ray/doc/source/serve/tutorials/sklearn.md

(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}`../core-apis` to learn more general information about Ray Serve.

Ray Serve is framework agnostic. You can use any version of sklearn.

```bash
pip install scikit-learn
```

Let's import Ray Serve and some other helpers.

```{literalinclude} ../../../../python/ray/serve/examples/doc/tutorial_sklearn.py
:end-before: __doc_import_end__
:start-after: __doc_import_begin__
```

We will train a logistic regression with the iris dataset.

```{literalinclude} ../../../../python/ray/serve/examples/doc/tutorial_sklearn.py
:end-before: __doc_train_model_end__
:start-after: __doc_train_model_begin__
```

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
:end-before: __doc_define_servable_end__
:start-after: __doc_define_servable_begin__
```

Now that we've defined our services, let's deploy the model to Ray Serve. We will
define a Serve deployment that will be exposed over an HTTP route.

```{literalinclude} ../../../../python/ray/serve/examples/doc/tutorial_sklearn.py
:end-before: __doc_deploy_end__
:start-after: __doc_deploy_begin__
```

Let's query it!

```{literalinclude} ../../../../python/ray/serve/examples/doc/tutorial_sklearn.py
:end-before: __doc_query_end__
:start-after: __doc_query_begin__
```