Quick Start

The following creates a model and model version set, and then performs some common operations on the model version set:

import tempfile
from ads.model import SklearnModel
from ads.model import ModelVersionSet
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

# Create a model version set
mvs = ModelVersionSet(
    name = "my_test_model_version_set",
    description = "A test creating the model version set using ModelVersionSet")
mvs.create()

# Create a Sklearn model
iris = load_iris()
X, y = iris.data, iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)
sklearn_estimator = LogisticRegression()
sklearn_estimator.fit(X_train, y_train)


# Create an SklearnModel object
sklearn_model = SklearnModel(estimator=sklearn_estimator, artifact_dir=tempfile.mkdtemp())
sklearn_model.prepare(inference_conda_env="dbexp_p38_cpu_v1")

# Save the model and add it to the model version set
model_id = sklearn_model.save(
    display_name="Quickstart model",
    model_version_set=mvs,
    version_label="Version 1")

# Print a list of models in the model version set
for item in ModelVersionSet.list():
    print(item)
    print("---------")

# Update the model version set
mvs.description = "Updated description of the model version set"
mvs.update()

# Delete the model version set and associated models
# mvs.delete(delete_model=True)