skl.md

description
Documentation on how to export a Scikit-learn model to the model registry

How To Export a Scikit-learn Model

Introduction

In this guide you will learn how to export a Scikit-learn model and register it in the Model Registry.

Code

Step 1: Connect to Hopsworks

=== "Python" ```python import hopsworks

project = hopsworks.login()

# get Hopsworks Model Registry handle
mr = project.get_model_registry()
```

Step 2: Train

Define your Scikit-learn model and run the training loop.

=== "Python" ```python # Define a model iris_knn = KNeighborsClassifier(..)

iris_knn.fit(..)
```

Step 3: Export to local path

Export the Scikit-learn model to a directory on the local filesystem.

=== "Python" ```python model_file = "skl_knn.pkl"

joblib.dump(iris_knn, model_file)
```

Step 4: Register model in registry

Use the ModelRegistry.sklearn.create_model(..) function to register a model as a Scikit-learn model. Define a name, and attach optional metrics for your model, then invoke the save() function with the parameter being the path to the local directory where the model was exported to.

=== "Python" ```python # Model evaluation metrics metrics = {'accuracy': 0.92}

skl_model = mr.sklearn.create_model("skl_model", metrics=metrics)

skl_model.save(model_file)
```

Going Further

You can attach an Input Example and a Model Schema to your model to document the shape and type of the data the model was trained on.