hopsworks-cloud-sdk 2.0.0.2

An SDK to integrate cloud solutions such as SageMaker and Databricks with Hopsworks.

===================
hopsworks-cloud-sdk
===================

|Downloads| |PypiStatus| |PythonVersions|

.. |Downloads| image:: https://pepy.tech/badge/hopsworks-cloud-sdk
:target: https://pepy.tech/project/hopsworks-cloud-sdk
.. |PypiStatus| image:: https://img.shields.io/pypi/v/hopsworks-cloud-sdk.svg
:target: https://pypi.org/project/hopsworks-cloud-sdk
.. |PythonVersions| image:: https://img.shields.io/pypi/pyversions/hopsworks-cloud-sdk.svg
:target: https://travis-ci.org/hopsworks-cloud-sdk

`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 <https://hops.readthedocs.io/en/latest/user_guide/hopsworks/featurestore.html#connecting-from-amazon-sagemaker>`_

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"])

--------
Examples
--------

`Examples for using the Cloud SDK on SageMaker <https://github.com/logicalclocks/hops-examples/blob/master/notebooks/featurestore/aws/SageMakerFeaturestoreTourPython.ipynb>`_

------------------------------------
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 <http://hopsworks-cloud-sdk.logicalclocks.com/>`_

**Reading from the featurestore**:

.. code-block:: python

from hops import featurestore
features_df = featurestore.get_features(["team_budget", "average_attendance", "average_player_age"])

**Integration with Sci-kit Learn**:

.. code-block:: python

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**:

.. code-block:: python

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**:

.. _feature_plots1.png: imgs/feature_plots1.png
.. figure:: imgs/feature_plots1.png
:alt: Visualizing feature distributions
:target: `feature_plots1.png`_
:align: center
:scale: 75 %
:figclass: align-center


.. _feature_plots2.png: imgs/feature_plots2.png
.. figure:: imgs/feature_plots2.png
:alt: Visualizing feature correlations
:target: `feature_plots2.png`_
:align: center
:scale: 75 %
:figclass: align-center

------------------------
Development Instructions
------------------------

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

.. _Development: ./Development.rst