Ray: A Distributed Framework for Emerging AI Applications

Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw +6 more
12/16/2017
cs.DCcs.AIcs.LG

Official code: ray-project/ray (43,577 stars, Apache-2.0, PyTorch, last commit 2026-05-04).

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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Code Implementations(1)

ray-project/rayOfficial100%
PyTorchApache-2.0

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

43,5777,950C, C++Oct 25, 20165 months agoApache-2.0
data-sciencedeep-learningdeploymentdistributedhyperparameter-optimization+15 more

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Cite this paper

@article{moritz2017ray,
  title  = {Ray: A Distributed Framework for Emerging AI Applications},
  author = {Philipp Moritz and Robert Nishihara and Stephanie Wang and Alexey Tumanov and Richard Liaw and Eric Liang and Melih Elibol and Zongheng Yang and William Paul and Michael I. Jordan and Ion Stoica},
  year   = {2017},
  eprint = {1712.05889},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1712.05889}
}

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