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pepijn
54221ceea2
Hardcoding ``n_subtask_target=10`` and ``n_memory_target=6`` baked task complexity into the config — a simple pick-and-place needs ~6, a multi-step recipe needs ~20. The VLM already sees the clips, so let it pick the count itself from what's recurring across episodes. Drop both knobs from ``VocabularyConfig`` and the ``module_0_vocabulary`` prompt template. The prompt now says "decide the count yourself based on what you see — the smallest set that still covers every recurring phase" and adds an "each label must recur across the demos" rule so the VLM filters out one-off motions. Update the launcher script + docs to remove the old knobs. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> Co-authored-by: Cursor <cursoragent@cursor.com>
85 lines
3.2 KiB
Python
85 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. discovers the dataset's canonical subtask + memory vocabulary
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from the first 3 sample episodes (phase 0),
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4. runs the plan / interjections / vqa modules across the dataset
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(subtasks + memory are constrained to the canonical vocabulary),
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5. uploads the annotated dataset to ``--dest_repo_id`` (when set)
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or back to ``--repo_id``.
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Usage:
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HF_TOKEN=hf_... uv run python examples/annotations/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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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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"openai && "
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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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"--dest_repo_id=pepijn223/super_poulain_vocab "
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"--push_to_hub=true "
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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=128 "
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"--vlm.max_new_tokens=512 "
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"--vlm.temperature=0.7 "
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"--executor.episode_parallelism=16 "
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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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# Phase 0 — canonical vocabulary discovery from the first N sample
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# episodes. The VLM picks the right number of subtask + memory
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# entries itself from what it sees; the resulting
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# meta/canonical_vocabulary.json constrains every subtask + memory
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# string to a small repeatable target distribution.
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"--vocabulary.sample_episodes=3 "
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# Phase 1 — plan module (subtasks + plan + memory + task_aug).
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"--plan.frames_per_second=1.0 "
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"--plan.use_video_url=true "
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"--plan.use_video_url_fps=1.0 "
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"--plan.derive_task_from_video=always "
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"--plan.n_task_rephrasings=30 "
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# Phase 2 — interjections + speech.
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"--interjections.max_interjections_per_episode=6 "
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# Phase 4 — general VQA.
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"--vqa.K=3 "
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"--vqa.vqa_emission_hz=1.0"
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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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