Docker

← Tutorial Library · Environment and Training Tools

This section introduces the general functions of LeRobot. Please configure the example according to the target robot, data set and operating environment; before using it with AlohaMini 2 / 2 Pro, you need to confirm the hardware and interface compatibility.

---

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