.. _serve-pytorch-tutorial:

PyTorch Tutorial
================

In this guide, we will load and serve a PyTorch Resnet Model.
In particular, we show:

- How to load the model from PyTorch's pre-trained modelzoo.
- How to parse the JSON request, transform the payload and evaluated in the model.

Please see the :doc:`../key-concepts` to learn more general information about Ray Serve.

This tutorial requires Pytorch and Torchvision installed in your system. Ray Serve
is framework agnostic and works with any version of PyTorch.

.. code-block:: bash

    pip install torch torchvision

Let's import Ray Serve and some other helpers.

.. literalinclude:: ../../../../python/ray/serve/examples/doc/tutorial_pytorch.py
    :start-after: __doc_import_begin__
    :end-before: __doc_import_end__


Services are just defined as normal classes with ``__init__`` and ``__call__`` methods.
The ``__call__`` method will be invoked per request.

.. literalinclude:: ../../../../python/ray/serve/examples/doc/tutorial_pytorch.py
    :start-after: __doc_define_servable_begin__
    :end-before: __doc_define_servable_end__

Now that we've defined our services, let's deploy the model to Ray Serve. We will
define an :ref:`endpoint <serve-endpoint>` for the route representing the digit classifier task, a
:ref:`backend <serve-backend>` correspond the physical implementation, and connect them together.

.. literalinclude:: ../../../../python/ray/serve/examples/doc/tutorial_pytorch.py
    :start-after: __doc_deploy_begin__
    :end-before: __doc_deploy_end__

Let's query it!

.. literalinclude:: ../../../../python/ray/serve/examples/doc/tutorial_pytorch.py
    :start-after: __doc_query_begin__
    :end-before: __doc_query_end__