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https://github.com/huggingface/lerobot.git
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Pepijn
cec8ee0be6
Steerable annotation pipeline (lerobot-annotate) that populates the language_persistent and language_events columns introduced in PR 1 (#3467) directly into data/chunk-*/file-*.parquet. This is PR 2 of the three-PR plan: PR 1 (Add extensive language support #3467): schema + DSL + rendering, base of this PR PR 2 (this PR): annotation pipeline writing into PR 1's columns PR 3: model with language prediction and runtime A VLM (Qwen-VL family, served on vLLM) watches each episode's video and emits grounded language annotations: subtasks, plans, memory, task rephrasings, interjections + speech, and per-camera VQA. The pipeline is built for production annotation at scale — single-camera grounding, embedded-frame inputs, a describe-then-segment grounding flow, and a deterministic full-episode coverage guarantee — informed by Scale's dense-captioning findings (representation > sampling, rules > reasoning, model capacity is the biggest lever, two-pass systems compound errors)
78 lines
2.9 KiB
Python
78 lines
2.9 KiB
Python
#!/usr/bin/env python
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# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Launch ``lerobot-annotate`` on a Hugging Face job (vllm + Qwen3.6-27B VLM).
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Spawns one single-GPU ``h200`` job that:
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1. installs ``lerobot`` from ``main`` plus the annotation extras,
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2. boots one vllm server with Qwen3.6-27B (dense VLM),
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3. runs the plan / interjections / vqa modules across the dataset
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in free-form mode (each episode generates its own subtasks +
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memory),
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4. uploads the annotated dataset to ``--new_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`` (dataset, model, hub repo) and ``flavor`` below for your
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run. For larger datasets, scale to ``h200x4`` and raise
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``--vlm.parallel_servers`` / ``--vlm.num_gpus`` to match.
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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@main' && "
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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=pepijn223/robocasa_pretrain_human300_v4 "
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"--new_repo_id=pepijn223/robocasa_pretrain_human300_v4_annotated "
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"--push_to_hub=true "
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"--vlm.backend=openai "
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"--vlm.model_id=Qwen/Qwen3.6-27B "
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"--vlm.num_gpus=1 "
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'--vlm.serve_command="vllm serve Qwen/Qwen3.6-27B '
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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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# Qwen3.6 ships with thinking on; annotation wants plain JSON answers.
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"--vlm.chat_template_kwargs='{\"enable_thinking\": false}'"
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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="h200",
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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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