hopsworks-cloud-sdk

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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.

Quick Start

Ensure that your Hopsworks installation is set up correctly: Setting up Hopsworks for the cloud

To Install:

>>> pip install hopsworks-cloud-sdk

Sample usage:

>>> 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"])

Documentation

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.

API documentation

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()

Feature Visualizations:

Visualizing feature distributions
Visualizing feature correlations

Development Instructions

For development details such as how to test and build docs, see this reference: Development.