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Docker Setup for NVIDIA Isaac GR00T

Docker configuration for building and running a containerized GR00T environment with all dependencies pre-installed. The image (gr00t-dev) is based on NVIDIA's PyTorch container and includes CUDA support, Python dependencies, PyTorch3D, and the GR00T codebase.

Prerequisites

  • Docker (version 20.10+) and perform post-installation setup to verify that you can run docker commands without sudo.
  • NVIDIA Container Toolkit (installation guide)
  • NVIDIA GPU with compatible drivers
  • Bash shell
  • Sufficient disk space (several GB)

Building the Docker Image

Make sure you are using a bash environment:

bash build.sh

The build process uses nvcr.io/nvidia/pytorch:25.04-py3 as the base image, installs all dependencies, and sets up the GR00T codebase at /workspace/gr00t/.

Running the Container

Interactive shell (uses code baked into image):

docker run -it --rm --gpus all gr00t-dev /bin/bash

Development mode (mounts local codebase for live editing):

docker run -it --rm --gpus all \
    -v $(pwd)/..:/workspace/gr00t \
    gr00t-dev /bin/bash

Run this from the docker/ directory. Changes to your local GR00T code will be immediately reflected inside the container.

Troubleshooting

GPU not detected:

  • Verify NVIDIA Container Toolkit: nvidia-container-toolkit --version
  • Restart Docker: sudo systemctl restart docker
  • Test GPU access: docker run --rm --gpus all nvidia/cuda:12.0.0-base-ubuntu22.04 nvidia-smi

Permission errors:

  • Use sudo with Docker commands, or add your user to the docker group: sudo usermod -aG docker $USER

Build failures:

  • Check disk space: df -h
  • Clean Docker: docker system prune -a
  • Rebuild: sudo bash build.sh --no-cache