Docker Development

cuRobo will work on most docker images that have pytorch. We provide some example dockerfiles in curobo/docker for reference. The dockerfiles are described in the below table.






Dockerfile that builds cuRobo with a pytorch base container.



Dockerfile that builds cuRobo with a pytorch base container for using on a NVIDIA Jetson. This container can only be built on a NVIDIA Jetson.



Dockerfile that builds cuRobo with NVIDIA Isaac Sim VERSION and vulkan. This docker can run Isaac sim with native GUI or headless. Replace VERSION with 2022.2.1 or 2023.1.0.

Building your own docker image with cuRobo

  1. Add default nvidia runtime to enable cuda compilation during docker build:

    Edit/create the /etc/docker/daemon.json with content:
        "runtimes": {
            "nvidia": {
                "path": "/usr/bin/nvidia-container-runtime",
                "runtimeArgs": []
        "default-runtime": "nvidia" # ADD this line (the above lines will already exist in your json file)
  2. If you are building a docker with isaac sim, setup a NGC account following instructions in Isaac Sim Container Setup.

  3. bash TAG, replace TAG with a name from the tag column in the above table.

  4. bash TAG to launch the built docker.

If you want to have a docker that also enables development by mounting a folder, you can create a user docker with the below commands (skip step 4 above). This will mount a folder /home/${USER}/code into the docker container where you can do your development.

  1. bash TAG

  2. bash TAG will start the docker.

Build Warp for NVIDIA Jetson (Deprecated)


Warp is available from pypi starting with 0.11.0. The below instructions are not needed anymore.

NVIDIA Warp requires 11.5+ CUDA to compile and NVIDIA Jetson ships with CUDA 11.4. We will install a new version of CUDA and then compile the library (.so) file for using warp on NVIDIA Jetson.

  1. Install a newer version of cuda using the below commands (details):

    sudo mv /etc/apt/preferences.d/cuda-repository-pin-600
    sudo dpkg -i cuda-tegra-repo-ubuntu2004-11-8-local_11.8.0-1_arm64.deb
    sudo cp /var/cuda-tegra-repo-ubuntu2004-11-8-local/cuda-*-keyring.gpg /usr/share/keyrings/
    sudo apt-get update
    sudo apt-get -y install cuda
    export CUDA_HOME=/usr/local/cuda-11.8/
  2. Download and compile warp inside a clone of curobo for use inside a dockerfile later:

    git clone && cd curobo && mkdir pkgs && cd pkgs
    git clone && cd warp && python3 --no_standalone


Make sure when running python3 for warp, it’s compiling the CUDA kernels. warp looks for cuda toolkit at CUDA_HOME so set it to your cuda toolkit path before running