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Pepijn
1394a6ab5d
Following Pi0.7 §V (prompt expansion / diverse context conditioning),
push more atom variants per episode and higher VLM sampling
temperature so the training distribution has enough wording diversity
that the LM head is forced to use its parameters rather than memorise
specific (prompt, target) pairs.
Changes vs prior annotation pass:
* vlm.temperature: 0.2 (default) → 0.7 — every Module-1/2/3 call
now produces diverse phrasings; same prompt yields different
completions across emissions.
* module_1.n_task_rephrasings: 10 → 30 — three times as many
``task_aug`` rows in language_persistent. ``${task}`` already
rotates through them deterministically per sample_idx (see
``_resolve_task`` in language_render.py).
* module_2.max_interjections_per_episode: 3 (default) → 9 — more
``user_interjection_response`` training samples + more plan
refresh events.
* module_3.K: 1 → 3 — three VQA pairs per emission tick instead of
one. Combined with the hz bump below, ~6× more VQA samples.
* module_3.vqa_emission_hz: 1.0 → 2.0 — double the VQA emission
rate within each subtask span.
Pushes to a new hub repo (``_tool3``) so the working ``_tool2``
dataset stays intact for comparison. ``${task}`` already wired to
rotate through ``task_aug`` rows, so no renderer change needed.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
82 lines
3.2 KiB
Python
82 lines
3.2 KiB
Python
#!/usr/bin/env python
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"""Launch ``lerobot-annotate`` on a Hugging Face job (vllm + Qwen3.6 MoE).
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Spawns one ``h200x2`` job that:
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1. installs this branch of ``lerobot`` plus the annotation extras,
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2. boots two vllm servers (one per GPU) with Qwen3.6-35B-A3B-FP8,
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3. runs Module 1/2/3 across the dataset (per-camera VQA via PR 3471),
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4. uploads the annotated dataset to ``--push_to_hub``.
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Usage:
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HF_TOKEN=hf_... uv run python examples/annotation/run_hf_job.py
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Adjust ``CMD`` below to point at your own dataset / target hub repo.
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"""
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import os
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from huggingface_hub import get_token, run_job
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token = os.environ.get("HF_TOKEN") or get_token()
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if not token:
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raise RuntimeError("No HF token. Run `huggingface-cli login` or `export HF_TOKEN=hf_...`")
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# --- Diversity knobs (Pi0.7-style prompt expansion) -----------------------
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# Bumped roughly 3x across the board to fight memorization on small datasets.
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# A single dataset trained for many epochs with deterministic atom wording
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# converges to perfect recall on training prompts but produces JSON-token
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# garbage at inference for any wording that drifts slightly. More atom
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# variants per episode + higher sampling temperature widens the training
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# distribution so the model has to actually use its language head, not
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# just memorize.
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#
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# Pushes to a *new* hub repo (``_tool3``) so the previous annotation pass
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# (``_tool2``) stays intact — re-train from scratch on the new dataset and
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# compare loss-curve shapes to verify the diversity bump is doing something.
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CMD = (
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"apt-get update -qq && apt-get install -y -qq git ffmpeg && "
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"pip install --no-deps "
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"'lerobot @ git+https://github.com/huggingface/lerobot.git@feat/language-annotation-pipeline' && "
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"pip install --upgrade-strategy only-if-needed "
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"datasets pyarrow av jsonlines draccus gymnasium torchcodec mergedeep pyyaml-include toml typing-inspect && "
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"export VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS=0 && "
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"export VLLM_VIDEO_BACKEND=pyav && "
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"lerobot-annotate "
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"--repo_id=imstevenpmwork/super_poulain_draft "
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"--vlm.backend=openai "
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"--vlm.model_id=Qwen/Qwen3.6-35B-A3B-FP8 "
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"--vlm.parallel_servers=2 "
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"--vlm.num_gpus=2 "
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'--vlm.serve_command="vllm serve Qwen/Qwen3.6-35B-A3B-FP8 '
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"--tensor-parallel-size 1 --max-model-len 32768 "
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'--gpu-memory-utilization 0.8 --uvicorn-log-level warning --port {port}" '
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"--vlm.serve_ready_timeout_s=1800 "
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"--vlm.client_concurrency=256 "
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"--vlm.max_new_tokens=512 "
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"--vlm.temperature=0.7 "
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"--executor.episode_parallelism=32 "
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"--vlm.chat_template_kwargs='{\"enable_thinking\": false}' "
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"--vlm.camera_key=observation.images.wrist "
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"--module_1.frames_per_second=1.0 "
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"--module_1.use_video_url=true "
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"--module_1.use_video_url_fps=1.0 "
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"--module_1.derive_task_from_video=always "
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"--module_1.n_task_rephrasings=30 "
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"--module_2.max_interjections_per_episode=9 "
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"--module_3.K=3 "
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"--module_3.vqa_emission_hz=2.0 "
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"--push_to_hub=pepijn223/super_poulain_full_tool3"
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)
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job = run_job(
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image="vllm/vllm-openai:latest",
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command=["bash", "-c", CMD],
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flavor="h200x2",
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secrets={"HF_TOKEN": token},
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timeout="2h",
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)
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print(f"Job URL: {job.url}")
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print(f"Job ID: {job.id}")
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