Pepijn
c505a71f78
Security: - Remove "Login to Hugging Face" step — it was a no-op (ephemeral --rm container) that exposed the HF token via CLI argument in docker inspect / /proc/*/cmdline. The eval step already re-authenticates via env var. Functional: - Remove feat/benchmark-ci from push trigger branches (won't exist post-merge). Dockerfiles: - Pin uv to 0.8.0 (was unpinned, fetching whatever latest ships). - Add comment explaining the chmod +x ptxas workaround (Triton packaging bug — ships ptxas without execute bit). Scripts: - parse_eval_metrics.py: add note that it runs on bare host and must stay stdlib-only. - parse_eval_metrics.py: add NaN guard for avg_sum_reward and eval_s (was only guarding pc_success). Co-Authored-By: Claude Opus 4.6 (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