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feat(annotate): run lerobot-annotate on HF Jobs via --job.target (#4095)

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* feat(annotate): run lerobot-annotate on HF Jobs via --job.target

Annotation needed a hand-edited launcher script (examples/annotations/run_hf_job.py)
to reach a GPU: users copied it, rewrote the embedded CMD string for their dataset,
and ran it with `python`. Fold that into the CLI instead, mirroring `lerobot-train`:
`lerobot-annotate --job.target=h200` submits the exact command you'd run locally.

- AnnotationJobConfig extends JobConfig with the annotation runtime's defaults
  (vllm/vllm-openai image, 2h cap) plus --job.lerobot_ref, so an unmerged branch
  can be exercised remotely without editing a script.
- lerobot.jobs.annotate builds the pod command by replaying the user's own CLI
  flags (minus --job.*/--root, with --repo_id re-emitted from the config) after a
  setup prelude that installs lerobot on top of the vLLM image. Job monitoring,
  log tailing and Ctrl-C-detaches reuse the training submitter's plumbing.
- Remote runs require --repo_id; a local-only dataset is pushed privately first.

The generated pod command is byte-for-byte the script's old CMD.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

* fix(annotate): reject client-side config files on remote runs

draccus exposes `--config_path` plus a `--<field>` config-file arg for every
nested dataclass (`--vlm`, `--plan`, `--job`, ...). All name files on the
client's disk, so forwarding them to the pod silently dropped whatever settings
they carried. Reject them up front instead.

Bare `--job` also slipped past the `--job.` prefix filter, so a `--job=cfg.yaml`
holding `target: h200` would have reached the pod and had the job submit a job
of its own, recursively. It is dropped from the forwarded args as well.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

* refactor(jobs): share the submit-and-follow loop between both submitters

`submit_annotate_to_hf` reused the leaf helpers (`_poll_until_done`, `_tail_logs`,
`_pod_forwarded_args`) but duplicated the orchestration around them: ~40 of the 50
lines that spawn the poll/log threads, install the Ctrl-C-detaches handler and
raise on a non-COMPLETED stage were identical in both files.

Extract that into `follow_job(job_id, *, detach, success_marker=None) -> bool`,
returning True when the job finished and False when we stopped watching without a
verdict (detach or Ctrl-C). Training keeps its model-pushed marker by passing it in;
annotation has no equivalent line (the CLI keeps working after the upload log to
write the card and tag) so its completion stays stage-based.

Kept in hf.py rather than a new module so every existing monkeypatch target in
test_hf.py still resolves.

Behaviour change: a training run whose job reaches COMPLETED without the marker
matching now prints its completion line instead of returning silently. The marker
was already documented as an optimisation with a stage-based fallback; the fallback
just never reported success.

Tests: adds annotate coverage for the non-detach path (completion and failure) —
previously only ever exercised with detach=true — plus a detach short-circuit test.
Both new annotate tests verified to fail under a mutation that stubs out follow_job.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

---------

Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
Pepijn
2026-07-23 10:30:33 +02:00
committed by GitHub
parent 73dbb6f43a
commit 9c82c39c7b
11 changed files with 616 additions and 162 deletions
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examples/annotations/run_hf_job.py
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#!/usr/bin/env python
# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Launch ``lerobot-annotate`` on a Hugging Face job (vllm + Qwen3.6-27B VLM).
Spawns one single-GPU ``h200`` job that:
1. installs ``lerobot`` from ``main`` plus the annotation extras,
2. boots one vllm server with Qwen3.6-27B (dense VLM),
3. runs the plan / interjections / vqa modules across the dataset
in free-form mode (each episode generates its own subtasks +
memory),
4. uploads the annotated dataset to ``--new_repo_id`` (when set)
or back to ``--repo_id``.
Usage:
HF_TOKEN=hf_... uv run python examples/annotations/run_hf_job.py
Adjust ``CMD`` (dataset, model, hub repo) and ``flavor`` below for your
run. For larger datasets, scale to ``h200x4`` and raise
``--vlm.parallel_servers`` / ``--vlm.num_gpus`` to match.
"""
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@main' && "
# Pins mirror pyproject.toml — unpinned installs pull av 18 / datasets 5 /
# draccus 0.11, which break lerobot at import time.
"pip install --upgrade-strategy only-if-needed "
"'datasets>=4.7.0,<5.0.0' 'pyarrow>=21.0.0,<30.0.0' 'av>=15.0.0,<16.0.0' 'draccus==0.10.0' "
"'pandas>=2.0.0,<3.0.0' jsonlines 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_pretrain_human300_v4 "
"--new_repo_id=pepijn223/robocasa_pretrain_human300_v4_annotated "
"--push_to_hub=true "
"--vlm.backend=openai "
"--vlm.model_id=Qwen/Qwen3.6-27B "
"--vlm.num_gpus=1 "
'--vlm.serve_command="vllm serve Qwen/Qwen3.6-27B '
"--tensor-parallel-size 1 --max-model-len 32768 "
'--gpu-memory-utilization 0.8 --uvicorn-log-level warning --port {port}" '
"--vlm.serve_ready_timeout_s=1800 "
# Qwen3.6 ships with thinking on; annotation wants plain JSON answers.
"--vlm.chat_template_kwargs='{\"enable_thinking\": false}'"
)
job = run_job(
image="vllm/vllm-openai:latest",
command=["bash", "-c", CMD],
flavor="h200",
secrets={"HF_TOKEN": token},
timeout="2h",
)
print(f"Job URL: {job.url}")
print(f"Job ID: {job.id}")