Keras is a high-level deep learning framework originally developed as part of the research project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System) and now on Github as an open source project. Keras employs an MIT license.

Keras is a high-level API which calls into lower-level deep learning libraries. It supports TensorFlow, Theano, and CNTK.

In the sections below, we provide guidance on installing Keras on Databricks and give an example of running Keras programs. See Integrating Deep Learning Libraries with Apache Spark for an example of integrating a deep learning library with Spark.


This guide is not a comprehensive guide on Keras. Please also refer to the Keras website.

Install Keras

Keras may be installed as a regular Databricks Library from PyPi. Use the keras PyPi library. See Libraries for more info on Databricks Libraries.


In the upcoming TensorFlow 1.1 release, Keras will be included within the TensorFlow package under tf.contrib.keras.

Use Keras on a single node

To test and migrate single-machine Keras workflows, you can start with a driver-only cluster on Databricks by setting the number of workers to zero. Though Apache Spark is not functional under this setting, it is a cost-effective way to run single-machine Keras workflows.