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54 lines
1.7 KiB
Markdown
54 lines
1.7 KiB
Markdown
(serve-sklearn-tutorial)=
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# Scikit-Learn Tutorial
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In this guide, we will train and deploy a simple Scikit-Learn classifier.
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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 sklearn model
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Please see the [Key Concepts](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 sklearn.
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```bash
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pip install scikit-learn
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```
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Let's import Ray Serve and some other helpers.
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```{literalinclude} ../../../../python/ray/serve/examples/doc/tutorial_sklearn.py
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:end-before: __doc_import_end__
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:start-after: __doc_import_begin__
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```
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We will train a logistic regression with the iris dataset.
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```{literalinclude} ../../../../python/ray/serve/examples/doc/tutorial_sklearn.py
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:end-before: __doc_train_model_end__
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:start-after: __doc_train_model_begin__
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```
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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_sklearn.py
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:end-before: __doc_define_servable_end__
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:start-after: __doc_define_servable_begin__
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```
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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 a Serve deployment that will be exposed over an HTTP route.
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```{literalinclude} ../../../../python/ray/serve/examples/doc/tutorial_sklearn.py
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:end-before: __doc_deploy_end__
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:start-after: __doc_deploy_begin__
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```
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Let's query it!
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```{literalinclude} ../../../../python/ray/serve/examples/doc/tutorial_sklearn.py
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:end-before: __doc_query_end__
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:start-after: __doc_query_begin__
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```
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