ray/doc/source/serve/tutorials/sklearn.rst
2020-05-27 11:03:28 -05:00

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.. _serve-sklearn-tutorial:
Scikit-Learn Tutorial
=====================
In this guide, we will train and deploy a simple Scikit-Learn classifier.
In particular, we show:
- How to load the model from file system in your Ray Serve definition
- How to parse the JSON request and evaluated in sklearn model
Please see the :doc:`../key-concepts` to learn more general information about Ray Serve.
Ray Serve is framework agnostic. You can use any version of sklearn.
.. code-block:: bash
pip install scikit-learn
Let's import Ray Serve and some other helpers.
.. literalinclude:: ../../../../python/ray/serve/examples/doc/tutorial_sklearn.py
:start-after: __doc_import_begin__
:end-before: __doc_import_end__
We will train a logistic regression with the iris dataset.
.. literalinclude:: ../../../../python/ray/serve/examples/doc/tutorial_sklearn.py
:start-after: __doc_train_model_begin__
:end-before: __doc_train_model_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_sklearn.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 classifier task, a
:ref:`backend <serve-backend>` correspond the physical implementation, and connect them together.
.. literalinclude:: ../../../../python/ray/serve/examples/doc/tutorial_sklearn.py
:start-after: __doc_deploy_begin__
:end-before: __doc_deploy_end__
Let's query it!
.. literalinclude:: ../../../../python/ray/serve/examples/doc/tutorial_sklearn.py
:start-after: __doc_query_begin__
:end-before: __doc_query_end__