from sagemaker.sklearn.estimator import SKLearn

script_path = 'sklearn_nearest_neighbors.py'
sess = sagemaker.Session()

# run the Scikit-Learn script
sklearn = SKLearn(
    entry_point=script_path,
    train_instance_type="ml.m5.large",
    role=role,
    sagemaker_session=sess,
    hyperparameters={'n_neighbors': 10, 'metric': 'cosine'})

sklearn.fit({'train': 's3://data-science-wine-reviews/nearest_neighbors/data/wine_review_vectors.csv'})

# deploy the model to a SageMaker endpoint
predictor = sklearn.deploy(initial_instance_count=1, instance_type="ml.m5.large")