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47 lines
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1.7 KiB
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.. _serve-pytorch-tutorial:
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PyTorch Tutorial
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================
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In this guide, we will load and serve a PyTorch Resnet Model.
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In particular, we show:
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- How to load the model from PyTorch's pre-trained modelzoo.
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- How to parse the JSON request, transform the payload and evaluated in the model.
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Please see the :ref:`overview <rayserve-overview>` to learn more general information about Ray Serve.
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This tutorial requires Pytorch and Torchvision installed in your system. Ray Serve
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is :ref:`framework agnostic <serve_frameworks>` and work with any version of PyTorch.
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.. code-block:: bash
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pip install torch torchvision
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Let's import Ray Serve and some other helpers.
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.. literalinclude:: ../../../../python/ray/serve/examples/doc/tutorial_pytorch.py
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:start-after: __doc_import_begin__
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:end-before: __doc_import_end__
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Services are just defined as normal classes with ``__init__`` and ``__call__`` methods.
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The ``__call__`` method will be invoked per request.
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.. literalinclude:: ../../../../python/ray/serve/examples/doc/tutorial_pytorch.py
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:start-after: __doc_define_servable_begin__
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:end-before: __doc_define_servable_end__
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Now that we've defined our services, let's deploy the model to Ray Serve. We will
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define an :ref:`endpoint <serve-endpoint>` for the route representing the digit classifier task, a
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:ref:`backend <serve-backend>` correspond the physical implementation, and connect them together.
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.. literalinclude:: ../../../../python/ray/serve/examples/doc/tutorial_pytorch.py
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:start-after: __doc_deploy_begin__
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:end-before: __doc_deploy_end__
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Let's query it!
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.. literalinclude:: ../../../../python/ray/serve/examples/doc/tutorial_pytorch.py
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:start-after: __doc_query_begin__
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:end-before: __doc_query_end__ |