{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/flexible-and-scalable-deep-learning-with","title":"Flexible and Scalable Deep Learning with MMLSpark","arxiv_id":"1804.04031","date":"2018-04-11","proceeding":null,"authors":["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"],"abstract":"In this work we detail a novel open source library, called MMLSpark, that\ncombines the flexible deep learning library Cognitive Toolkit, with the\ndistributed computing framework Apache Spark. To achieve this, we have\ncontributed Java Language bindings to the Cognitive Toolkit, and added several\nnew components to the Spark ecosystem. In addition, we also integrate the\npopular image processing library OpenCV with Spark, and present a tool for the\nautomated generation of PySpark wrappers from any SparkML estimator and use\nthis tool to expose all work to the PySpark ecosystem. Finally, we provide a\nlarge library of tools for working and developing within the Spark ecosystem.\nWe apply this work to the automated classification of Snow Leopards from camera\ntrap images, and provide an end to end solution for the non-profit conservation\norganization, the Snow Leopard Trust.","url_abs":"http://arxiv.org/abs/1804.04031v1","url_pdf":"http://arxiv.org/pdf/1804.04031v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"flexible-and-scalable-deep-learning-with","repo_url":"https://github.com/Azure/mmlspark","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"distributed-computing","task_name":"Distributed Computing"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}