SFBigAnalytics_20180912:Project Hydrogen: State-of-the-Art Deep Learning on Apache Spark
Project Hydrogen: State-of-the-Art Deep Learning on Apache Spark (Databricks)
Abstract: Big data and AI are joined at the hip: the best AI applications require massive amounts of constantly updated training data to build state-of-the-art models AI has always been on of the most exciting applications of big data and Apache Spark. Increasingly Spark users want to integrate Spark with distributed deep learning and machine learning frameworks built for state-of-the-art training. On the other side, increasingly DL/AI users want to handle large and complex data scenarios needed for their production pipelines.
This talk introduces a new project that substantially improves the performance and fault-recovery of distributed deep learning and machine learning frameworks on Spark. We will introduce the major directions and provide progress updates, including 1) barrier execution mode for distributed DL training, 2) fast data exchange between Spark and DL frameworks, and 3) accelerator-awareness scheduling.
Bio: Xiangrui Meng is an Apache Spark PMC member and a software engineer at Databricks. His main interests center around developing and implementing scalable algorithms for scientific applications. He has been actively involved in the development and maintenance of Spark MLlib since he joined Databricks. Before Databricks, he worked as an applied research engineer at LinkedIn, where he was the main developer of an offline machine learning framework in Hadoop MapReduce. His Ph.D. work at Stanford is on randomized algorithms for large-scale linear regression problems.