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.. _serve-tensorflow-tutorial:
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Keras and Tensorflow Tutorial
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=============================
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In this guide, we will train and deploy a simple Tensorflow neural net.
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In particular, we show:
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- How to load the model from file system in your Ray Serve definition
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- How to parse the JSON request and evaluated in Tensorflow
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Please see the :doc:`../key-concepts` to learn more general information about Ray Serve.
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Ray Serve is framework agnostic -- you can use any version of Tensorflow.
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However, for this tutorial, we use Tensorflow 2 and Keras. Please make sure you have
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Tensorflow 2 installed.
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.. code-block:: bash
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pip install "tensorflow>=2.0"
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Let's import Ray Serve and some other helpers.
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.. literalinclude:: ../../../../python/ray/serve/examples/doc/tutorial_tensorflow.py
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:start-after: __doc_import_begin__
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:end-before: __doc_import_end__
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We will train a simple MNIST model using Keras.
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.. literalinclude:: ../../../../python/ray/serve/examples/doc/tutorial_tensorflow.py
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:start-after: __doc_train_model_begin__
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:end-before: __doc_train_model_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_tensorflow.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_tensorflow.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_tensorflow.py
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:start-after: __doc_query_begin__
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:end-before: __doc_query_end__ |