Machine Learning & Data Science frameworks - DAWN
============================

We provide a set of pre-populated Conda environments based on the Intel
Distribution for Python:

.. code-block:: bash

   module av intelpython-conda
   conda info -e

This module provides environments for PyTorch and Tensorflow.

Please note that Intel code and documentation sometimes refers to 'XPUs', a more
generic term for accelerators, GPU or otherwise. For Dawn, 'XPU' and 'GPU' can
usually be considered interchangeable.

PyTorch
^^^^^^^

PyTorch on Intel GPUs is supported by the `Intel Extension for PyTorch <https://intel.github.io/intel-extension-for-pytorch/xpu/latest/>`_.
On Dawn this version of PyTorch is accessible as a conda environment named pytorch-gpu:

.. code-block:: bash

   module load intelpython-conda
   conda activate pytorch-gpu

Adapting your code to run on the PVCs is straightforward and only takes a few
lines of code. For details, see the official documentation - but as a quick
example:

.. code-block:: python

  import torch
  import intel_extension_for_pytorch as ipex

  ...

  # Enable GPU
  model = model.to('xpu')
  data = data.to('xpu')

  model = ipex.optimize(model, dtype=torch.float32)

TensorFlow
^^^^^^^^^^

Intel supports optimised TensorFlow on both CPU and GPU, using the `Intel
Extension for TensorFlow
<https://intel.github.io/intel-extension-for-tensorflow/latest/get_started.html>`_.
On Dawn this version on TensorFlow is accessible as a conda environment named tensorflow-gpu:

.. code-block:: bash

   module load intelpython-conda
   conda activate tensorflow-gpu

To run on the PVCs, there should be no need to modify your code - the Intel
optimised implementation will run automatically on the GPU, assuming it has been
installed as `intel-extension-for-tensorflow[xpu]`.


Jax/OpenXLA
^^^^^^^^^^^

Documentation can be found on GitHub: `Intel OpenXLA
<https://github.com/intel/intel-extension-for-openxla>`_

Julia
^^^^^

This is currently known not to work correctly on PVC GPUs. (Mar 2024)