| # Restoring TD-PRM on a new server |
|
|
| This HF dataset repo holds everything needed to bring the project back except what can be downloaded again or |
| is too large to keep: **no model weights, no model checkpoints, no environment, no dataset text.** |
|
|
| | in this repo | what it is | |
| |---|---| |
| | `code/v1/tdprm`, `code/v2/tdprm`, `code/v3/tdprm` | the three code versions (v3 = current; mirrored every sync) | |
| | `tdprm_results.tar` | all of `$TDPRM_HOME` minus weights/envs/dataset text: `runs/` (metrics, logs, traces, configs), `evals/` (every final test incl. samples), `states/` (fitted spectral / OpenJev / JevK5 calibration states), `rollouts/` (base samples, token ids only), `queue/` (plans, queue state, job scripts, logs), `report/` (gates, probes, `services.sh`), `prmbench/` (features, results), `runs_aborted/`, `data/manifest/` and filter summaries, `env.sh`; plus the status pages, the step-credit report and the session notes at the archive root | |
| | `README.md` | the live status page (math / ProverQA) | |
|
|
| | code | version | adds | |
| |---|---|---| |
| | v1 | original pipeline | sampling, spectral/JEV states, latent credit arms, PRM baselines, GRPO (TRL + vLLM), gpuq queue, live reports, HF sync | |
| | v2 | data pipeline v2 | `credit/v2.py`, `rl/pool.py` (skip-solved, hard retest slots), `report/pilot_v2.py` | |
| | v3 | current | `credit/v3.py`, `rl/provider_v3.py`, `states/reader.py` (HC count-back over a transition table), step-credit evaluation (`prmbench/`), Biology/MedQA, Countdown/K&K and commonsense domains, zero-sum process reward, checkpoints | |
|
|
| Job scripts pin their code version (`TDPRM_REPO`): v1 at `/home/colligo/tdprm`, v2 at `/home/colligo/tdprm_v2_repo`, |
| v3 at `/home/colligo/tdprm_v3_repo`. |
|
|
| ## One-shot restore |
|
|
| Requirements: Linux, 8x A100 80 GB (any NVIDIA GPU with driver >= 580 for the CUDA 13 wheels), ~250 GB free for |
| models + data + results (checkpoints of new runs need ~3 GB (Qwen) / 6.4 GB (Llama) each), an HF account that has |
| accepted the Llama-3.2 licence. |
|
|
| ```bash |
| curl -LsSf https://astral.sh/uv/install.sh | sh && export PATH="$HOME/.local/bin:$PATH" |
| uvx --from huggingface_hub hf auth login |
| uvx --from huggingface_hub hf download JoshuaW1997/tdprm-results code/v3/tdprm/scripts/restore_from_hf.sh \ |
| --repo-type dataset --local-dir /tmp/tdprm_restore |
| bash /tmp/tdprm_restore/code/v3/tdprm/scripts/restore_from_hf.sh |
| ``` |
|
|
| Defaults: `TDPRM_HOME=/mnt/localssd/tdprm`, code under `CODE_ROOT=/home/colligo` (other locations work: the script |
| rewrites the absolute paths in `env.sh` and the job scripts). Steps it runs: |
|
|
| 1. downloads this repo; copies the three code trees into place; |
| 2. extracts the results archive into `$TDPRM_HOME`, un-gzips the files the sync compressed (`SYNC_GZIPPED.txt`), |
| puts the status pages / reports / session notes in `$CODE_ROOT`; |
| 3. creates the exact environment with uv from `code/v3/tdprm/requirements.lock.txt` (Python 3.12, torch 2.13 cu130, |
| vLLM 0.29, TRL 1.15, transformers 5.17); |
| 4. downloads all models (`scripts/download_models.sh`: Qwen2.5-1.5B-Instruct, Llama-3.2-3B-Instruct, Qwen2.5-Math-PRM-7B, |
| Qwen2.5-Math-7B-PRM800K, VersaPRM, Qwen3-4B (v3 reader), JevK5, OpenJev); |
| 5. re-prepares every dataset from its public source (`tdprm.data.prepare`) and rebuilds the base-model pass-rate |
| training pools (`data/train/*.parquet`, which cannot be re-sampled identically) from `data/manifest/` -- ordered |
| ids and per-record content hashes. `data_manifest restore` must end with `N/N data files match the manifest`; |
| a mismatch means an upstream dataset changed (the listed files show which); |
| 6. downloads ProcessBench / PRMBench (`tdprm.prmbench.data`). |
|
|
| Then start the services listed in `$TDPRM_HOME/report/services.sh` (queue schedulers, status pages, this HF sync), |
| each in its own detached session. Queue jobs that were running when the project was paused show as `running`: use |
| `python -m tdprm.queue.gpuq --plan <plan> retry <job>` to re-run them. |
|
|
| ## Not restored (by design) |
|
|
| - `runs/*/checkpoints/` (best / final policies): re-evaluating a finished run on a new test set needs a retrain. |
| - Model weights and the Python environment (downloaded / rebuilt above), dataset text (re-prepared above). |
| - Prompts and gold answers inside traces and rollouts (every record keeps its prompt id). |
|
|
