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 >=3.10
  • Provides-Extra: cgraph , client , data , default , observability , serve , tune , adag , serve-grpc , serve-async-inference , cpp , rllib , train , air , all , all-cpp , llm
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 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

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.

Learn more about Monitoring and Debugging:

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 >=3.10
  • Provides-Extra: cgraph , client , data , default , observability , serve , tune , adag , serve-grpc , serve-async-inference , cpp , rllib , train , air , all , all-cpp , llm
Classifiers

Release history Release notifications | RSS feed

2.55.1

This version

2.55.0

2.54.1

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

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-2.55.0-cp314-cp314-manylinux2014_x86_64.whl (73.6 MB view details)

Uploaded CPython 3.14

ray-2.55.0-cp314-cp314-manylinux2014_aarch64.whl (72.8 MB view details)

Uploaded CPython 3.14

ray-2.55.0-cp314-cp314-macosx_12_0_arm64.whl (65.8 MB view details)

Uploaded CPython 3.14macOS 12.0+ ARM64

ray-2.55.0-cp313-cp313-manylinux2014_x86_64.whl (73.7 MB view details)

Uploaded CPython 3.13

ray-2.55.0-cp313-cp313-manylinux2014_aarch64.whl (72.8 MB view details)

Uploaded CPython 3.13

ray-2.55.0-cp313-cp313-macosx_12_0_arm64.whl (65.8 MB view details)

Uploaded CPython 3.13macOS 12.0+ ARM64

ray-2.55.0-cp312-cp312-win_amd64.whl (27.9 MB view details)

Uploaded CPython 3.12Windows x86-64

ray-2.55.0-cp312-cp312-manylinux2014_x86_64.whl (73.8 MB view details)

Uploaded CPython 3.12

ray-2.55.0-cp312-cp312-manylinux2014_aarch64.whl (72.9 MB view details)

Uploaded CPython 3.12

ray-2.55.0-cp312-cp312-macosx_12_0_arm64.whl (65.8 MB view details)

Uploaded CPython 3.12macOS 12.0+ ARM64

ray-2.55.0-cp311-cp311-win_amd64.whl (27.9 MB view details)

Uploaded CPython 3.11Windows x86-64

ray-2.55.0-cp311-cp311-manylinux2014_x86_64.whl (73.7 MB view details)

Uploaded CPython 3.11

ray-2.55.0-cp311-cp311-manylinux2014_aarch64.whl (72.9 MB view details)

Uploaded CPython 3.11

ray-2.55.0-cp311-cp311-macosx_12_0_arm64.whl (65.8 MB view details)

Uploaded CPython 3.11macOS 12.0+ ARM64

ray-2.55.0-cp310-cp310-win_amd64.whl (27.9 MB view details)

Uploaded CPython 3.10Windows x86-64

ray-2.55.0-cp310-cp310-manylinux2014_x86_64.whl (73.6 MB view details)

Uploaded CPython 3.10

ray-2.55.0-cp310-cp310-manylinux2014_aarch64.whl (72.8 MB view details)

Uploaded CPython 3.10

ray-2.55.0-cp310-cp310-macosx_12_0_arm64.whl (65.8 MB view details)

Uploaded CPython 3.10macOS 12.0+ ARM64

File details

Details for the file ray-2.55.0-cp314-cp314-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for ray-2.55.0-cp314-cp314-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 eb0a6179641bc420a66ee85cc9b382e58f22effbd36297e3683a793e5cdc0898
MD5 23c2a91e03a1d17887cda3ca0c8e1cfe
BLAKE2b-256 a8daf701527fe5e3b84afbdffe206b39d2929013301ef15660b99794debdf2d3

See more details on using hashes here.

File details

Details for the file ray-2.55.0-cp314-cp314-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for ray-2.55.0-cp314-cp314-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 b74390f201f28f05c8f250069dfed54d6d6a0109ffe482425d76c11be820e309
MD5 e33bb3f114ad5a8d64c2423024a3f4a9
BLAKE2b-256 b8720283b4d2289567511918de44a890d3dc3f5da529112481b75402bb67550d

See more details on using hashes here.

File details

Details for the file ray-2.55.0-cp314-cp314-macosx_12_0_arm64.whl.

File metadata

File hashes

Hashes for ray-2.55.0-cp314-cp314-macosx_12_0_arm64.whl
Algorithm Hash digest
SHA256 d48bc4533b3b76d59ed3f9eab1e6b7322a53a7cdefb8f657d9b46eebad56dbee
MD5 1450d7ae96ad21b9a1d3af1d2e025eb9
BLAKE2b-256 9b902b71910c00372634c73a15d5252a09999b1a806301e9759c90dc71479247

See more details on using hashes here.

File details

Details for the file ray-2.55.0-cp313-cp313-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for ray-2.55.0-cp313-cp313-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 f16dea32e5cc58ed406c0ef0dd4be69d60ce77a075edb5f0380356a48bf85ab3
MD5 a6f34e05062291edd838647c4e8c52dd
BLAKE2b-256 726c97bd20bc62e5dc1b40784261e38f5fce9aee9765c51332a41e95083507a1

See more details on using hashes here.

File details

Details for the file ray-2.55.0-cp313-cp313-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for ray-2.55.0-cp313-cp313-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 ceee87a884602aab34db109415e6839a6e9169f4750cab727b7ea1610df5b91f
MD5 b247d85d2601c556e891948c40878cc1
BLAKE2b-256 00e809ebc53f76800130da0a38bb28e14924ec28daa0a9f41b75146056b52f7c

See more details on using hashes here.

File details

Details for the file ray-2.55.0-cp313-cp313-macosx_12_0_arm64.whl.

File metadata

File hashes

Hashes for ray-2.55.0-cp313-cp313-macosx_12_0_arm64.whl
Algorithm Hash digest
SHA256 1da8b8755b6e4fde03db78b6ce2bbcecfcfbd20d39b93833d246c515daeedf3c
MD5 55f59b66a9e66a0c4174aa8520a4eb47
BLAKE2b-256 a547c57556d981a7ed0fe1438f9ff1ecd601bf7d5c704e7698bf181536470acc

See more details on using hashes here.

File details

Details for the file ray-2.55.0-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: ray-2.55.0-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 27.9 MB
  • Tags: CPython 3.12, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.4

File hashes

Hashes for ray-2.55.0-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 5da06d27358d38c30a723a617bf9b7df138f4d90e8046f1fa51d9b8c7473b64a
MD5 e713a13326b9ae8d162889ab25116191
BLAKE2b-256 eaac40ee9b4a514366a1dc8ed50a16a4fc095192c89f2eccb30b15ab710addf3

See more details on using hashes here.

File details

Details for the file ray-2.55.0-cp312-cp312-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for ray-2.55.0-cp312-cp312-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 cd460bdbf8a8a4bb768a20c38b1c534d84fe63bc0e5f3580c5c0ef7302b986b3
MD5 df6c22b7f436154910308009b1db54a1
BLAKE2b-256 7ea52a17fd0aed4f053462eeabfeb6b3ff1b46a85ac9a7da1ebf99d60683f3c2

See more details on using hashes here.

File details

Details for the file ray-2.55.0-cp312-cp312-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for ray-2.55.0-cp312-cp312-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 b77f406072ac0ce90431ac436828f364c183ab57ba15c3a0e688a74ae3c2d3f3
MD5 6c1e9e9291c618d8e1549644374c0be5
BLAKE2b-256 4a6a0c1a1179832b9dd93c615289ab92eefd5d844f6e6cea313db09bd55b84da

See more details on using hashes here.

File details

Details for the file ray-2.55.0-cp312-cp312-macosx_12_0_arm64.whl.

File metadata

File hashes

Hashes for ray-2.55.0-cp312-cp312-macosx_12_0_arm64.whl
Algorithm Hash digest
SHA256 6f0b8dfa3716cc9be5fce3b53e9bfdb73cea36025bfe6f1d27928d0f84cfd695
MD5 a3a0770997df21674bbf9067faad29ad
BLAKE2b-256 f9d4586e6a696004262b74a7a53b3860c3178b9940ed3c5ee5d909cb7f8f3149

See more details on using hashes here.

File details

Details for the file ray-2.55.0-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: ray-2.55.0-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 27.9 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.4

File hashes

Hashes for ray-2.55.0-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 66814d57c533f58ec378a55591b2968150fd4c057f0b2b87460438e7353fa062
MD5 471df7aac1743b7ba855a11dd9f144c5
BLAKE2b-256 769a3e4fc1591e450a151e9f3dad37777ea8bc0d01bf836a6a8e8e683a441a7c

See more details on using hashes here.

File details

Details for the file ray-2.55.0-cp311-cp311-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for ray-2.55.0-cp311-cp311-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 52249146cfdcf769ecffe54f5b2caf25551bf6ea78d9dd89788c0a669ca769b7
MD5 5c05e05acfe437339fc2be0ba24b6df5
BLAKE2b-256 5fa9d69e6bf24393efa7c3cab34e945dc6b3bba49c4f80d6df23ae9f49289f06

See more details on using hashes here.

File details

Details for the file ray-2.55.0-cp311-cp311-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for ray-2.55.0-cp311-cp311-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 b1fd73427093d1aef1fb7bd3c5430a4e686d5e10fa04089757163a4c2a517deb
MD5 5f606ae516445b6399866cd57a932386
BLAKE2b-256 3dea846705b5a44fc49d65c39bf20cb3d1900b168134b25cc7e9f2d2f9e78d42

See more details on using hashes here.

File details

Details for the file ray-2.55.0-cp311-cp311-macosx_12_0_arm64.whl.

File metadata

File hashes

Hashes for ray-2.55.0-cp311-cp311-macosx_12_0_arm64.whl
Algorithm Hash digest
SHA256 2426e9c4513cb4842bfaf70aaedf4b9edc302fb01de5c1906f68b9b427a0c243
MD5 090cf7f0f7babe55497cff93be3a58a7
BLAKE2b-256 b127a4f419818c5eea064b3b098a80225430cb596385f866cde2bfb1c4a0d798

See more details on using hashes here.

File details

Details for the file ray-2.55.0-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: ray-2.55.0-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 27.9 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.4

File hashes

Hashes for ray-2.55.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 c85109c255d3d3e71bda431332beb716ed445041e06fd2c7fa6c2232f3cb9d75
MD5 3bb8e0e2e38d171077a1e33bf58409ab
BLAKE2b-256 af71bbabc53548ada957354a5be1fb0ebdf48e7e4564228f64554504517f0a3b

See more details on using hashes here.

File details

Details for the file ray-2.55.0-cp310-cp310-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for ray-2.55.0-cp310-cp310-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 2cf8c2a3f6cd947934cd75d99cf4b5615228fe6c0b42faa7a6926733a82c2467
MD5 124dd89473dcb28c01722cd1a3cbdbd6
BLAKE2b-256 73bf78bbb5c47bea59c7df33ea5e0124af91ef3607e68ea7c6f20786bbbad7b0

See more details on using hashes here.

File details

Details for the file ray-2.55.0-cp310-cp310-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for ray-2.55.0-cp310-cp310-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 2c19db3faf5b662175aa80c4b117f223fa198996161b6c5a421401227c456b55
MD5 7e6313f98f1ace66cd0bc0f3e137e875
BLAKE2b-256 ec875fb99505e1c3019132b3f824a7838d1b2f89a1d0a538e9fec18084148e9f

See more details on using hashes here.

File details

Details for the file ray-2.55.0-cp310-cp310-macosx_12_0_arm64.whl.

File metadata

File hashes

Hashes for ray-2.55.0-cp310-cp310-macosx_12_0_arm64.whl
Algorithm Hash digest
SHA256 c1bc110d39edfa8f8ed85fbab0086dc7e4a8b8c0d784a74ff39eac5e73580d62
MD5 753528bf64660efaf2c5f6c58b319ec6
BLAKE2b-256 aebb9f8a45a866b454fd9d8eb6070a916b38cee1bd1a19606b5a952f63d75940

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