[Serve] Rename RayServe -> "Ray Serve" in Documentation (#8504)

doc/source/serve/tutorials/pytorch-tutorial.rst

@@ -0,0 +1,47 @@
+.. _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 :ref:`overview <rayserve-overview>` to learn more general information about Ray Serve.
+
+This tutorial requires Pytorch and Torchvision installed in your system. Ray Serve
+is :ref:`framework agnostic <serve_frameworks>` and work 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__