Flexible and Scalable Deep Learning with MMLSpark

Mark Hamilton; Sudarshan Raghunathan; Akshaya Annavajhala; Danil Kirsanov; Eduardo de Leon; Eli Barzilay; Ilya Matiach; Joe Davison; Maureen Busch; Miruna Oprescu; Ratan Sur; Roope Astala; Tong Wen; ChangYoung Park

arXiv · 1804.04031

Flexible and Scalable Deep Learning with MMLSpark

Mark Hamilton·Sudarshan Raghunathan·Akshaya Annavajhala·Danil Kirsanov·Eduardo de Leon·Eli Barzilay·Ilya Matiach·Joe Davison·Maureen Busch·Miruna Oprescu·Ratan Sur·Roope Astala·Tong Wen·ChangYoung Park

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Abstract

In this work we detail a novel open source library, called MMLSpark, that combines the flexible deep learning library Cognitive Toolkit, with the distributed computing framework Apache Spark. To achieve this, we have contributed Java Language bindings to the Cognitive Toolkit, and added several new components to the Spark ecosystem. In addition, we also integrate the popular image processing library OpenCV with Spark, and present a tool for the automated generation of PySpark wrappers from any SparkML estimator and use this tool to expose all work to the PySpark ecosystem. Finally, we provide a large library of tools for working and developing within the Spark ecosystem. We apply this work to the automated classification of Snow Leopards from camera trap images, and provide an end to end solution for the non-profit conservation organization, the Snow Leopard Trust.

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Mark Hamilton, Sudarshan Raghunathan, Akshaya Annavajhala, Danil Kirsanov, Eduardo de Leon, Eli Barzilay, Ilya Matiach, Joe Davison, Maureen Busch, Miruna Oprescu, Ratan Sur, Roope Astala, Tong Wen, ChangYoung Park. 2018-04-11. Flexible and Scalable Deep Learning with MMLSpark. https://arxiv.org/abs/1804.04031

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