Pepijn
ae8ca0719e
Use huggingface/lerobot-gpu:latest as base for both libero and metaworld benchmark Dockerfiles instead of building from nvidia/cuda scratch. The nightly image already has all extras installed via uv sync --extra all, so we only need to overlay the PR source code (and libero asset setup). This eliminates duplicated system dep installation, Python setup, uv venv creation, and the Triton ptxas workaround from both files. 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