Add GH200 TensorFlow via NGC docs

software/applications/tensorflow.rst

@@ -7,52 +7,145 @@ TensorFlow
 
 TensorFlow can be installed through a number of python package managers such as :ref:`Conda<software-applications-conda>` or ``pip``.
 
-For use on Bede, the simplest method is to install TensorFlow using the :ref:`Open-CE Conda distribution<software-applications-open-ce>`.
+For use on Bede's ``ppc64le`` nodes, the simplest method is to install TensorFlow using the :ref:`Open-CE Conda distribution<software-applications-open-ce>`.
+
+For the ``aarch64`` nodes, using a NVIDIA provided `NGC Tensorflow container <https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tensorflow>`__ is likely preferred.
 
 
 Installing via Conda (Open-CE)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-With a working Conda installation (see :ref:`Installing Miniconda<software-applications-conda-installing>`) the following instructions can be used to create a Python 3.8 conda environment named ``tf-env`` with the latest Open-CE provided TensorFlow:
+.. tabs::
+
+   .. group-tab:: ppc64le
+
+      With a working Conda installation (see :ref:`Installing Miniconda<software-applications-conda-installing>`) the following instructions can be used to create a Python 3.8 conda environment named ``tf-env`` with the latest Open-CE provided TensorFlow:
+
+      .. note:: 
+
+         TensorFlow installations via conda can be relatively large. Consider installing your miniconda (and therfore your conda environments) to the ``/nobackup`` file store.
+
+
+      .. code-block:: bash
+
+         # Create a new conda environment named tf-env within your conda installation
+         conda create -y --name tf-env python=3.8
+
+         # Activate the conda environment
+         conda activate tf-env
+
+         # Add the OSU Open-CE conda channel to the current environment config
+         conda config --env --prepend channels https://ftp.osuosl.org/pub/open-ce/current/
 
-.. note:: 
+         # Also use strict channel priority
+         conda config --env --set channel_priority strict
 
-   TensorFlow installations via conda can be relatively large. Consider installing your miniconda (and therfore your conda environments) to the ``/nobackup`` file store.
+         # Install the latest available version of Tensorflow
+         conda install -y tensorflow
 
+      In subsequent interactive sessions, and when submitting batch jobs which use TensorFlow, you will then need to re-activate the conda environment.
 
-.. code-block:: bash
+      For example, to verify that TensorFlow is available and print the version:
 
-   # Create a new conda environment named tf-env within your conda installation
-   conda create -y --name tf-env python=3.8
+      .. code-block:: bash
 
-   # Activate the conda environment
-   conda activate tf-env
+         # Activate the conda environment
+         conda activate tf-env
 
-   # Add the OSU Open-CE conda channel to the current environment config
-   conda config --env --prepend channels https://ftp.osuosl.org/pub/open-ce/current/
+         # Invoke python
+         python3 -c "import tensorflow;print(tensorflow.__version__)"
 
-   # Also use strict channel priority
-   conda config --env --set channel_priority strict
+      .. note::
+
+         The :ref:`Open-CE<software-applications-open-ce>` distribution of TensorFlow does not include IBM technologies such as DDL or LMS, which were previously available via :ref:`WMLCE<software-applications-wmlce>`. 
+         WMLCE is no longer supported.
 
-   # Install the latest available version of Tensorflow
-   conda install -y tensorflow
+   .. group-tab:: aarch64
 
-In subsequent interactive sessions, and when submitting batch jobs which use TensorFlow, you will then need to re-activate the conda environment.
+      .. warning::
 
-For example, to verify that TensorFlow is available and print the version:
+         Conda and pip builds of TensorFlow for ``aarch64`` do not include CUDA support as of April 2024. For now, see :ref:`software-applications-tensorflow-ngc` or `build from source <https://tensorflow.org/install/source>`__.
 
-.. code-block:: bash
+.. _software-applications-tensorflow-ngc:
 
-   # Activate the conda environment
-   conda activate tf-env
+Using NGC TensorFlow Containers
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-   # Invoke python
-   python3 -c "import tensorflow;print(tensorflow.__version__)"
 
-.. note::
+.. tabs::
+
+   .. group-tab:: ppc64le
+
+      .. warning::
+
+         NVIDIA do not provide ``ppc64le`` containers for TensorFlow through NGC. This method should only be used for ``aarch64`` partitions.
 
-   The :ref:`Open-CE<software-applications-open-ce>` distribution of TensorFlow does not include IBM technologies such as DDL or LMS, which were previously available via :ref:`WMLCE<software-applications-wmlce>`. 
-   WMLCE is no longer supported.
+   .. group-tab:: aarch64
+
+      NVIDIA provide docker containers with CUDA-enabled TensorFlow builds for ``x86_64`` and ``aarch64`` architectures through NGC.
+
+      The `NGC Tensorflow <https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tensorflow>`__ containers have included Hopper support since ``22.09``.
+
+      For details of which TensorFlow version is provided by the each container release, see the `NGC TensorFlow container release notes <https://docs.nvidia.com/deeplearning/frameworks/tensorflow-release-notes>`__.
+
+
+      :ref:`software-tools-apptainer` can be used to convert and run docker containers, or to build an apptainer container based on a docker container. 
+      These can be built on the ``aarch64`` nodes in Bede using :ref:`software-tools-apptainer-rootless`.
+
+      .. note::
+
+         TensorFlow containers can consume a large amount of disk space. Consider setting :ref:`software-tools-apptainer-cachedir` to an appropriate location in ``/nobackup``, e.g. ``export APPTAINER_CACHEDIR=/nobackup/projects/${SLURM_JOB_ACCOUNT}/${USER}/apptainer-cache``.
+
+      .. note::
+
+         The following apptainer commands should be executed from an ``aarch64`` node only, i.e. on ``ghlogin``, ``gh`` or ``ghtest``.
+
+      Docker containers can be fetched and converted using ``apptainer pull``, prior to using ``apptainer exec`` to execute code within the container.
+
+      .. code:: bash
+
+         # Pull and convert the docker container. This may take a while.
+         apptainer pull docker://nvcr.io/nvidia/tensorflow:24.03-tf2-py3
+         # Run a command in the container, i.e. showing the TensorFlow version
+         apptainer exec --nv docker://nvcr.io/nvidia/tensorflow:24.03-tf2-py3 python3 -c "import tensorflow; print(tensorflow.__version__);"
+
+      Alternatively, if you require more than just TensorFlow within the container you can create an `apptainer definition file <https://apptainer.org/docs/user/main/definition_files.html>`__.
+      E.g. for a container based on ``tensorflow:24.03-tf2-py3`` which also installs HuggingFace Transformers ``4.37.0``, the following definition file could be used:
+
+      .. code:: singularity
+
+         Bootstrap: docker
+         From: nvcr.io/nvidia/tensorflow:24.03-tf2-py3
+
+         %post
+           # Install other python dependencies, e.g. hugging face transformers
+           python3 -m pip install transformers==4.37.0
+
+         %test
+           # Print the torch version, if CUDA is enabled and which architectures
+           python3 -c "import tensorflow; print(tensorflow.__version__); print(tensorflow.config.list_physical_devices('GPU'));"
+           # Print the TensorFlow transformers version, demonstrating it is available.
+           python3 -c "import transformers;print(transformers.__version__);"
+
+      Assuming this is named ``tf-transformers.def``, a corresponding apptainer image file name ``tf-transformers.sif`` can then be created via:
+
+      .. code-block:: bash
+
+         apptainer build --nv tf-transformers.sif tf-transformers.def
+
+      Commands within this container can then be executed using ``apptainer exec``.
+      I.e. to see the version of transformers installed within the container:
+
+      .. code-block:: bash
+
+         apptainer exec --nv tf-transformers.sif python3 -c "import transformers;print(transformers.__version__);"
+
+      Or in this case due to the ``%test`` segment of the container, run the test command.
+
+      .. code-block:: bash
+
+         apptainer test --nv tf-transformers.sif
+
 
 Further Information
 ~~~~~~~~~~~~~~~~~~~