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Clara AGX PyTorch

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Description

This release is PyTorch built only with support for Clara AGX Hardware. PyTorch is a GPU accelerated tensor computational framework with a Python front end.

Publisher

Facebook

Latest Tag

21.05-1.7-py3

Modified

July 1, 2022

Compressed Size

4.34 GB

Multinode Support

No

Multi-Arch Support

No

21.05-1.7-py3 (Latest) Scan Results

Linux / arm64

This container is deprecated. It was released as part of the Clara Holoscan SDK v0.1 and will no longer be compatible with Clara Holoscan SDK v0.2.

What Is Clara AGX PyTorch?

This release is PyTorch built only with support for Clara AGX Hardware.

PyTorch is a GPU accelerated tensor computational framework with a Python front end. Functionality can be easily extended with common Python libraries such as NumPy, SciPy, and Cython. Automatic differentiation is done with a tape-based system at both a functional and neural network layer level. This functionality brings a high level of flexibility and speed as a deep learning framework and provides accelerated NumPy-like functionality.

Running PyTorch

Before you can run an NGC deep learning framework container, your Docker environment must support NVIDIA GPUs. To run a container, issue the appropriate command as explained in the Running A Container chapter in the NVIDIA Containers And Frameworks User Guide and specify the registry, repository, and tags. For more information about using NGC, refer to the NGC Container User Guide.

The method implemented in your system depends on the AGX OS version installed, the specific NGC Cloud Image provided by a Cloud Service Provider, or the software that you have installed in preparation for running NGC containers.

Procedure

  1. Select the Tags tab and locate the container image release that you want to run.

  2. In the Pull Tag column, click the icon to copy the docker pull command.

  3. Open a command prompt and paste the pull command. The pulling of the container image begins. Ensure the pull completes successfully before proceeding to the next step.

  4. Run the container image. To run the container, choose interactive mode or non-interactive mode.

  • Interactive mode:

A typical command to launch the container is:

docker run --gpus all -it --rm -v local_dir:container_dir nvcr.io/nvidia/pytorch:xx.xx-py3
  • Non-interactive mode:

A typical command to launch the container is:

docker run --gpus all -it --rm -v local_dir:container_dir nvcr.io/ea-clara-agx/agx-pytorch:xx.xx-py3

Where:

  • -it means run in interactive mode

  • --rm will delete the container when finished

  • -v is the mounting directory

  • local_dir is the directory or file from your host system (absolute path) that you want to access from inside your container. For example, the local_dir in the following path is /home/jsmith/data/mnist.

    -v /home/jsmith/data/mnist:/data/mnist
    

    If you are inside the container, for example, ls /data/mnist, you will see the same files as if you issued the ls /home/jsmith/data/mnist command from outside the container.

  • container_dir is the target directory when you are inside your container. For example, /data/mnist is the target directory in the example:

    -v /home/jsmith/data/mnist:/data/mnist
    
  • xx.xx is the container version. For example, 20.01.

You might want to pull in data and model descriptions from locations outside the container for use by Torch. To accomplish this, the easiest method is to mount one or more host directories as Docker data volumes. You have pulled the latest files and run the container image.

Note: DIGITS uses shared memory to share data between processes. For example, if you use Torch multiprocessing for multi-threaded data loaders, the default shared memory segment size that the container runs with may not be enough. Therefore, you should increase the shared memory size by issuing either:

--ipc=host

or

--shm-size=

in the command line to:

docker run --gpus all
  1. See /workspace/README.md inside the container for information on customizing your PyTorch image.

Suggested Reading

For the latest Release Notes, see the PyTorch Release Notes Documentation website.

For more information about PyTorch, including tutorials, documentation, and examples, see:

License

Licenses are available and can be pulled as part of the procedure described. By pulling and using the container, you accept the terms and conditions of these licenses.