{"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/ray-a-distributed-framework-for-emerging-ai","title":"Ray: A Distributed Framework for Emerging AI Applications","arxiv_id":"1712.05889","date":"2017-12-16","proceeding":null,"authors":["Philipp Moritz","Robert Nishihara","Stephanie Wang","Alexey Tumanov","Richard Liaw","Eric Liang","Melih Elibol","Zongheng Yang","William Paul","Michael. I. Jordan","Ion Stoica"],"abstract":"The next generation of AI applications will continuously interact with the\nenvironment and learn from these interactions. These applications impose new\nand demanding systems requirements, both in terms of performance and\nflexibility. In this paper, we consider these requirements and present Ray---a\ndistributed system to address them. Ray implements a unified interface that can\nexpress both task-parallel and actor-based computations, supported by a single\ndynamic execution engine. To meet the performance requirements, Ray employs a\ndistributed scheduler and a distributed and fault-tolerant store to manage the\nsystem's control state. In our experiments, we demonstrate scaling beyond 1.8\nmillion tasks per second and better performance than existing specialized\nsystems for several challenging reinforcement learning applications.","url_abs":"http://arxiv.org/abs/1712.05889v2","url_pdf":"http://arxiv.org/pdf/1712.05889v2.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":"ray-a-distributed-framework-for-emerging-ai","repo_url":"https://github.com/ray-project/ray","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"ray-a-distributed-framework-for-emerging-ai","repo_url":"https://github.com/AmeerHajAli/ray2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"ray-a-distributed-framework-for-emerging-ai","repo_url":"https://github.com/Neuraxio/Neuraxle","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"ray-a-distributed-framework-for-emerging-ai","repo_url":"https://github.com/flow-project/ray","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[{"method_slug":"virtual-data-augmentation","method_name":"Virtual Data Augmentation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1712.05889","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}