Scikit-Learn

About this page

This is an API reference for using Scikit-Learn in BentoML. Please refer to Scikit-Learn Guide for more information about how to use Scikit-learn in BentoML.

Note

You can find more examples for Scikit-Learn in our BentoML/examples directory.

bentoml.sklearn.save_model(name: Tag | str, model: SklearnModel, *, signatures: ModelSignaturesType | None = None, labels: t.Dict[str, str] | None = None, custom_objects: t.Dict[str, t.Any] | None = None, external_modules: t.List[ModuleType] | None = None, metadata: t.Dict[str, t.Any] | None = None) → bentoml.Model

Save a model instance to BentoML modelstore.

Parameters:

• name – Name for given model instance. This should pass Python identifier check.

• model – Instance of model to be saved.

• signatures – Methods to expose for running inference on the target model. Signatures are used for creating Runner instances when serving model with bentoml.Service

• labels – user-defined labels for managing models, e.g. team=nlp, stage=dev

• custom_objects – user-defined additional python objects to be saved alongside the model, e.g. a tokenizer instance, preprocessor function, model configuration json

• external_modules – user-defined additional python modules to be saved alongside the model or custom objects, e.g. a tokenizer module, preprocessor module, model configuration module

• metadata – Custom metadata for given model.

Returns:

A tag with a format name:version where name is the user-defined model’s name, and a generated version.

Return type:

Tag

Examples:

import bentoml

from sklearn.datasets import load_iris
from sklearn.neighbors import KNeighborsClassifier

model = KNeighborsClassifier()
iris = load_iris()
X = iris.data[:, :4]
Y = iris.target
model.fit(X, Y)

bento_model = bentoml.sklearn.save_model('kneighbors', model)

bentoml.sklearn.load_model(bento_model: str | Tag | Model) → SklearnModel

Load the scikit-learn model with the given tag from the local BentoML model store.

Parameters:

bento_model – Either the tag of the model to get from the store, or a BentoML ~bentoml.Model instance to load the model from.

Returns:

The scikit-learn model loaded from the model store or BentoML Model.

Example:

import bentoml
sklearn = bentoml.sklearn.load_model('my_model:latest')

bentoml.sklearn.get(tag: t.Union[Tag, str], *, _model_store: ModelStore = <simple_di.providers.SingletonFactory object>, model_aliases: t.Dict[str, str] = <simple_di.providers.Static object>) → Model

Get a model by tag. If the tag is a string, it will be looked up in the model_aliases dict.