Pepijn
eff1d5a528
- envs(vlabench): drop the unused `episode_index` / `n_envs` constructor args — they were stored but never referenced. - envs(vlabench): replace module-level `print()` calls with the standard `logging.getLogger(__name__)` logger (matches the rest of the repo's script convention). Covers the two PhysicsError retry/step paths and the `create_vlabench_envs` progress lines. - envs(vlabench): drop `_make_env_fns` and build factories MetaWorld-style via a lambda list comprehension — simpler, no extra helper. - docker(vlabench): pin upstream clones to commit SHAs (`OpenMOSS/VLABench@cf588fe6`, `motion-planning/rrt-algorithms@e51d95ee`) so benchmark images are reproducible. - ci(vlabench): run `scripts/ci/extract_task_descriptions.py` after the eval so `metrics.json` carries per-task natural-language labels, matching LIBERO / MetaWorld jobs. Added a VLABench extractor that produces cleaned-name labels keyed by `<task>_0`. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Docker
This directory contains Dockerfiles for running LeRobot in containerized environments. Both images are built nightly from main and published to Docker Hub with the full environment pre-baked — no dependency setup required.
Pre-built Images
# CPU-only image (based on Dockerfile.user)
docker pull huggingface/lerobot-cpu:latest
# GPU image with CUDA support (based on Dockerfile.internal)
docker pull huggingface/lerobot-gpu:latest
Quick Start
The fastest way to start training is to pull the GPU image and run lerobot-train directly. This is the same environment used for all of our CI, so it is a well-tested, batteries-included setup.
docker run -it --rm --gpus all --shm-size 16gb huggingface/lerobot-gpu:latest
# inside the container:
lerobot-train --policy.type=act --dataset.repo_id=lerobot/aloha_sim_transfer_cube_human
Dockerfiles
Dockerfile.user (CPU)
A lightweight image based on python:3.12-slim. Includes all Python dependencies and system libraries but does not include CUDA — there is no GPU support. Useful for exploring the codebase, running scripts, or working with robots, but not practical for training.
Dockerfile.internal (GPU)
A CUDA-enabled image based on nvidia/cuda. This is the image for training — mostly used for internal interactions with the GPU cluster.
Usage
Running a pre-built image
# CPU
docker run -it --rm huggingface/lerobot-cpu:latest
# GPU
docker run -it --rm --gpus all --shm-size 16gb huggingface/lerobot-gpu:latest
Building locally
From the repo root:
# CPU
docker build -f docker/Dockerfile.user -t lerobot-user .
docker run -it --rm lerobot-user
# GPU
docker build -f docker/Dockerfile.internal -t lerobot-internal .
docker run -it --rm --gpus all --shm-size 16gb lerobot-internal
Multi-GPU training
To select specific GPUs, set CUDA_VISIBLE_DEVICES when launching the container:
# Use 4 GPUs
docker run -it --rm --gpus all --shm-size 16gb \
-e CUDA_VISIBLE_DEVICES=0,1,2,3 \
huggingface/lerobot-gpu:latest
USB device access (e.g. robots, cameras)
docker run -it --device=/dev/ -v /dev/:/dev/ --rm huggingface/lerobot-cpu:latest