mirror of
https://github.com/huggingface/lerobot.git
synced 2026-06-01 11:21:27 +00:00
8615f3f613
Low-confidence VLM detections were producing many overlapping, loose boxes per frame (oven + toaster oven + counter + drawer + ...) and coarse keypoints, hurting downstream policy grounding. Two surgical fixes: - module_3_vqa prompt: cap bbox at most 3 high-confidence detections (prefer 1 tight box), require specific labels and ≤10% padding, allow empty detections list when nothing meets the bar; keypoint must be a single pixel-precise feature (handle / button / gripper tip) rather than a coarse "somewhere on object" point. - run_hf_job: lower vlm.temperature 0.7 → 0.2. Bbox + keypoint are coordinate-regression tasks where sampling noise directly degrades localization; question phrasing still varies enough at 0.2. No new config knobs — the count cap lives in the prompt since "top-N by confidence" is best picked by the VLM itself. Validator already accepts empty detections. Co-authored-by: Cursor <cursoragent@cursor.com>
122 lines
5.6 KiB
Python
122 lines
5.6 KiB
Python
#!/usr/bin/env python
|
||
"""Launch ``lerobot-annotate`` on a Hugging Face job (vllm + Qwen3.6 MoE).
|
||
|
||
Spawns one ``h200x4`` job that:
|
||
|
||
1. installs this branch of ``lerobot`` plus the annotation extras,
|
||
2. boots four vllm servers (one per H200) with Qwen3.6-35B-A3B-FP8,
|
||
3. runs the plan + vqa modules across the dataset in free-form
|
||
mode — phase 0 (canonical vocabulary discovery) is disabled so
|
||
every episode's subtasks + memory are generated independently;
|
||
interjections is also disabled, which short-circuits the
|
||
plan_update phase that depends on it,
|
||
4. uploads the annotated dataset to ``--dest_repo_id`` (when set)
|
||
or back to ``--repo_id``.
|
||
|
||
Usage:
|
||
|
||
HF_TOKEN=hf_... uv run python examples/annotations/run_hf_job.py
|
||
|
||
Adjust ``CMD`` below to point at your own dataset / target hub repo.
|
||
"""
|
||
|
||
import os
|
||
|
||
from huggingface_hub import get_token, run_job
|
||
|
||
token = os.environ.get("HF_TOKEN") or get_token()
|
||
if not token:
|
||
raise RuntimeError("No HF token. Run `huggingface-cli login` or `export HF_TOKEN=hf_...`")
|
||
|
||
CMD = (
|
||
"apt-get update -qq && apt-get install -y -qq git ffmpeg && "
|
||
"pip install --no-deps "
|
||
"'lerobot @ git+https://github.com/huggingface/lerobot.git@feat/language-annotation-pipeline' && "
|
||
"pip install --upgrade-strategy only-if-needed "
|
||
"datasets pyarrow av jsonlines draccus gymnasium torchcodec mergedeep pyyaml-include toml typing-inspect "
|
||
"openai && "
|
||
"export VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS=0 && "
|
||
"export VLLM_VIDEO_BACKEND=pyav && "
|
||
"lerobot-annotate "
|
||
"--repo_id=pepijn223/robocasa_smoke_2atomic_v3 "
|
||
"--dest_repo_id=pepijn223/robocasa_smoke_2atomic_v3_annotated "
|
||
"--push_to_hub=true "
|
||
"--vlm.backend=openai "
|
||
"--vlm.model_id=Qwen/Qwen3.6-35B-A3B-FP8 "
|
||
"--vlm.parallel_servers=4 "
|
||
"--vlm.num_gpus=4 "
|
||
'--vlm.serve_command="vllm serve Qwen/Qwen3.6-35B-A3B-FP8 '
|
||
# 4× the context (32768 → 131072) so long episodes at 1 Hz fit even
|
||
# at full Qwen vision resolution: 90 frames @ ~700 vision tokens/frame
|
||
# ≈ 63 k tokens, comfortably under 131 k. On 1× H200 (144 GB) the
|
||
# 35B-FP8 model leaves plenty of room for the bigger KV cache.
|
||
"--tensor-parallel-size 1 --max-model-len 131072 "
|
||
'--gpu-memory-utilization 0.85 --uvicorn-log-level warning --port {port}" '
|
||
"--vlm.serve_ready_timeout_s=1800 "
|
||
"--vlm.client_concurrency=256 "
|
||
"--vlm.max_new_tokens=512 "
|
||
# Low temperature for VQA: bbox + keypoint are coordinate-regression
|
||
# tasks where sampling noise directly degrades localization
|
||
# (overlapping boxes, drifted points). 0.2 keeps the model decisive
|
||
# while still letting question/label phrasing vary across frames.
|
||
"--vlm.temperature=0.2 "
|
||
"--executor.episode_parallelism=64 "
|
||
"--vlm.chat_template_kwargs='{\"enable_thinking\": false}' "
|
||
# Whole-scene agentview is the right choice for subtask reasoning +
|
||
# VQA on robocasa: the wrist (``robot0_eye_in_hand``) usually only
|
||
# sees the gripper + nearby object, which hurts "what is happening
|
||
# in this episode" decomposition. Override per-dataset if your
|
||
# cameras are named differently (inspect ``meta/info.json``).
|
||
"--vlm.camera_key=observation.images.robot0_agentview_left "
|
||
# Phase 0 — canonical vocabulary discovery DISABLED. This dataset's
|
||
# episodes span heterogeneous tasks/scenes, so a single shared
|
||
# subtask + memory vocabulary would be too narrow — each episode
|
||
# generates its subtasks + memory free-form instead.
|
||
"--vocabulary.enabled=false "
|
||
# Phase 1 — plan module (subtasks + plan + memory + task_aug).
|
||
"--plan.enabled=true "
|
||
"--plan.frames_per_second=1.0 "
|
||
"--plan.use_video_url=true "
|
||
"--plan.use_video_url_fps=1.0 "
|
||
# Force coarse, composite subtasks (``pick up X`` = approach + grasp
|
||
# + lift in one span, not three). 3 s is large enough to host a
|
||
# full grasp-or-place composite at typical 20 fps robocasa speeds;
|
||
# any candidate span shorter than this gets merged into a neighbour
|
||
# by the prompt's authoring rules (see module_1_subtasks.txt).
|
||
"--plan.min_subtask_seconds=3.0 "
|
||
# Cap so the VLM can't drift into micro-segmentation. Combined with
|
||
# the composite-action rules in the prompt, this targets ~3-6
|
||
# meaningful spans per episode for typical pick-and-place demos.
|
||
"--plan.plan_max_steps=9 "
|
||
# ``off`` keeps the dataset's canonical ``record.episode_task`` as-is
|
||
# — no per-episode VLM "what is this video about" call. Switch to
|
||
# ``if_short`` (default) only if some episodes have placeholder /
|
||
# missing canonical tasks; ``always`` overrides every episode's task.
|
||
"--plan.derive_task_from_video=off "
|
||
# 0 disables the task_aug pass entirely (see PlanConfig.n_task_rephrasings
|
||
# docstring) — no per-episode paraphrase generation, no task_aug rows.
|
||
"--plan.n_task_rephrasings=0 "
|
||
# Phase 2 — interjections OFF (also skips phase 3 plan_update,
|
||
# see executor.py:_run_plan_update_phase guard).
|
||
"--interjections.enabled=false "
|
||
# Phase 4 — general VQA. K=1 keeps each VQA answer on its own
|
||
# emission frame (no temporal smear); see VqaConfig.K docstring.
|
||
# 3 Hz cadence: at 20 fps source, that's a VQA tick every ~7 frames.
|
||
# NOTE: VQA emits per-camera, so for robocasa (3 cameras) each tick
|
||
# produces 3 (user, assistant) row pairs — total call volume ~= 3 *
|
||
# 3 Hz * mean_episode_seconds * n_episodes.
|
||
"--vqa.enabled=true "
|
||
"--vqa.K=1 "
|
||
"--vqa.vqa_emission_hz=3.0"
|
||
)
|
||
|
||
job = run_job(
|
||
image="vllm/vllm-openai:latest",
|
||
command=["bash", "-c", CMD],
|
||
flavor="h200x4",
|
||
secrets={"HF_TOKEN": token},
|
||
timeout="24h",
|
||
)
|
||
print(f"Job URL: {job.url}")
|
||
print(f"Job ID: {job.id}")
|