An SDK to integrate cloud solutions such as SageMaker and Databricks with Hopsworks.
hopsworks-cloud-sdk is an SDK to integrate existing cloud solutions such as Amazon SageMaker our Databricks with the Hopsworks platform.
It enables accessing the Hopsworks feature store from SageMaker and Databricks notebooks.
Ensure that your Hopsworks installation is set up correctly: Setting up Hopsworks for the cloud
>>> pip install hopsworks-cloud-sdk
>>> from hops import featurestore >>> featurestore.connect('ec2-w-x-y-z.us-east-2.compute.amazonaws.com', 'my_hopsworks_project') >>> features_df = featurestore.get_features(["my_feature_1", "my_feature_2"])
API for the Hopsworks Feature Store
Hopsworks has a data management layer for machine learning, called a feature store. The feature store enables simple and efficient versioning, sharing, governance and definition of features that can be used to both train machine learning models or to serve inference requests. The featurestore serves as a natural interface between data engineering and data science.
Reading from the featurestore:
from hops import featurestore features_df = featurestore.get_features(["team_budget", "average_attendance", "average_player_age"])
Integration with Sci-kit Learn:
from hops import featurestore train_df = featurestore.get_featuregroup("iris_features", dataframe_type="pandas") x_df = train_df[['sepal_length', 'sepal_width', 'petal_length', 'petal_width']] y_df = train_df[["label"]] X = x_df.values y = y_df.values.ravel() iris_knn = KNeighborsClassifier() iris_knn.fit(X, y)
Integration with Tensorflow:
from hops import featurestore feature_list = ["team_budget", "average_attendance", "average_player_age", "team_position", "sum_attendance", "average_player_rating", "average_player_worth", "sum_player_age", "sum_player_rating", "sum_player_worth", "sum_position", "average_position" ] latest_version = featurestore.get_latest_training_dataset_version("team_position_prediction") featurestore.create_training_dataset( features = feature_list, training_dataset = "team_position_prediction", descriptive_statistics = False, feature_correlation = False, feature_histograms = False, cluster_analysis = False, training_dataset_version = latest_version + 1 ) def create_tf_dataset(): dataset_dir = featurestore.get_training_dataset_path("team_position_prediction") input_files = tf.gfile.Glob(dataset_dir + "/part-r-*") dataset = tf.data.TFRecordDataset(input_files) tf_record_schema = ... # Add tf schema feature_names = ["team_budget", "average_attendance", "average_player_age", "sum_attendance", "average_player_rating", "average_player_worth", "sum_player_age", "sum_player_rating", "sum_player_worth", "sum_position", "average_position" ] label_name = "team_position" def decode(example_proto): example = tf.parse_single_example(example_proto, tf_record_schema) x =  for feature_name in feature_names: x.append(example[feature_name]) y = [tf.cast(example[label_name], tf.float32)] return x,y dataset = dataset.map(decode).shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE).repeat(NUM_EPOCHS) return dataset tf_dataset = create_tf_dataset()
For development details such as how to test and build docs, see this reference: Development.
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