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Clara AGX TensorFlow

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Description

This release is TensorFlow built only with support for Clara AGX Hardware. TensorFlow is an open-source software library for high-performance numerical computation.

Publisher

Google Brain Team

Latest Tag

21.05-tf1-py3

Modified

December 1, 2022

Compressed Size

4.46 GB

Multinode Support

No

Multi-Arch Support

No

21.05-tf1-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 TensorFlow?

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

TensorFlow is an open-source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code.

TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.

Running TensorFlow

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.

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-tensorflow:xx.xx-tfx-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.

  • tfx is the version of TensorFlow. For example, tf1 or tf2.

TensorFlow is run by importing it as a Python module:

$ python
>>> import tensorflow as tf
>>> print(tf.__version__)
1.15.0

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

  1. See /workspace/README.md inside the container for information on customizing your TensorFlow image.

Suggested Reading

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

For a full list of the supported software and specific versions that come packaged with this framework based on the container image, see the Frameworks Support Matrix.

For more information about TensorFlow, 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.