fb8ea04e2afc03733b77475c3ee98ec353c"},
|
| 250 |
+
{"name": "0126_223_126223416_qa_5", "source": "0126/223/126223416.ipynb_qa_5", "notebook": "0126/223/126223416.ipynb", "difficulty": "hard", "instruction_sha256": "bf9fdef8013dfd12241731291f01a41c77d85f4f0f423f4612147d71949a74f2"},
|
| 251 |
+
{"name": "0138_500_138500445_qa_1", "source": "0138/500/138500445.ipynb_qa_1", "notebook": "0138/500/138500445.ipynb", "difficulty": "hard", "instruction_sha256": "f1ecbcaab9a48a1ab5481192443d0749968e40988ca0f7df06ffc13230ac5277"}
|
| 252 |
+
]
|
data/train.json
ADDED
|
The diff for this file is too large to render.
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|
|
|
prepare_tasks.py
ADDED
|
@@ -0,0 +1,111 @@
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|
|
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|
|
|
|
| 1 |
+
"""Materialize the pinned 1,000 train / 250 test SmolDataEnvs tasks for HF Sandboxes.
|
| 2 |
+
|
| 3 |
+
Adapted from the FineEnvs env Space's prepare.py (same pinned revisions, same
|
| 4 |
+
instruction and grader hash checks), with one change: every task.toml gets
|
| 5 |
+
|
| 6 |
+
[environment] docker_image = "docker.io/savatar101/env-data-agent-train:base"
|
| 7 |
+
|
| 8 |
+
because Harbor's hf-sandbox backend needs a prebuilt image and will not build the
|
| 9 |
+
task's environment/Dockerfile. The Dockerfile is a one-line FROM of exactly this
|
| 10 |
+
image, so the sandbox runs bit-identical content.
|
| 11 |
+
|
| 12 |
+
Run once at job startup, then point OPENENV_DATASETS at the output:
|
| 13 |
+
|
| 14 |
+
python prepare_tasks.py --output /app/prepared
|
| 15 |
+
export OPENENV_DATASETS=/app/prepared/datasets/train,/app/prepared/datasets/test
|
| 16 |
+
"""
|
| 17 |
+
|
| 18 |
+
from __future__ import annotations
|
| 19 |
+
|
| 20 |
+
import argparse
|
| 21 |
+
import hashlib
|
| 22 |
+
import json
|
| 23 |
+
import shutil
|
| 24 |
+
import tomllib
|
| 25 |
+
from pathlib import Path
|
| 26 |
+
|
| 27 |
+
ROOT = Path(__file__).resolve().parent
|
| 28 |
+
|
| 29 |
+
# All tasks share this one base image (their Dockerfile is `FROM <this>` + WORKDIR).
|
| 30 |
+
DOCKER_IMAGE = "docker.io/savatar101/env-data-agent-train:base"
|
| 31 |
+
|
| 32 |
+
DATASETS = {
|
| 33 |
+
"train": (
|
| 34 |
+
"FineEnvs/SmolDataEnvs-harbor-train",
|
| 35 |
+
"4719635555de1666f374d847baebb500d368e493",
|
| 36 |
+
),
|
| 37 |
+
"test": (
|
| 38 |
+
"FineEnvs/SmolDataEnvs-harbor-test",
|
| 39 |
+
"b130595b579fc6026ad5762925a9a3b40e72f5bb",
|
| 40 |
+
),
|
| 41 |
+
}
|
| 42 |
+
GRADER_SHA256 = "16f906e9248fa8b7e1141939e425e88e59d52d65e98e360bbacef3720a43629f"
|
| 43 |
+
|
| 44 |
+
|
| 45 |
+
def task_rows(split):
|
| 46 |
+
return json.loads((ROOT / "data" / f"{split}.json").read_text())
|
| 47 |
+
|
| 48 |
+
|
| 49 |
+
def prepare(destination):
|
| 50 |
+
import tomli_w
|
| 51 |
+
from huggingface_hub import snapshot_download
|
| 52 |
+
|
| 53 |
+
destination = Path(destination).resolve()
|
| 54 |
+
for split in ("train", "test"):
|
| 55 |
+
rows = task_rows(split)
|
| 56 |
+
repo, revision = DATASETS[split]
|
| 57 |
+
source = Path(
|
| 58 |
+
snapshot_download(
|
| 59 |
+
repo,
|
| 60 |
+
repo_type="dataset",
|
| 61 |
+
revision=revision,
|
| 62 |
+
allow_patterns=[f"tasks/{r['name']}/*" for r in rows],
|
| 63 |
+
max_workers=16,
|
| 64 |
+
)
|
| 65 |
+
)
|
| 66 |
+
for row in rows:
|
| 67 |
+
folder = source / "tasks" / row["name"]
|
| 68 |
+
instruction = (folder / "instruction.md").read_bytes()
|
| 69 |
+
if hashlib.sha256(instruction).hexdigest() != row["instruction_sha256"]:
|
| 70 |
+
raise ValueError(f"Historical task instruction changed: {split}/{row['name']}")
|
| 71 |
+
grader = (folder / "tests/grader.py").read_bytes()
|
| 72 |
+
if hashlib.sha256(grader).hexdigest() != GRADER_SHA256:
|
| 73 |
+
raise ValueError(f"Expected corrected numeric-only Harbor grader: {folder}")
|
| 74 |
+
target = destination / "datasets" / split / "tasks" / row["name"]
|
| 75 |
+
target.mkdir(parents=True, exist_ok=True)
|
| 76 |
+
for rel in (
|
| 77 |
+
"instruction.md",
|
| 78 |
+
"task.toml",
|
| 79 |
+
"environment/Dockerfile",
|
| 80 |
+
"environment/pull_bucket.py",
|
| 81 |
+
"tests/grader.py",
|
| 82 |
+
"tests/test.sh",
|
| 83 |
+
):
|
| 84 |
+
dst = target / rel
|
| 85 |
+
dst.parent.mkdir(parents=True, exist_ok=True)
|
| 86 |
+
shutil.copyfile(folder / rel, dst)
|
| 87 |
+
spec = tomllib.loads((target / "task.toml").read_text())
|
| 88 |
+
spec["environment"]["memory_mb"] = 4096
|
| 89 |
+
spec["environment"]["cpus"] = 1
|
| 90 |
+
# hf-sandbox runs a prebuilt image instead of building environment/Dockerfile.
|
| 91 |
+
spec["environment"]["docker_image"] = DOCKER_IMAGE
|
| 92 |
+
# Cluster user IDs may not exist inside the sandbox user namespace.
|
| 93 |
+
spec["environment"].setdefault("env", {})["TAR_OPTIONS"] = "--no-same-owner"
|
| 94 |
+
(target / "task.toml").write_text(tomli_w.dumps(spec))
|
| 95 |
+
print(f"{split}: {len(rows)} tasks -> {destination / 'datasets' / split}")
|
| 96 |
+
# Overlap guard, as in the original.
|
| 97 |
+
def names(split):
|
| 98 |
+
return {r["name"] for r in task_rows(split)}
|
| 99 |
+
notebooks = [
|
| 100 |
+
{r.get("notebook") for r in task_rows(s)} - {None} for s in ("train", "test")
|
| 101 |
+
]
|
| 102 |
+
if names("train") & names("test") or notebooks[0] & notebooks[1]:
|
| 103 |
+
raise ValueError("Train/test overlap")
|
| 104 |
+
print("no train/test overlap")
|
| 105 |
+
|
| 106 |
+
|
| 107 |
+
if __name__ == "__main__":
|
| 108 |
+
p = argparse.ArgumentParser(description=__doc__)
|
| 109 |
+
p.add_argument("--output", type=Path, default=ROOT / "prepared")
|
| 110 |
+
a = p.parse_args()
|
| 111 |
+
prepare(a.output)
|
run_job.sh
CHANGED
|
@@ -4,13 +4,14 @@
|
|
| 4 |
# GPU 1: vLLM policy server (serve_policy.sh)
|
| 5 |
# CPU: OpenEnv Harbor env server (serve_env.sh)
|
| 6 |
# GPU 0: TRL AsyncGRPOTrainer (train_lora.py)
|
|
|
|
|
|
|
| 7 |
#
|
| 8 |
# Expected env (via `hf jobs run --secrets/--env`):
|
| 9 |
-
# HF_TOKEN (secret) hub downloads
|
| 10 |
-
# DAYTONA_API_KEY (secret) sandboxes — or E2B_API_KEY with SANDBOX=e2b
|
| 11 |
# ADAPTER_REPO (env) Hub repo that receives adapter checkpoints
|
| 12 |
# MAX_STEPS (env) 20 for the smoke run, 1000 for the full run
|
| 13 |
-
# SANDBOX (env)
|
| 14 |
set -euo pipefail
|
| 15 |
cd "$(dirname "$0")"
|
| 16 |
|
|
@@ -21,8 +22,8 @@ python -m pip install --quiet 'transformers>=5.2.0' --no-deps
|
|
| 21 |
python -m pip install --quiet \
|
| 22 |
'trl[peft]>=1.16.0' \
|
| 23 |
'openenv[harbor]' \
|
| 24 |
-
'harbor[
|
| 25 |
-
math-verify \
|
| 26 |
aiohttp
|
| 27 |
|
| 28 |
echo "=== [setup] OpenEnv repo (harbor_env lives in envs/, not the wheel) ==="
|
|
@@ -30,6 +31,9 @@ git clone --depth 1 https://github.com/huggingface/OpenEnv.git /opt/OpenEnv
|
|
| 30 |
SITE=$(python -c "import site; print(site.getsitepackages()[0])")
|
| 31 |
echo "/opt/OpenEnv/envs" > "$SITE/openenv_examples.pth"
|
| 32 |
|
|
|
|
|
|
|
|
|
|
| 33 |
echo "=== [check] imports ==="
|
| 34 |
python - <<'EOF'
|
| 35 |
import trl, openenv, harbor_env # noqa: F401
|
|
@@ -48,7 +52,7 @@ vllm serve "${MODEL:-LiquidAI/LFM2.5-2.6B}" \
|
|
| 48 |
VLLM_PID=$!
|
| 49 |
|
| 50 |
echo "=== [start] env server (CPU) ==="
|
| 51 |
-
bash serve_env.sh &
|
| 52 |
ENV_PID=$!
|
| 53 |
|
| 54 |
echo "=== [wait] servers ==="
|
|
@@ -68,7 +72,10 @@ for name, url in [("vllm", "http://127.0.0.1:8001/v1/models"),
|
|
| 68 |
EOF
|
| 69 |
|
| 70 |
echo "=== [train] GPU 0, MAX_STEPS=${MAX_STEPS:-1000} ==="
|
| 71 |
-
CUDA_VISIBLE_DEVICES=0
|
|
|
|
|
|
|
|
|
|
| 72 |
python train_lora.py
|
| 73 |
|
| 74 |
kill $VLLM_PID $ENV_PID 2>/dev/null || true
|
|
|
|
| 4 |
# GPU 1: vLLM policy server (serve_policy.sh)
|
| 5 |
# CPU: OpenEnv Harbor env server (serve_env.sh)
|
| 6 |
# GPU 0: TRL AsyncGRPOTrainer (train_lora.py)
|
| 7 |
+
# Sandboxes: Harbor's hf-sandbox backend — each rollout boots an HF Jobs
|
| 8 |
+
# cpu-basic machine under the same HF token. No Daytona/E2B account needed.
|
| 9 |
#
|
| 10 |
# Expected env (via `hf jobs run --secrets/--env`):
|
| 11 |
+
# HF_TOKEN (secret) hub downloads, checkpoint pushes, sandbox Jobs
|
|
|
|
| 12 |
# ADAPTER_REPO (env) Hub repo that receives adapter checkpoints
|
| 13 |
# MAX_STEPS (env) 20 for the smoke run, 1000 for the full run
|
| 14 |
+
# SANDBOX (env) hf-sandbox (default) | daytona | e2b
|
| 15 |
set -euo pipefail
|
| 16 |
cd "$(dirname "$0")"
|
| 17 |
|
|
|
|
| 22 |
python -m pip install --quiet \
|
| 23 |
'trl[peft]>=1.16.0' \
|
| 24 |
'openenv[harbor]' \
|
| 25 |
+
'harbor[hf-sandbox]>=0.24.0' \
|
| 26 |
+
math-verify tomli-w \
|
| 27 |
aiohttp
|
| 28 |
|
| 29 |
echo "=== [setup] OpenEnv repo (harbor_env lives in envs/, not the wheel) ==="
|
|
|
|
| 31 |
SITE=$(python -c "import site; print(site.getsitepackages()[0])")
|
| 32 |
echo "/opt/OpenEnv/envs" > "$SITE/openenv_examples.pth"
|
| 33 |
|
| 34 |
+
echo "=== [setup] pinned tasks (1,000 train / 250 test, hash-checked) ==="
|
| 35 |
+
python prepare_tasks.py --output /app/prepared
|
| 36 |
+
|
| 37 |
echo "=== [check] imports ==="
|
| 38 |
python - <<'EOF'
|
| 39 |
import trl, openenv, harbor_env # noqa: F401
|
|
|
|
| 52 |
VLLM_PID=$!
|
| 53 |
|
| 54 |
echo "=== [start] env server (CPU) ==="
|
| 55 |
+
PREPARED=/app/prepared bash serve_env.sh &
|
| 56 |
ENV_PID=$!
|
| 57 |
|
| 58 |
echo "=== [wait] servers ==="
|
|
|
|
| 72 |
EOF
|
| 73 |
|
| 74 |
echo "=== [train] GPU 0, MAX_STEPS=${MAX_STEPS:-1000} ==="
|
| 75 |
+
CUDA_VISIBLE_DEVICES=0 \
|
| 76 |
+
VLLM_URL=http://127.0.0.1:8001 \
|
| 77 |
+
ENV_URL=http://127.0.0.1:8000 \
|
| 78 |
+
TRAIN_SPLIT=/app/prepared/datasets/train \
|
| 79 |
python train_lora.py
|
| 80 |
|
| 81 |
kill $VLLM_PID $ENV_PID 2>/dev/null || true
|
serve_env.sh
CHANGED
|
@@ -1,20 +1,25 @@
|
|
| 1 |
#!/usr/bin/env bash
|
| 2 |
# OpenEnv Harbor environment server: Task API + capture proxy + web UI.
|
| 3 |
-
# Sandboxes
|
|
|
|
|
|
|
| 4 |
#
|
| 5 |
# Concurrency and capture settings are env vars, not CLI flags (verified against
|
| 6 |
# openenv 0.10.0 and the FineEnvs production env Space). MAX_CONCURRENT_ENVS must
|
| 7 |
-
#
|
|
|
|
| 8 |
set -euo pipefail
|
| 9 |
|
| 10 |
-
|
|
|
|
|
|
|
| 11 |
export OPENENV_MAX_OUTPUT_TOKENS=4096 # same cap as training + eval
|
| 12 |
export OPENENV_HARBOR_REWARD_KEY="correctness,reward"
|
| 13 |
-
# Pin the harness build so rollouts are reproducible (
|
| 14 |
export OPENENV_HARBOR_AGENT_VERSIONS='{"opencode":"1.18.31"}'
|
| 15 |
|
| 16 |
openenv harbor serve \
|
| 17 |
-
--dataset
|
| 18 |
--port "${ENV_PORT:-8000}" \
|
| 19 |
--capture-port "${CAPTURE_PORT:-8100}" \
|
| 20 |
--expose gradio
|
|
|
|
| 1 |
#!/usr/bin/env bash
|
| 2 |
# OpenEnv Harbor environment server: Task API + capture proxy + web UI.
|
| 3 |
+
# Sandboxes are HF Sandboxes (Harbor's hf-sandbox backend: each rollout boots an
|
| 4 |
+
# HF Jobs cpu-basic machine running the task's prebuilt image). Only HF_TOKEN is
|
| 5 |
+
# needed — no Daytona/E2B account.
|
| 6 |
#
|
| 7 |
# Concurrency and capture settings are env vars, not CLI flags (verified against
|
| 8 |
# openenv 0.10.0 and the FineEnvs production env Space). MAX_CONCURRENT_ENVS must
|
| 9 |
+
# be >= the GRPO group size (8) or rollouts are rejected with CAPACITY_REACHED —
|
| 10 |
+
# it also bounds how many sandbox Jobs run at once under your HF account.
|
| 11 |
set -euo pipefail
|
| 12 |
|
| 13 |
+
PREPARED="${PREPARED:-/app/prepared}"
|
| 14 |
+
export OPENENV_DATASETS="${OPENENV_DATASETS:-$PREPARED/datasets/train,$PREPARED/datasets/test}"
|
| 15 |
+
export MAX_CONCURRENT_ENVS="${MAX_CONCURRENT_ENVS:-8}"
|
| 16 |
export OPENENV_MAX_OUTPUT_TOKENS=4096 # same cap as training + eval
|
| 17 |
export OPENENV_HARBOR_REWARD_KEY="correctness,reward"
|
| 18 |
+
# Pin the harness build so rollouts are reproducible (version from the FineEnvs Space).
|
| 19 |
export OPENENV_HARBOR_AGENT_VERSIONS='{"opencode":"1.18.31"}'
|
| 20 |
|
| 21 |
openenv harbor serve \
|
| 22 |
+
--dataset "$OPENENV_DATASETS" \
|
| 23 |
--port "${ENV_PORT:-8000}" \
|
| 24 |
--capture-port "${CAPTURE_PORT:-8100}" \
|
| 25 |
--expose gradio
|
train_lora.py
CHANGED
|
@@ -20,7 +20,7 @@ from the harness's own ATIF trace.
|
|
| 20 |
import os
|
| 21 |
from functools import partial
|
| 22 |
|
| 23 |
-
from datasets import
|
| 24 |
from peft import LoraConfig
|
| 25 |
from transformers import AutoTokenizer
|
| 26 |
from trl.experimental.async_grpo.async_grpo_config import AsyncGRPOConfig
|
|
@@ -32,8 +32,8 @@ from harbor_env.harness import HarborSessionFactory # via .pth to OpenEnv/envs
|
|
| 32 |
MODEL = "LiquidAI/LFM2.5-2.6B"
|
| 33 |
VLLM_URL = os.environ.get("VLLM_URL", "http://127.0.0.1:8001") # serve_policy.sh
|
| 34 |
ENV_URL = os.environ.get("ENV_URL", "http://127.0.0.1:8000") # serve_env.sh
|
| 35 |
-
TRAIN_SPLIT = "
|
| 36 |
-
SANDBOX = os.environ.get("SANDBOX", "
|
| 37 |
MAX_STEPS = int(os.environ.get("MAX_STEPS", "1000"))
|
| 38 |
ADAPTER_REPO = os.environ.get("ADAPTER_REPO", "") # push checkpoints here
|
| 39 |
|
|
@@ -54,8 +54,8 @@ def correctness_plus_efficiency(outcome):
|
|
| 54 |
return reward + min(bonus, MAX_BONUS)
|
| 55 |
|
| 56 |
|
| 57 |
-
def
|
| 58 |
-
|
| 59 |
HarborSessionFactory,
|
| 60 |
server_url=ENV_URL,
|
| 61 |
split=TRAIN_SPLIT,
|
|
@@ -65,8 +65,11 @@ def make_rollout_worker(dataset, tokenizer):
|
|
| 65 |
sandbox=SANDBOX,
|
| 66 |
reward_key="correctness,reward",
|
| 67 |
)
|
|
|
|
|
|
|
|
|
|
| 68 |
return HarnessRolloutWorker(
|
| 69 |
-
harness_session_factory=
|
| 70 |
harness_adapter=None, # let the harness own the loop (black-box capture)
|
| 71 |
rollout_reward_fn=correctness_plus_efficiency,
|
| 72 |
model_name=MODEL,
|
|
@@ -88,7 +91,13 @@ def make_rollout_worker(dataset, tokenizer):
|
|
| 88 |
|
| 89 |
def main():
|
| 90 |
tokenizer = AutoTokenizer.from_pretrained(MODEL)
|
| 91 |
-
dataset
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 92 |
worker = make_rollout_worker(dataset, tokenizer)
|
| 93 |
|
| 94 |
config_kwargs = {
|
|
|
|
| 20 |
import os
|
| 21 |
from functools import partial
|
| 22 |
|
| 23 |
+
from datasets import Dataset
|
| 24 |
from peft import LoraConfig
|
| 25 |
from transformers import AutoTokenizer
|
| 26 |
from trl.experimental.async_grpo.async_grpo_config import AsyncGRPOConfig
|
|
|
|
| 32 |
MODEL = "LiquidAI/LFM2.5-2.6B"
|
| 33 |
VLLM_URL = os.environ.get("VLLM_URL", "http://127.0.0.1:8001") # serve_policy.sh
|
| 34 |
ENV_URL = os.environ.get("ENV_URL", "http://127.0.0.1:8000") # serve_env.sh
|
| 35 |
+
TRAIN_SPLIT = os.environ.get("TRAIN_SPLIT", "/app/prepared/datasets/train")
|
| 36 |
+
SANDBOX = os.environ.get("SANDBOX", "hf-sandbox") # Harbor backend
|
| 37 |
MAX_STEPS = int(os.environ.get("MAX_STEPS", "1000"))
|
| 38 |
ADAPTER_REPO = os.environ.get("ADAPTER_REPO", "") # push checkpoints here
|
| 39 |
|
|
|
|
| 54 |
return reward + min(bonus, MAX_BONUS)
|
| 55 |
|
| 56 |
|
| 57 |
+
def make_factory():
|
| 58 |
+
return partial(
|
| 59 |
HarborSessionFactory,
|
| 60 |
server_url=ENV_URL,
|
| 61 |
split=TRAIN_SPLIT,
|
|
|
|
| 65 |
sandbox=SANDBOX,
|
| 66 |
reward_key="correctness,reward",
|
| 67 |
)
|
| 68 |
+
|
| 69 |
+
|
| 70 |
+
def make_rollout_worker(dataset, tokenizer):
|
| 71 |
return HarnessRolloutWorker(
|
| 72 |
+
harness_session_factory=make_factory(),
|
| 73 |
harness_adapter=None, # let the harness own the loop (black-box capture)
|
| 74 |
rollout_reward_fn=correctness_plus_efficiency,
|
| 75 |
model_name=MODEL,
|
|
|
|
| 91 |
|
| 92 |
def main():
|
| 93 |
tokenizer = AutoTokenizer.from_pretrained(MODEL)
|
| 94 |
+
# The trainer dataset is discovered from the env server, not loaded from the
|
| 95 |
+
# Hub: HarborSessionFactory.create() only accepts instructions the server
|
| 96 |
+
# itself serves, and prompt_rows() is its blessed export (one row per task,
|
| 97 |
+
# shared by all num_generations of a group).
|
| 98 |
+
discovery = make_factory()()
|
| 99 |
+
dataset = Dataset.from_list(discovery.prompt_rows())
|
| 100 |
+
print(f"discovered {len(dataset)} tasks from {ENV_URL} split={TRAIN_SPLIT}")
|
| 101 |
worker = make_rollout_worker(dataset, tokenizer)
|
| 102 |
|
| 103 |
config_kwargs = {
|