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

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for artur1993 from gravatar.com artur1993 Avatar for aslonnie from gravatar.com aslonnie Avatar for bveeramani from gravatar.com bveeramani Avatar for ekl from gravatar.com ekl Avatar for eoakes from gravatar.com eoakes Avatar for jjyao from gravatar.com jjyao Avatar for matthewdeng from gravatar.com matthewdeng Avatar for pcmoritz from gravatar.com pcmoritz Avatar for richardliaw from gravatar.com richardliaw Avatar for rkn from gravatar.com rkn Avatar for swang from gravatar.com swang

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: Apache 2.0
  • Author: Ray Team
  • Tags ray , distributed , parallel , machine-learning , hyperparameter-tuningreinforcement-learning , deep-learning , serving , python
  • Requires: Python >=3.8
Classifiers
Report project as malware

Project description

https://github.com/ray-project/ray/raw/master/doc/source/images/ray_header_logo.png https://readthedocs.org/projects/ray/badge/?version=master https://img.shields.io/badge/Ray-Join%20Slack-blue https://img.shields.io/badge/Discuss-Ask%20Questions-blue https://img.shields.io/twitter/follow/raydistributed.svg?style=social&logo=twitter

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:

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

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.

Monitor and debug Ray applications and clusters using the Ray dashboard.

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.

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

Older documents:

Getting Involved

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

Project details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for artur1993 from gravatar.com artur1993 Avatar for aslonnie from gravatar.com aslonnie Avatar for bveeramani from gravatar.com bveeramani Avatar for ekl from gravatar.com ekl Avatar for eoakes from gravatar.com eoakes Avatar for jjyao from gravatar.com jjyao Avatar for matthewdeng from gravatar.com matthewdeng Avatar for pcmoritz from gravatar.com pcmoritz Avatar for richardliaw from gravatar.com richardliaw Avatar for rkn from gravatar.com rkn Avatar for swang from gravatar.com swang

Unverified details

These details have not been verified by PyPI
Project links
Meta
  • License: Apache 2.0
  • Author: Ray Team
  • Tags ray , distributed , parallel , machine-learning , hyperparameter-tuningreinforcement-learning , deep-learning , serving , python
  • Requires: Python >=3.8
Classifiers

Release history Release notifications | RSS feed

2.54.0

2.53.0

2.52.1

2.52.0

2.51.2

2.51.1

2.51.0

2.50.1

2.50.0

2.49.2

2.49.1

2.49.0

2.48.0

2.47.1

2.47.0

2.46.0

2.45.0

2.44.1

2.44.0

2.43.0

2.42.1

2.42.0

2.41.0

2.40.0

2.39.0

2.38.0

2.37.0

2.36.1

2.36.0

2.35.0

2.34.0

2.33.0

2.32.0

2.32.0rc0 pre-release

2.31.0

2.30.0

2.24.0

2.23.0

2.22.0

2.21.0

2.20.0

2.12.0

2.11.0

2.10.0

2.9.3

2.9.2

This version

2.9.1

2.9.0

2.8.1

2.8.0

2.7.2

2.7.1

2.7.0

2.7.0rc0 pre-release

2.6.3

2.6.2

2.6.1

2.6.0

2.5.1

2.5.0

2.4.0

2.3.1

2.3.0

2.3.0rc0 pre-release

2.2.0

2.1.0

2.0.1

2.0.0

2.0.0rc1 pre-release

2.0.0rc0 pre-release

1.13.0

1.12.1

1.12.0

1.12.0rc1 pre-release

1.11.1

1.11.0

1.11.0rc1 pre-release

1.11.0rc0 pre-release

1.10.0

1.10.0rc0 pre-release

1.9.2

1.9.1

1.9.1rc0 pre-release

1.9.0

1.9.0rc2 pre-release

1.9.0rc1 pre-release

1.8.0

1.7.1 yanked

Reason this release was yanked:

https://github.com/ray-project/ray/issues/19813

1.7.0

1.7.0rc0 pre-release

1.6.0

1.5.2

1.5.1

1.5.0

1.4.1

1.4.0

1.4.0rc2 pre-release

1.4.0rc1 pre-release

1.3.0

1.2.0

1.1.0

1.0.1.post1

1.0.1

1.0.0

1.0.0rc2 pre-release

1.0.0rc1 pre-release

1.0.0rc0 pre-release

0.8.7

0.8.6

0.8.5

0.8.4

0.8.3

0.8.2

0.8.1

0.8.0

0.7.7

0.7.6

0.7.5

0.7.4

0.7.3

0.7.2

0.7.1

0.7.0

0.6.6

0.6.5

0.6.4

0.6.3

0.6.2

0.6.1

0.6.0

0.5.3

0.5.2

0.5.0

0.4.0

0.3.1

0.3.0

0.2.2

0.2.1

0.2.0

0.1.2

0.1.1

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

ray-2.9.1-cp311-cp311-win_amd64.whl (25.6 MB view details)

Uploaded CPython 3.11Windows x86-64

ray-2.9.1-cp311-cp311-manylinux2014_x86_64.whl (65.4 MB view details)

Uploaded CPython 3.11

ray-2.9.1-cp311-cp311-manylinux2014_aarch64.whl (64.5 MB view details)

Uploaded CPython 3.11

ray-2.9.1-cp311-cp311-macosx_11_0_arm64.whl (63.6 MB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

ray-2.9.1-cp311-cp311-macosx_10_15_x86_64.whl (66.0 MB view details)

Uploaded CPython 3.11macOS 10.15+ x86-64

ray-2.9.1-cp310-cp310-win_amd64.whl (25.2 MB view details)

Uploaded CPython 3.10Windows x86-64

ray-2.9.1-cp310-cp310-manylinux2014_x86_64.whl (64.8 MB view details)

Uploaded CPython 3.10

ray-2.9.1-cp310-cp310-manylinux2014_aarch64.whl (64.0 MB view details)

Uploaded CPython 3.10

ray-2.9.1-cp310-cp310-macosx_11_0_arm64.whl (63.2 MB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

ray-2.9.1-cp310-cp310-macosx_10_15_x86_64.whl (65.7 MB view details)

Uploaded CPython 3.10macOS 10.15+ x86-64

ray-2.9.1-cp39-cp39-win_amd64.whl (25.2 MB view details)

Uploaded CPython 3.9Windows x86-64

ray-2.9.1-cp39-cp39-manylinux2014_x86_64.whl (64.9 MB view details)

Uploaded CPython 3.9

ray-2.9.1-cp39-cp39-manylinux2014_aarch64.whl (64.0 MB view details)

Uploaded CPython 3.9

ray-2.9.1-cp39-cp39-macosx_11_0_arm64.whl (63.2 MB view details)

Uploaded CPython 3.9macOS 11.0+ ARM64

ray-2.9.1-cp39-cp39-macosx_10_15_x86_64.whl (65.7 MB view details)

Uploaded CPython 3.9macOS 10.15+ x86-64

ray-2.9.1-cp38-cp38-win_amd64.whl (25.3 MB view details)

Uploaded CPython 3.8Windows x86-64

ray-2.9.1-cp38-cp38-manylinux2014_x86_64.whl (64.9 MB view details)

Uploaded CPython 3.8

ray-2.9.1-cp38-cp38-manylinux2014_aarch64.whl (64.1 MB view details)

Uploaded CPython 3.8

ray-2.9.1-cp38-cp38-macosx_11_0_arm64.whl (63.2 MB view details)

Uploaded CPython 3.8macOS 11.0+ ARM64

ray-2.9.1-cp38-cp38-macosx_10_15_x86_64.whl (65.7 MB view details)

Uploaded CPython 3.8macOS 10.15+ x86-64

File details

Details for the file ray-2.9.1-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: ray-2.9.1-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 25.6 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.5

File hashes

Hashes for ray-2.9.1-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 bb0c83c0f40a5ab4139f9357d3fd4ef8a2e8b46f5c023fe45f305fe2297c520c
MD5 c2f92a1c70208f6558d132a5707d0b12
BLAKE2b-256 a2772cd1967982ef518065f181f5273e2bdf84cdcda586941b307c7f17aaad16

See more details on using hashes here.

File details

Details for the file ray-2.9.1-cp311-cp311-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for ray-2.9.1-cp311-cp311-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 fabc520990c1b98dde592813d62737e5e817460e0ac359f32ba029ace292cbe2
MD5 b81c686fdb64bcf06c33368bb3cc925f
BLAKE2b-256 69b310ba2aec86f3425ca1c4a4533ee5678319c63f0f2bde5ab1a8b5d64048b4

See more details on using hashes here.

File details

Details for the file ray-2.9.1-cp311-cp311-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for ray-2.9.1-cp311-cp311-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 1907649d69efdc1b9ffbc03db086f6d768216cb73908ebd4038ac5030effef9e
MD5 193ddff545076c8a7b6283e59e286cfa
BLAKE2b-256 198d285948593414f1db7622cbe6f0329bdb640ac55d53bbc0d64ed945fd0053

See more details on using hashes here.

File details

Details for the file ray-2.9.1-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

  • Download URL: ray-2.9.1-cp311-cp311-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 63.6 MB
  • Tags: CPython 3.11, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.5

File hashes

Hashes for ray-2.9.1-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 9e9d99496effa490f94e43c10a09964146269733cd24610d3b6902b566190a9b
MD5 897bf6a424fac86eca373c2bc4c206c7
BLAKE2b-256 0649632b930a0802edb7bb6ffc3612dc844352825e26f97b7526b1a81f9ae7ac

See more details on using hashes here.

File details

Details for the file ray-2.9.1-cp311-cp311-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for ray-2.9.1-cp311-cp311-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 cd974b141088b752d1eed4d6d0cf94e8ed63b97d5f1d5f5844970f3f373dde87
MD5 1224bcb03be87c2626af386c1720dd0f
BLAKE2b-256 7f41bb546796a81bff60443f227d0135612a4068153bcbb9c1559358a069b100

See more details on using hashes here.

File details

Details for the file ray-2.9.1-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: ray-2.9.1-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 25.2 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.5

File hashes

Hashes for ray-2.9.1-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 8760d406d782cbf6684c2b98c09bd4893a14c009c2287cbe65aa11cb6e7a571f
MD5 7c85f5843476325934896a5bdca4dd3c
BLAKE2b-256 e2201d144032a58cc7c37bc218ff4bfb9910bca68b70bd83edbe548c6584c25f

See more details on using hashes here.

File details

Details for the file ray-2.9.1-cp310-cp310-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for ray-2.9.1-cp310-cp310-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 50436361012cefdd90ebb8c920711cb334cf64d7a5677c9b72e60d8c9e23ee70
MD5 8c076be8bf2e0353bd567fe47028de99
BLAKE2b-256 75b8323aa4967f6d63316d7bf9f54b16d3221e2bc710f8c708da1a881aaa92c9

See more details on using hashes here.

File details

Details for the file ray-2.9.1-cp310-cp310-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for ray-2.9.1-cp310-cp310-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 74a1d12117e87ffd7411fadb96b40bf66ca7d32fdb2049cd3dd66705a0923f9e
MD5 e2f4e41ec8b3a317014c211ec33c7585
BLAKE2b-256 aa9edd3966b5b1c330fe753ec543158cc7441bccc990858a42957fa53dcb0cbd

See more details on using hashes here.

File details

Details for the file ray-2.9.1-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

  • Download URL: ray-2.9.1-cp310-cp310-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 63.2 MB
  • Tags: CPython 3.10, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.5

File hashes

Hashes for ray-2.9.1-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 eb3dbb0639fedf2bc2b98784bb94dbdc2c2a470c91c6b54e12c51d0a0069aebf
MD5 e78a90bf2f1b4e9941377a05402473a9
BLAKE2b-256 7914a4dfac5f3ea8860b1d18abd00fe7755646ca40e3cb96ceb52c76b182827a

See more details on using hashes here.

File details

Details for the file ray-2.9.1-cp310-cp310-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for ray-2.9.1-cp310-cp310-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 586d462e555ba51840fbfce4d62b0ed886930e520517b34a88befeb4fb4c244a
MD5 25a64731025fa5f995a06b35d990ad83
BLAKE2b-256 5537c2dd4181807a7ac7a7d6004b5c827d15993488b5929ff0d6ad7e7de9b27e

See more details on using hashes here.

File details

Details for the file ray-2.9.1-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: ray-2.9.1-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 25.2 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.5

File hashes

Hashes for ray-2.9.1-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 c2e360743ae25babfcb436250275550fd96a567c830393ff5dd7fc708875c4c9
MD5 ff49ce5911a363da0abb011eee3f3601
BLAKE2b-256 64aa67922aac61dcc6f2e7baba3f305430e5bcf24a97f33a270d1dfefcab5ae4

See more details on using hashes here.

File details

Details for the file ray-2.9.1-cp39-cp39-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for ray-2.9.1-cp39-cp39-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 334c47ca24dbe59e295e2d46152c09ff113f2c2cde873181da11c24dfdacfcfb
MD5 4b38705c5df17ccce530e2ef90b59598
BLAKE2b-256 dc72b4a437ffa2363bf4573094cd15091a8b32b1b7abc9a9fe8a53acbebaba64

See more details on using hashes here.

File details

Details for the file ray-2.9.1-cp39-cp39-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for ray-2.9.1-cp39-cp39-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 f063f0140bc1ea0b02f8ee59abd8e964866c1ca6c768a2b0fd19b691cf9feace
MD5 c364308b635cec7231f5d4e2d05d9f75
BLAKE2b-256 3e6dd7c56be4e925e7e76aea4f012c8459997fdde3db12b49f7671ea37a9d21d

See more details on using hashes here.

File details

Details for the file ray-2.9.1-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

  • Download URL: ray-2.9.1-cp39-cp39-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 63.2 MB
  • Tags: CPython 3.9, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.5

File hashes

Hashes for ray-2.9.1-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 4aa6a66fb20a35ded74674ad8d48e813afd4e65a0bc8ccd99e981bccf656ce13
MD5 9bae52c8cbf23159f6fab2e239f07c84
BLAKE2b-256 b108bedaf0a5d1eb6d4db037fd5d1426c70921dcd402c9583ddd49a482cf5160

See more details on using hashes here.

File details

Details for the file ray-2.9.1-cp39-cp39-macosx_10_15_x86_64.whl.

File metadata

  • Download URL: ray-2.9.1-cp39-cp39-macosx_10_15_x86_64.whl
  • Upload date:
  • Size: 65.7 MB
  • Tags: CPython 3.9, macOS 10.15+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.5

File hashes

Hashes for ray-2.9.1-cp39-cp39-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 9efc8a2035521c5d66b625222a7b03c7759f1c0969d382697fd688577bea21a4
MD5 04edd7090eb1ad996c824c8a7f25fe21
BLAKE2b-256 3856c7e07722b5eac2f73862562e07fbe5fadd39559b345323d07f0e65e7ae1f

See more details on using hashes here.

File details

Details for the file ray-2.9.1-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: ray-2.9.1-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 25.3 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.5

File hashes

Hashes for ray-2.9.1-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 94961e948763a101d99f9e9cfe8ba1d789f5ca030ebc8089fbf02da1d085f870
MD5 477f302eaf50b05f9d0e41c769ae4e40
BLAKE2b-256 f9bef346d4d97af2a5b8a0c6727d8a103d41a44efd00fb80de154517f2a6d9b8

See more details on using hashes here.

File details

Details for the file ray-2.9.1-cp38-cp38-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for ray-2.9.1-cp38-cp38-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 917efa43b88d5f5de19a5ffa7c4aa0aa28399a0c33595d83c26d5b9f79dfb861
MD5 7a5adf9396afb1008cc0a5d60c0232f9
BLAKE2b-256 860d91894812bc1df4241d1819e0a1a29f4b80697137780f532216908e42464f

See more details on using hashes here.

File details

Details for the file ray-2.9.1-cp38-cp38-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for ray-2.9.1-cp38-cp38-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 177a5a018d9ff0eef822b279f7af62ca5f5935e4d83246105868017ee298faae
MD5 799ad5c42397f10bf639863457fb317b
BLAKE2b-256 3f01feef6bd79c9e0c0ec25e6d0420246c0f47d8cc3376e9a3345f4760e21467

See more details on using hashes here.

File details

Details for the file ray-2.9.1-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

  • Download URL: ray-2.9.1-cp38-cp38-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 63.2 MB
  • Tags: CPython 3.8, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.5

File hashes

Hashes for ray-2.9.1-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 38b7a3282783f74cfd232b0e04bfde40e51e13bf3f83423ce97b2ae577a4a345
MD5 e35b191f915956b851b3ff143e8d7fec
BLAKE2b-256 55f87cebc17f8a0d011674a35879a59535bbcbdaa909e9c15a490c7436ee8131

See more details on using hashes here.

File details

Details for the file ray-2.9.1-cp38-cp38-macosx_10_15_x86_64.whl.

File metadata

  • Download URL: ray-2.9.1-cp38-cp38-macosx_10_15_x86_64.whl
  • Upload date:
  • Size: 65.7 MB
  • Tags: CPython 3.8, macOS 10.15+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.5

File hashes

Hashes for ray-2.9.1-cp38-cp38-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 e7b1f3284b35aa98968ba8cdc8ea43f6a0afe42090711f2db678d3f73c5cb8f9
MD5 b301061f3898bc08419e5c41a00530ef
BLAKE2b-256 1af42816085191d668f0f9cd19f1f89d26b2f9f35f29b0bf5df2023bc60c9b34

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page