Ray: A Distributed Framework for Emerging AI Applications

Michael I. Jordan, University of California, Berkeley

Abstract: 

The next generation of AI applications will continuously interact with the environment and learn from these interactions. These applications impose new and demanding systems requirements, both in terms of performance and flexibility. In this talk, we consider these requirements and present Ray—a distributed system to address them. Ray implements a unified interface that can express both task-parallel and actor-based computations, supported by a single dynamic execution engine. To meet the performance requirements, Ray employs a distributed scheduler and a distributed and fault-tolerant store to manage the system’s control state. In our experiments, we demonstrate scaling beyond 1.8 million tasks per second and better performance than existing specialized systems for several challenging reinforcement learning applications.

Michael I. Jordan, University of California, Berkeley

Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. He received his Masters in Mathematics from Arizona State University, and earned his PhD in Cognitive Science in 1985 from the University of California, San Diego. He was a professor at MIT from 1988 to 1998. His research interests bridge the computational, statistical, cognitive and biological sciences, and have focused in recent years on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines and applications to problems in distributed computing systems, natural language processing, signal processing and statistical genetics. He is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He is a Fellow of the American Association for the Advancement of Science. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. He is a Fellow of the AAAS (American Association for the Advancement of Science), AAAI (Association for the Advancement of Artificial Intelligence), ACM (Association for Computing Machinery), ASA (American Statistical Association), IEEE (Institute of Electrical and Electronic Engineers), IMS (Institute of Mathematical Statistics), ISBA (International Society for Bayesian Analysis), and SIAM (Society for Industrial and Applied Mathematics). His work has been cited over 125,000 times by other scientists all over the world and in 2016 he was identified as the “most influential computer scientist” based on analysis of the published literature by the Semantic Scholar project.

BibTeX
@conference {232933,
author = {Michael I. Jordan},
title = {Ray: A Distributed Framework for Emerging {AI} Applications},
year = {2019},
address = {Santa Clara, CA},
publisher = {{USENIX} Association},
month = may,
}