ray

Ray provides a simple, universal API for building distributed applications.

Apache 2.0 128 个版本 Python >=3.10

Ray Team <ray-dev@googlegroups.com>

安装

pip install ray

poetry add ray

pipenv install ray

conda install ray

描述

.. image:: https://github.com/ray-project/ray/raw/master/doc/source/images/ray_header_logo.png

.. image:: https://readthedocs.org/projects/ray/badge/?version=master :target: http://docs.ray.io/en/master/?badge=master

.. image:: https://img.shields.io/badge/Ray-Join%20Slack-blue :target: https://www.ray.io/join-slack

.. image:: https://img.shields.io/badge/Discuss-Ask%20Questions-blue :target: https://discuss.ray.io/

.. image:: https://img.shields.io/twitter/follow/raydistributed.svg?style=social&logo=twitter :target: https://x.com/raydistributed

.. image:: https://img.shields.io/badge/Get_started_for_free-3C8AE9?logo=data%3Aimage%2Fpng%3Bbase64%2CiVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8%2F9hAAAAAXNSR0IArs4c6QAAAERlWElmTU0AKgAAAAgAAYdpAAQAAAABAAAAGgAAAAAAA6ABAAMAAAABAAEAAKACAAQAAAABAAAAEKADAAQAAAABAAAAEAAAAAA0VXHyAAABKElEQVQ4Ea2TvWoCQRRGnWCVWChIIlikC9hpJdikSbGgaONbpAoY8gKBdAGfwkfwKQypLQ1sEGyMYhN1Pd%2B6A8PqwBZeOHt%2FvsvMnd3ZXBRFPQjBZ9K6OY8ZxF%2B0IYw9PW3qz8aY6lk92bZ%2BVqSI3oC9T7%2FyCVnrF1ngj93us%2B540sf5BrCDfw9b6jJ5lx%2FyjtGKBBXc3cnqx0INN4ImbI%2Bl%2BPnI8zWfFEr4chLLrWHCp9OO9j19Kbc91HX0zzzBO8EbLK2Iv4ZvNO3is3h6jb%2BCwO0iL8AaWqB7ILPTxq3kDypqvBuYuwswqo6wgYJbT8XxBPZ8KS1TepkFdC79TAHHce%2F7LbVioi3wEfTpmeKtPRGEeoldSP%2FOeoEftpP4BRbgXrYZefsAI%2BP9JU7ImyEAAAAASUVORK5CYII%3D :target: https://www.anyscale.com/ray-on-anyscale?utm_source=github&utm_medium=ray_readme&utm_campaign=get_started_badge

Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI libraries for simplifying ML compute:

.. image:: https://github.com/ray-project/ray/raw/master/doc/source/images/what-is-ray-padded.svg

.. https://docs.google.com/drawings/d/1Pl8aCYOsZCo61cmp57c7Sja6HhIygGCvSZLi_AuBuqo/edit

Learn more about Ray AI Libraries_:

Data_: Scalable Datasets for ML
Train_: Distributed Training
Tune_: Scalable Hyperparameter Tuning
RLlib_: Scalable Reinforcement Learning
Serve_: Scalable and Programmable Serving

Or more about Ray Core_ and its key abstractions:

Tasks_: Stateless functions executed in the cluster.
Actors_: Stateful worker processes created in the cluster.
Objects_: Immutable values accessible across the cluster.

Learn more about Monitoring and Debugging:

Monitor Ray apps and clusters with the Ray Dashboard <https://docs.ray.io/en/latest/ray-core/ray-dashboard.html>__.
Debug Ray apps with the Ray Distributed Debugger <https://docs.ray.io/en/latest/ray-observability/ray-distributed-debugger.html>__.

Ray runs on any machine, cluster, cloud provider, and Kubernetes, and features a growing ecosystem of community integrations_.

Install Ray with: pip install ray. For nightly wheels, see the Installation page <https://docs.ray.io/en/latest/ray-overview/installation.html>__.

.. _Serve: https://docs.ray.io/en/latest/serve/index.html .. _Data: https://docs.ray.io/en/latest/data/dataset.html .. _Workflow: https://docs.ray.io/en/latest/workflows/ .. _Train: https://docs.ray.io/en/latest/train/train.html .. _Tune: https://docs.ray.io/en/latest/tune/index.html .. _RLlib: https://docs.ray.io/en/latest/rllib/index.html .. _ecosystem of community integrations: https://docs.ray.io/en/latest/ray-overview/ray-libraries.html

Why Ray?

Today's ML workloads are increasingly compute-intensive. As convenient as they are, single-node development environments such as your laptop cannot scale to meet these demands.

Ray is a unified way to scale Python and AI applications from a laptop to a cluster.

With Ray, you can seamlessly scale the same code from a laptop to a cluster. Ray is designed to be general-purpose, meaning that it can performantly run any kind of workload. If your application is written in Python, you can scale it with Ray, no other infrastructure required.

More Information

Documentation_
Ray Architecture whitepaper_
Exoshuffle: large-scale data shuffle in Ray_
Ownership: a distributed futures system for fine-grained tasks_
RLlib paper_
Tune paper_

Older documents:

Ray paper_
Ray HotOS paper_
Ray Architecture v1 whitepaper_

.. _Ray AI Libraries: https://docs.ray.io/en/latest/ray-air/getting-started.html .. _Ray Core: https://docs.ray.io/en/latest/ray-core/walkthrough.html .. _Tasks: https://docs.ray.io/en/latest/ray-core/tasks.html .. _Actors: https://docs.ray.io/en/latest/ray-core/actors.html .. _Objects: https://docs.ray.io/en/latest/ray-core/objects.html .. _Documentation: http://docs.ray.io/en/latest/index.html .. _Ray Architecture v1 whitepaper: https://docs.google.com/document/d/1lAy0Owi-vPz2jEqBSaHNQcy2IBSDEHyXNOQZlGuj93c/preview .. _Ray Architecture whitepaper: https://docs.google.com/document/d/1tBw9A4j62ruI5omIJbMxly-la5w4q_TjyJgJL_jN2fI/preview .. _Exoshuffle: large-scale data shuffle in Ray: https://arxiv.org/abs/2203.05072 .. _Ownership: a distributed futures system for fine-grained tasks: https://www.usenix.org/system/files/nsdi21-wang.pdf .. _Ray paper: https://arxiv.org/abs/1712.05889 .. _Ray HotOS paper: https://arxiv.org/abs/1703.03924 .. _RLlib paper: https://arxiv.org/abs/1712.09381 .. _Tune paper: https://arxiv.org/abs/1807.05118

Getting Involved

.. list-table:: :widths: 25 50 25 25 :header-rows: 1

- Platform
- Purpose
- Estimated Response Time
- Support Level
- Discourse Forum_
- For discussions about development and questions about usage.
- < 1 day
- Community
- GitHub Issues_
- For reporting bugs and filing feature requests.
- < 2 days
- Ray OSS Team
- Slack_
- For collaborating with other Ray users.
- < 2 days
- Community
- StackOverflow_
- For asking questions about how to use Ray.
- 3-5 days
- Community
- Meetup Group_
- For learning about Ray projects and best practices.
- Monthly
- Ray DevRel
- Twitter_
- For staying up-to-date on new features.
- Daily
- Ray DevRel

.. _Discourse Forum: https://discuss.ray.io/ .. _GitHub Issues: https://github.com/ray-project/ray/issues .. _StackOverflow: https://stackoverflow.com/questions/tagged/ray .. _Meetup Group: https://www.meetup.com/Bay-Area-Ray-Meetup/ .. _Twitter: https://x.com/raydistributed .. _Slack: https://www.ray.io/join-slack?utm_source=github&utm_medium=ray_readme&utm_campaign=getting_involved

分类

Programming Language :: Python :: 3.10 Programming Language :: Python :: 3.11 Programming Language :: Python :: 3.12 Programming Language :: Python :: 3.13 Programming Language :: Python :: 3.14

版本列表

2.55.1 2026-04-22

2.55.0 2026-04-15

2.54.1 2026-03-25

2.54.0 2026-02-18

2.53.0 2025-12-20

2.52.1 2025-11-28

2.52.0 2025-11-21

2.51.2 2025-11-29

2.51.1 2025-11-01

2.51.0 2025-10-29

2.50.1 2025-10-18

2.50.0 2025-10-10

2.49.2 2025-09-19

2.49.1 2025-09-03

2.49.0 2025-08-26

2.48.0 2025-07-18

2.47.1 2025-06-17

2.47.0 2025-06-11

2.46.0 2025-05-07

2.45.0 2025-04-29

2.44.1 2025-03-27

2.44.0 2025-03-21

2.43.0 2025-02-27

2.42.1 2025-02-11

2.42.0 2025-02-04

2.41.0 2025-01-23

2.40.0 2024-12-03

2.39.0 2024-11-12

2.38.0 2024-10-23

2.37.0 2024-09-24

2.36.1 2024-09-23

2.36.0 2024-09-16

2.35.0 2024-08-27

2.34.0 2024-07-30

2.33.0 2024-07-25

2.32.0 2024-07-10

2.32.0rc0 2024-07-03

2.31.0 2024-06-26

2.30.0 2024-06-20

2.24.0 2024-06-06

2.23.0 2024-05-22

2.22.0 2024-05-14

2.21.0 2024-05-08

2.20.0 2024-05-01

2.12.0 2024-04-25

2.11.0 2024-04-17

2.10.0 2024-03-21

2.9.3 2024-02-22

2.9.2 2024-02-05

2.9.1 2024-01-19

2.9.0 2023-12-21

2.8.1 2023-12-01

2.8.0 2023-11-03

2.7.2 2023-12-04

2.7.1 2023-10-09

2.7.0 2023-09-17

2.7.0rc0 2023-09-02

2.6.3 2023-08-15

2.6.2 2023-08-03

2.6.1 2023-07-24

2.6.0 2023-07-21

2.5.1 2023-06-21

2.5.0 2023-06-08

2.4.0 2023-04-25

2.3.1 2023-03-25

2.3.0 2023-02-24

2.3.0rc0 2023-02-23

2.2.0 2022-12-13

2.1.0 2022-11-08

2.0.1 2022-10-22

2.0.0 2022-08-23

1.13.0 2022-06-09

1.12.1 2022-05-16

1.12.0 2022-04-14

1.11.1 2022-04-29

1.11.0 2022-03-09

1.10.0 2022-02-04

1.9.2 2022-01-11

1.9.1 2021-12-22

1.9.0 2021-12-03

1.8.0 2021-11-02

1.7.1 2021-10-22

1.7.0 2021-10-07

1.6.0 2021-08-23

1.5.2 2021-08-12

1.5.1 2021-07-31

1.5.0 2021-07-26

1.4.1 2021-06-30

1.4.0 2021-06-07

1.3.0 2021-04-22

1.2.0 2021-02-13

1.1.0 2020-12-24

1.0.1 2020-11-10

1.0.0 2020-09-30

0.8.7 2020-08-13

0.8.6 2020-06-24

0.8.5 2020-05-07

0.8.4 2020-04-02

0.8.3 2020-03-25

0.8.2 2020-02-24

0.8.1 2020-01-27

0.8.0 2019-12-17

0.7.7 2019-12-16

0.7.6 2019-10-24

0.7.5 2019-09-25

0.7.4 2019-09-05

0.7.3 2019-08-04

0.7.2 2019-07-03

0.7.1 2019-06-11

0.7.0 2019-05-18

0.6.6 2019-04-19

0.6.5 2019-03-25

0.6.4 2019-03-06

0.6.3 2019-02-07

0.6.2 2019-01-17

0.6.1 2018-12-24

0.6.0 2018-12-01

0.5.3 2018-09-28

0.5.2 2018-08-29

0.5.0 2018-07-07

0.4.0 2018-03-27

0.3.1 2018-02-04

0.3.0 2017-11-28

0.2.2 2017-11-02

0.2.1 2017-10-01

0.2.0 2017-08-30

0.1.2 2017-06-27

0.1.1 2017-06-03