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NVIDIA L4T CUDA

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Description

CUDA is a parallel computing platform and programming model that enables dramatic increases in computing performance by harnessing the power of the NVIDIA GPUs.

Publisher

NVIDIA

Latest Tag

11.4.14-runtime

Modified

May 27, 2022

Compressed Size

1.23 GB

Multinode Support

No

Multi-Arch Support

No

11.4.14-runtime (Latest) Scan Results

Linux / arm64

NVIDIA CUDA

CUDA is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers can dramatically speed up computing applications by harnessing the power of GPUs.

Overview of Images

Currently only CUDA runtime container is provided. The CUDA runtime container image is intended to be used as a base image to containerize and deploy CUDA applications on Jetson and includes CUDA runtime and CUDA math libraries included in it; these components does not get mounted from host by NVIDIA container runtime. NVIDIA container rutime still mounts platform specific libraries and select device nodes into the container.

The image is tagged with the version corresponding to the CUDA release version. Based on this, the l4t-cuda:r10.2.460-runtime container is intended to be run on devices running JetPack 4.6 which supports CUDA version 10.2.460

Running the container

Prerequisites

Ensure that NVIDIA Container Runtime on Jetson is running on Jetson.

Note that NVIDIA Container Runtime is available for install as part of Nvidia JetPack

Pull the container

Before running the l4t-cuda runtime container, use Docker pull to ensure an up-to-date image is installed. Once the pull is complete, you can run the container image.

Procedure

  1. In the Pull column, click the icon to copy the Docker pull command for the l4t-cuda-runtime container.
  2. Open a command prompt and paste the pull command. Docker will initiate a pull of the container from the NGC registry.
    Ensure the pull completes successfully before proceeding to the next step.

Run the container

To run the container:

  1. Allow external applications to connect to the host's X display:
xhost +
  1. Run the docker container using the docker command
sudo docker run -it --rm --net=host --runtime nvidia -e DISPLAY=$DISPLAY -v /tmp/.X11-unix/:/tmp/.X11-unix nvcr.io/nvidia/l4t-cuda:r10.2.460-runtime

Option explained:

  • -it means run in interactive mode
  • --rm will delete the container when finished
  • --runtime nvidia will use the NVIDIA container runtime while running the l4t-base container
  • -v is the mounting directory, and used to mount host's X11 display in the container filesystem to render output videos
  • r10.2.460 is the tag for the image corresponding to the l4t release

Exposing additional features

By default a limited set of device nodes and associated functionality is exposed within the cuda-runtime containers using the mount plugin capability. This list is documented here.

User can expose additional devices using the --device command option provided by docker.
Directories and files can be bind mounted using the -v option.

Note that usage of some devices might need associated libraries to be available inside the container.

Run a sample application

Once you have successfully launched the l4t-cuda containers, you run CUDA applicaiton inside it. For example, to run CUDA sampels inside the l4t-cuda runtime container, you can mount the CUDA samples inside the container using -v options (-v ) during "docker run" and then run the CUDA samples from within the container.

End User License Agreements

The images are governed by the following NVIDIA End User License Agreements. By pulling and using the CUDA images, you accept the terms and conditions of these licenses. Since the images may include components licensed under open-source licenses such as GPL, the sources for these components are archived here.

NVIDIA Deep learning Container License

To view the NVIDIA Deep Learning Container license, click here

Documentation

For more information on CUDA, including the release notes, programming model, APIs and developer tools, visit the CUDA documentation site.