Article

Ray: a distributed framework for emerging AI applications

Authors:

Philipp Moritz,

Robert Nishihara,

Stephanie Wang,

Alexey Tumanov,

Michael I. Jordan,

Ion StoicaAuthors Info & Claims

OSDI'18: Proceedings of the 13th USENIX conference on Operating Systems Design and Implementation

Pages 561 - 577

Published: 08 October 2018 Publication History

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 paper, 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.

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cover image ACM Other conferences

OSDI'18: Proceedings of the 13th USENIX conference on Operating Systems Design and Implementation

October 2018

815 pages

ISBN:9781931971478

Program Chairs:
Andrea Arpaci-Dusseau
University of Wisconsin-Madison
,
Geoff Voelker
University of California, San Diego

Sponsors

NetApp
Google Inc.
NSF
Microsoft: Microsoft
Facebook: Facebook

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SIGOPS: ACM Special Interest Group on Operating Systems

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USENIX Association

United States

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Published: 08 October 2018

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