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
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 toolkit of libraries (Ray AIR) for simplifying ML compute:

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

Learn more about Ray AIR and its libraries:

  • Datasets: Distributed Data Preprocessing

  • 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.

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
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

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

This version

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-2.0.0-cp310-cp310-manylinux2014_x86_64.whl (59.1 MB view details)

Uploaded CPython 3.10

ray-2.0.0-cp310-cp310-macosx_11_0_arm64.whl (26.6 MB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

ray-2.0.0-cp310-cp310-macosx_10_15_universal2.whl (73.6 MB view details)

Uploaded CPython 3.10macOS 10.15+ universal2 (ARM64, x86-64)

ray-2.0.0-cp39-cp39-win_amd64.whl (20.7 MB view details)

Uploaded CPython 3.9Windows x86-64

ray-2.0.0-cp39-cp39-manylinux2014_x86_64.whl (59.1 MB view details)

Uploaded CPython 3.9

ray-2.0.0-cp39-cp39-macosx_11_0_arm64.whl (26.6 MB view details)

Uploaded CPython 3.9macOS 11.0+ ARM64

ray-2.0.0-cp39-cp39-macosx_10_15_x86_64.whl (73.6 MB view details)

Uploaded CPython 3.9macOS 10.15+ x86-64

ray-2.0.0-cp38-cp38-win_amd64.whl (20.7 MB view details)

Uploaded CPython 3.8Windows x86-64

ray-2.0.0-cp38-cp38-manylinux2014_x86_64.whl (59.2 MB view details)

Uploaded CPython 3.8

ray-2.0.0-cp38-cp38-macosx_11_0_arm64.whl (26.6 MB view details)

Uploaded CPython 3.8macOS 11.0+ ARM64

ray-2.0.0-cp38-cp38-macosx_10_15_x86_64.whl (73.6 MB view details)

Uploaded CPython 3.8macOS 10.15+ x86-64

ray-2.0.0-cp37-cp37m-win_amd64.whl (20.8 MB view details)

Uploaded CPython 3.7mWindows x86-64

ray-2.0.0-cp37-cp37m-manylinux2014_x86_64.whl (59.4 MB view details)

Uploaded CPython 3.7m

ray-2.0.0-cp37-cp37m-macosx_10_15_intel.whl (73.7 MB view details)

Uploaded CPython 3.7mmacOS 10.15+ Intel (x86-64, i386)

ray-2.0.0-cp36-cp36m-manylinux2014_x86_64.whl (59.4 MB view details)

Uploaded CPython 3.6m

ray-2.0.0-cp36-cp36m-macosx_10_15_intel.whl (73.7 MB view details)

Uploaded CPython 3.6mmacOS 10.15+ Intel (x86-64, i386)

File details

Details for the file ray-2.0.0-cp310-cp310-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for ray-2.0.0-cp310-cp310-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 bcf3bff9517d77ea6c98592fa16e1cfb8bc0cfa345d3be69729bfa9c5bd78a7c
MD5 35d913f44fdd27dd938ac0c04a760ace
BLAKE2b-256 986a5a0e5d5a5df337930b1af71abd096afd62af33191c905112fcadbb81bedb

See more details on using hashes here.

File details

Details for the file ray-2.0.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

  • Download URL: ray-2.0.0-cp310-cp310-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 26.6 MB
  • Tags: CPython 3.10, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.7.13

File hashes

Hashes for ray-2.0.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 7ac6114fcefa31698d0c1996aa48e1d3bc9e07c82ad1b587a167466b0ddc6cf7
MD5 f1f2986cc0fa3e0c3add8784be84f995
BLAKE2b-256 3be78d22f742dc0cdd22b51e6929e5bd9d284828c8b1cfec30c21664afb28dd5

See more details on using hashes here.

File details

Details for the file ray-2.0.0-cp310-cp310-macosx_10_15_universal2.whl.

File metadata

File hashes

Hashes for ray-2.0.0-cp310-cp310-macosx_10_15_universal2.whl
Algorithm Hash digest
SHA256 1f37a0587d9ded406bca58c259e12619fadd2bba8aaf2abd0332c2664daa09ee
MD5 c2e7331dfec38a64b177670b3cd389a5
BLAKE2b-256 9de6e5f85993bf0ffed009615ef2978d95adb0ea7b582d56a3d2681e4b4383e5

See more details on using hashes here.

File details

Details for the file ray-2.0.0-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: ray-2.0.0-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 20.7 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.7.13

File hashes

Hashes for ray-2.0.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 2c0153863a4b5e2c4d3964a9cae5a8f25874a66462e209ff4ef92b5ebddea09b
MD5 cb26c53519f26987a087126b5f4268a9
BLAKE2b-256 5c9ca359cdb3edfb093f89597fd06d2dff537e2c15ce5e64685536840827c26d

See more details on using hashes here.

File details

Details for the file ray-2.0.0-cp39-cp39-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for ray-2.0.0-cp39-cp39-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 cab13346650f88171b3f348ed352f04695b96d1ab1090ed3b80bdc93e897dbd4
MD5 955732f2be6ced91afe1105295a8cc75
BLAKE2b-256 99194cb291ee01b327274fe063827c2ca33754183abd7c456ee031e2919f5cdb

See more details on using hashes here.

File details

Details for the file ray-2.0.0-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

  • Download URL: ray-2.0.0-cp39-cp39-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 26.6 MB
  • Tags: CPython 3.9, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.7.13

File hashes

Hashes for ray-2.0.0-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 eb9d2dc11ca4503f600cd66a1fca986dd5c262a3baedd5f2d1d75d55aced2a5a
MD5 b1170050d97564b50a4c440799e9f722
BLAKE2b-256 25e2a8ffedf83690591c482c4751cb59bfc312b04ceb562ca53b471375cb35c0

See more details on using hashes here.

File details

Details for the file ray-2.0.0-cp39-cp39-macosx_10_15_x86_64.whl.

File metadata

  • Download URL: ray-2.0.0-cp39-cp39-macosx_10_15_x86_64.whl
  • Upload date:
  • Size: 73.6 MB
  • Tags: CPython 3.9, macOS 10.15+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.7.13

File hashes

Hashes for ray-2.0.0-cp39-cp39-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 91f82a5c24757c409b3132b240f89c51006ae4ae20e3d32e3e519c46d8440a1c
MD5 11e995e80ea1a746a13a7779fc709750
BLAKE2b-256 1b774ef2ceb7250830fe813fb235eea132c01fa85579f31ba0ad924f73d8b3f5

See more details on using hashes here.

File details

Details for the file ray-2.0.0-cp38-cp38-win_amd64.whl.

File metadata

  • Download URL: ray-2.0.0-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 20.7 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.7.13

File hashes

Hashes for ray-2.0.0-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 2e224855683bf0d3de26e1b44b392fa871d5204d43207c83ac6c9b79385fa8d3
MD5 d71481cdf36f71461c2068e34dca0864
BLAKE2b-256 b62e27c998bfd55cc1bd0c2b5f6a8685741131de018451210de78db688e8ee29

See more details on using hashes here.

File details

Details for the file ray-2.0.0-cp38-cp38-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for ray-2.0.0-cp38-cp38-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 da8adfa33ff54bc61cfe80334a0ee889e0060918db6ff9215aebe32e98b1f939
MD5 817816c76a51913d91b77a07262abdda
BLAKE2b-256 3cbcc5874dcddd7c3f18a3100ed3a0810b4cbb34c24e474636c3e4eadff3c1a8

See more details on using hashes here.

File details

Details for the file ray-2.0.0-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

  • Download URL: ray-2.0.0-cp38-cp38-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 26.6 MB
  • Tags: CPython 3.8, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.7.13

File hashes

Hashes for ray-2.0.0-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 efc3197e1e101f1cd3f7176f1593aa54a2ce404b417a01ff4725c004d975950f
MD5 d419b3a72db3d745bebc8c7d5e2ba407
BLAKE2b-256 59f75ea95ed86c0d26a6cbf878056414da0560ae8b51481b4b31168af89f3ecb

See more details on using hashes here.

File details

Details for the file ray-2.0.0-cp38-cp38-macosx_10_15_x86_64.whl.

File metadata

  • Download URL: ray-2.0.0-cp38-cp38-macosx_10_15_x86_64.whl
  • Upload date:
  • Size: 73.6 MB
  • Tags: CPython 3.8, macOS 10.15+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.7.13

File hashes

Hashes for ray-2.0.0-cp38-cp38-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 dcc614ee0abbfb2031d06e6d6c0a556be8285b43caf8ced64c0b7faf8bac1955
MD5 c989ad3e3135cdbbc5ffc491e2f6d2ad
BLAKE2b-256 67a7113cdcfffc208629d7ff1ff3b40e78f374763b91bfa6079f4f18c2128571

See more details on using hashes here.

File details

Details for the file ray-2.0.0-cp37-cp37m-win_amd64.whl.

File metadata

  • Download URL: ray-2.0.0-cp37-cp37m-win_amd64.whl
  • Upload date:
  • Size: 20.8 MB
  • Tags: CPython 3.7m, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.7.13

File hashes

Hashes for ray-2.0.0-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 d65b38e20db79bd6d5d8e018ced320614ffb645c86c3999d3ddcdea9e638e478
MD5 2ef1f4b6709443006954d5938f638eca
BLAKE2b-256 d366d470795dbcbd5954f02f086c51bba490b8636065243ce0c53164ef82bdd5

See more details on using hashes here.

File details

Details for the file ray-2.0.0-cp37-cp37m-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for ray-2.0.0-cp37-cp37m-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 6d05daf9653a0733a5501cad347f4be652cea06d9debed7a00e741b7d387e2df
MD5 249f767a34596bac599f0f8a2c0b42e2
BLAKE2b-256 b40fc2e6dbcc73f0e84dd75bdacb49208d577a5b720ca3eb692fef4e573a5fd3

See more details on using hashes here.

File details

Details for the file ray-2.0.0-cp37-cp37m-macosx_10_15_intel.whl.

File metadata

  • Download URL: ray-2.0.0-cp37-cp37m-macosx_10_15_intel.whl
  • Upload date:
  • Size: 73.7 MB
  • Tags: CPython 3.7m, macOS 10.15+ Intel (x86-64, i386)
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.7.13

File hashes

Hashes for ray-2.0.0-cp37-cp37m-macosx_10_15_intel.whl
Algorithm Hash digest
SHA256 de1616f5d47f70304e9df2f40f1aedc3a57b4742fd65dcec8a0824dd9d63ba50
MD5 fa2dfb0a688fd9d3f3f3405219503095
BLAKE2b-256 c7a6b04b0c6629bc9d11f24bf833c0c8fd41e9c652338c38b0bb1e8876815c9c

See more details on using hashes here.

File details

Details for the file ray-2.0.0-cp36-cp36m-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for ray-2.0.0-cp36-cp36m-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 8681c217059504394ab0c295efc622210e0b763e48f48541a9589f0657915ce3
MD5 c8d98280ff22190bc913f32338b6c03e
BLAKE2b-256 d58a8de50007adadf482ce1e494a16983c02e0ec34841a232e573dde63caab97

See more details on using hashes here.

File details

Details for the file ray-2.0.0-cp36-cp36m-macosx_10_15_intel.whl.

File metadata

  • Download URL: ray-2.0.0-cp36-cp36m-macosx_10_15_intel.whl
  • Upload date:
  • Size: 73.7 MB
  • Tags: CPython 3.6m, macOS 10.15+ Intel (x86-64, i386)
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.7.13

File hashes

Hashes for ray-2.0.0-cp36-cp36m-macosx_10_15_intel.whl
Algorithm Hash digest
SHA256 1c3417b58fec434a52e8f0ca0cd24308b462624fce0155cc0c17941a7a8292bb
MD5 4503e211b9d13c412401c4445cef0bf0
BLAKE2b-256 cae9745259c1e730978876099c4d6a2545d3ee57add73f313226352e1328d39e

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