Machine Learning Containers for Jetson and JetPack 4.4

Hosted on NVIDIA GPU Cloud (NGC) are the following Docker container images for machine learning on Jetson:

• l4t-ml
• l4t-pytorch
• l4t-tensorflow

Included in this repo are the Dockerfiles and scripts used to build the above containers.

Docker Default Runtime

To enable access to the CUDA compiler (nvcc) during docker build operations, add "default-runtime": "nvidia" to your /etc/docker/daemon.json configuration file before attempting to build the containers:

{
    "runtimes": {
        "nvidia": {
            "path": "nvidia-container-runtime",
            "runtimeArgs": []
        }
    },

    "default-runtime": "nvidia"
}

You will then want to restart the Docker service or reboot your system before proceeding.

Building the Containers

To rebuild the containers from a Jetson device running JetPack 4.4 Developer Preview, clone this repo and run ./scripts/docker_build_all.sh:

$ git clone https://github.com/dusty-nv/jetson-containers
$ cd jetson-containers
$ ./scripts/docker_build_all.sh

Note that the TensorFlow and PyTorch pip wheel installers for aarch64 are automatically downloaded in the Dockerfiles from the Jetson Zoo.

Testing the Containers

To run a series of automated tests on the packages installed in the containers, run the following from your jetson-containers directory:

$ ./scripts/docker_test.sh