Hopsworks Feature Store API

HSFS is the new library to interact with the Hopsworks Feature Store. The library makes creating new features, feature groups and training datasets easier.

The library can be used in two modes:

• Spark mode : For data engineering jobs that create and write features into the feature store or generate training datasets. It requires a Spark environment such as the one provided in the Hopsworks platform or Databricks. In Spark mode, HSFS provides binding both for Python and JVM languages.

• Python mode : For data science jobs to explore the features available in the feature store, generate training datasets and feed them in a training pipeline. Python mode requires just a Python interpreter and can be used both in Hopsworks from Python Jobs/Jupyter Kernels, Amazon SageMaker, KubeFlow.

The library automatically configures itself based on the environment it is run.

You can read more about the Hopsworks Feature Store and its concepts here

Getting Started

Instantiate a connection and get the project feature store handler

import hsfs

connection = hsfs.connection()
fs = connection.get_feature_store()

Create a new feature group

fg = fs.create_feature_group("rain", 
                        version=1,
                        description="Rain features",
                        primary_key=['date', 'location_id'],
                        online_enabled=True)

fg.save(dataframe)

Join features together

feature_join = rain_fg.select_all()
                    .join(temperature_fg.select_all(), ["date", "location_id"])
                    .join(location_fg.select_all()))

feature_join.show(5)

Use the query object to create a training dataset:

td = fs.create_training_dataset("training_dataset",
                           version=1,
                           data_format="tfrecords",
                           description="A test training dataset saved in TfRecords format",
                           splits={'train': 0.7, 'test': 0.2, 'validate': 0.1})

td.save(feature_join)

Feed the training dataset to a TensorFlow model:

train_input_feeder = training_dataset.feed(target_name='label',split='train', is_training=True)
train_input = train_input_feeder.tf_record_dataset()

You can find more examples on how to use the library in our hops-examples repository.

Issues

Please report any issue using Github issue tracking