| name | run-data-collection |
| description | Launch a data_collection automated trajectory-collection task on a GPU host using the `geniesim autocollect run` CLI verb (which wraps scripts/run_data_collection.sh: docker run -d + in-container server+client). Trigger: when the user asks to "采集数据", "跑数据采集", "run data collection", "collect a task", "生产轨迹", "launch a tasks/geniesim_2025/<...>.json", or wants to produce agibot-format episodes from a data_collection task template.
|
| license | MPL-2.0 |
| metadata | {"author":"genie-sim","version":"1.0"} |
When to Use
- User wants to produce trajectory episodes from a
data_collection task
template on a workstation with Docker + an NVIDIA GPU.
- User references a task under
source/data_collection/tasks/.
Do not use for:
- Running a benchmark/evaluation task →
run-benchmark.
- Just listing/inspecting tasks →
geniesim autocollect list directly.
Critical Patterns
run is host-orchestrated, not an in-container exec. It shells out to
scripts/run_data_collection.sh, which does docker run -d against
geniesim3-data-collection:latest and the entrypoint launches two
processes (Isaac Sim server + task client). Don't treat it like
benchmark run.- Collect the inputs first: the task (basename / path / unique substring)
and the run flags (
--headless, --no-record, --standalone,
--container-name). Use --dry-run to confirm resolution before launching.
- Prerequisites: Docker + NVIDIA GPU; the image
registry.agibot.com/genie-sim/geniesim3-data-collection:latest built/pulled;
geniesim_assets pip-installed (editable) on the host — the CLI discovers it via find_spec and bind-mounts it at /geniesim_assets.
- Unattended works.
run_data_collection.sh grants uid 1234 access
preferring sudo setfacl, degrading to chmod -R a+rwX when sudo isn't
usable — so headless/background runs work without a tty. (The fallback
world-writes the output dirs on the host.)
- Confirm before launching. A real run spawns a GPU container, takes
minutes, and writes ~1.5 GB per episode. Ask before kicking it off.
Workflow
Step 1 — Resolve the task
geniesim autocollect list --robot=g2 <substr>
geniesim autocollect run <TASK> --headless --standalone --dry-run
--dry-run prints the resolved task path + the exact run_data_collection.sh
command without launching. Disambiguate if it reports multiple matches.
Step 2 — Check prerequisites
docker images | grep geniesim3-data-collection
nvidia-smi
python3 -c "import importlib.util as u; print('geniesim_assets OK' if u.find_spec('geniesim_assets') else 'NOT INSTALLED')"
Step 3 — Launch
Interactive terminal (sudo can prompt):
pip install -e /path/to/geniesim_assets
geniesim autocollect run <TASK> --headless --standalone
Unattended / detached (no tty) — works directly (the script degrades to chmod
when sudo is unavailable; no PTY trick needed):
cd <repo-root>
PYTHONPATH=source/geniesim_cli/src \
nohup python3 -m geniesim_cli autocollect run <TASK> --headless --standalone \
> /tmp/dc-run.log 2>&1 &
(Use python3 -m geniesim_cli … if the geniesim console script isn't on PATH.)
Step 4 — Monitor & verify
tail -f source/data_collection/logs/<TASK>/data_collector_server.log
tail -f source/data_collection/logs/<TASK>/run_data_collection.log
docker ps | grep data_collection
ls source/data_collection/recording_data/
Success looks like job done in the client log, the container auto-removed
(EXIT trap), and one recording_data/[{TASK}_{INDEX}]/ dir per episode with
aligned_joints*.h5, observations/videos/*, state.json, data_info.json.
Notes
--no-record disables recording (drops --publish_ros + --use_recording);
omit it to record.- Recording produces ~1.5 GB/episode — watch disk; clean
recording_data/ after
validating.
- The container is ephemeral; only the mounted
recording_data/, logs/,
saved_task/ and the Isaac cache survive a run.
- Full task-config authoring:
source/data_collection/TASK_CONFIG_GUIDE.md.
Module reference: source/data_collection/AGENTS.md.
