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

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for amogkamsetty from gravatar.com amogkamsetty Avatar for architkulkarni from gravatar.com architkulkarni Avatar for artur1993 from gravatar.com artur1993 Avatar for aslonnie from gravatar.com aslonnie Avatar for bveeramani from gravatar.com bveeramani Avatar for cdnamz from gravatar.com cdnamz Avatar for ekl from gravatar.com ekl Avatar for eoakes from gravatar.com eoakes Avatar for gene_anyscale from gravatar.com gene_anyscale Avatar for jjyao from gravatar.com jjyao Avatar for kf from gravatar.com kf Avatar for khluu from gravatar.com khluu Avatar for matthewdeng from gravatar.com matthewdeng Avatar for pcmoritz from gravatar.com pcmoritz Avatar for richardliaw from gravatar.com richardliaw Avatar for rickyx from gravatar.com rickyx Avatar for rkn from gravatar.com rkn Avatar for rynewang from gravatar.com rynewang Avatar for scho010 from gravatar.com scho010 Avatar for scv119 from gravatar.com scv119 Avatar for swang from gravatar.com swang Avatar for vitsai from gravatar.com vitsai Avatar for wuisawesome from gravatar.com wuisawesome Avatar for xwjiang from gravatar.com xwjiang Avatar for yic from gravatar.com yic Avatar for zcin from gravatar.com zcin

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 >=3.9
  • Provides-Extra: adag , air , all , all-cpp , cgraph , client , cpp , data , default , llm , observability , rllib , serve , serve-grpc , train , tune
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 amogkamsetty from gravatar.com amogkamsetty Avatar for architkulkarni from gravatar.com architkulkarni Avatar for artur1993 from gravatar.com artur1993 Avatar for aslonnie from gravatar.com aslonnie Avatar for bveeramani from gravatar.com bveeramani Avatar for cdnamz from gravatar.com cdnamz Avatar for ekl from gravatar.com ekl Avatar for eoakes from gravatar.com eoakes Avatar for gene_anyscale from gravatar.com gene_anyscale Avatar for jjyao from gravatar.com jjyao Avatar for kf from gravatar.com kf Avatar for khluu from gravatar.com khluu Avatar for matthewdeng from gravatar.com matthewdeng Avatar for pcmoritz from gravatar.com pcmoritz Avatar for richardliaw from gravatar.com richardliaw Avatar for rickyx from gravatar.com rickyx Avatar for rkn from gravatar.com rkn Avatar for rynewang from gravatar.com rynewang Avatar for scho010 from gravatar.com scho010 Avatar for scv119 from gravatar.com scv119 Avatar for swang from gravatar.com swang Avatar for vitsai from gravatar.com vitsai Avatar for wuisawesome from gravatar.com wuisawesome Avatar for xwjiang from gravatar.com xwjiang Avatar for yic from gravatar.com yic Avatar for zcin from gravatar.com zcin

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 >=3.9
  • Provides-Extra: adag , air , all , all-cpp , cgraph , client , cpp , data , default , llm , observability , rllib , serve , serve-grpc , train , tune
Classifiers

Release history Release notifications | RSS feed

This version

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

ray-2.47.1-cp313-cp313-manylinux2014_x86_64.whl (68.8 MB view details)

Uploaded CPython 3.13

ray-2.47.1-cp313-cp313-manylinux2014_aarch64.whl (67.9 MB view details)

Uploaded CPython 3.13

ray-2.47.1-cp313-cp313-macosx_12_0_x86_64.whl (68.5 MB view details)

Uploaded CPython 3.13macOS 12.0+ x86-64

ray-2.47.1-cp313-cp313-macosx_11_0_arm64.whl (66.1 MB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

ray-2.47.1-cp312-cp312-win_amd64.whl (26.2 MB view details)

Uploaded CPython 3.12Windows x86-64

ray-2.47.1-cp312-cp312-manylinux2014_x86_64.whl (68.9 MB view details)

Uploaded CPython 3.12

ray-2.47.1-cp312-cp312-manylinux2014_aarch64.whl (67.9 MB view details)

Uploaded CPython 3.12

ray-2.47.1-cp312-cp312-macosx_12_0_x86_64.whl (68.5 MB view details)

Uploaded CPython 3.12macOS 12.0+ x86-64

ray-2.47.1-cp312-cp312-macosx_11_0_arm64.whl (66.1 MB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

ray-2.47.1-cp311-cp311-win_amd64.whl (26.2 MB view details)

Uploaded CPython 3.11Windows x86-64

ray-2.47.1-cp311-cp311-manylinux2014_x86_64.whl (68.9 MB view details)

Uploaded CPython 3.11

ray-2.47.1-cp311-cp311-manylinux2014_aarch64.whl (67.9 MB view details)

Uploaded CPython 3.11

ray-2.47.1-cp311-cp311-macosx_12_0_x86_64.whl (68.5 MB view details)

Uploaded CPython 3.11macOS 12.0+ x86-64

ray-2.47.1-cp311-cp311-macosx_11_0_arm64.whl (66.1 MB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

ray-2.47.1-cp310-cp310-win_amd64.whl (26.2 MB view details)

Uploaded CPython 3.10Windows x86-64

ray-2.47.1-cp310-cp310-manylinux2014_x86_64.whl (68.8 MB view details)

Uploaded CPython 3.10

ray-2.47.1-cp310-cp310-manylinux2014_aarch64.whl (67.8 MB view details)

Uploaded CPython 3.10

ray-2.47.1-cp310-cp310-macosx_12_0_x86_64.whl (68.6 MB view details)

Uploaded CPython 3.10macOS 12.0+ x86-64

ray-2.47.1-cp310-cp310-macosx_11_0_arm64.whl (66.1 MB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

ray-2.47.1-cp39-cp39-win_amd64.whl (26.2 MB view details)

Uploaded CPython 3.9Windows x86-64

ray-2.47.1-cp39-cp39-manylinux2014_x86_64.whl (68.8 MB view details)

Uploaded CPython 3.9

ray-2.47.1-cp39-cp39-manylinux2014_aarch64.whl (67.8 MB view details)

Uploaded CPython 3.9

ray-2.47.1-cp39-cp39-macosx_12_0_x86_64.whl (68.6 MB view details)

Uploaded CPython 3.9macOS 12.0+ x86-64

ray-2.47.1-cp39-cp39-macosx_11_0_arm64.whl (66.1 MB view details)

Uploaded CPython 3.9macOS 11.0+ ARM64

File details

Details for the file ray-2.47.1-cp313-cp313-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for ray-2.47.1-cp313-cp313-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 252a471e8afb918b105cdbffb4cbebb0143baad75a06c8ffcde27ac317579ccb
MD5 3493ad5d353736c257e5c852ecd1f7b7
BLAKE2b-256 526083430886d17526f4757e298b77268533afeedc74ed934f911d522f191ef2

See more details on using hashes here.

File details

Details for the file ray-2.47.1-cp313-cp313-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for ray-2.47.1-cp313-cp313-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 d6ed6d182e25d6f77179dc77bc97a749c81765b13cb671a46db3203029389663
MD5 d71f3b54b2b4d0b5db8a4760781348c2
BLAKE2b-256 0c97f36e8a19885d930ab0dbcb0b7b6706e630412336b08fa3312bcaaad818b7

See more details on using hashes here.

File details

Details for the file ray-2.47.1-cp313-cp313-macosx_12_0_x86_64.whl.

File metadata

File hashes

Hashes for ray-2.47.1-cp313-cp313-macosx_12_0_x86_64.whl
Algorithm Hash digest
SHA256 e578929f58b3f0c59c7544a96d864e26278238b755d13cd19ae798070c848e57
MD5 ae798efc67d9868f2f6ea459dd042e1a
BLAKE2b-256 5a8ee893176ca3c02a310bb962b287436508f5b61fd179a7283f37610c0e0087

See more details on using hashes here.

File details

Details for the file ray-2.47.1-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for ray-2.47.1-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 8cd625d469ce15391e5f1f44ddf8dd30b2380f917603fa0172661229acb0011f
MD5 ac41e1291e15c5071338036105d360b8
BLAKE2b-256 7b854dbf9a126f080a1114ec7738d9274759c256ff2a1c21662b09123311bbf0

See more details on using hashes here.

File details

Details for the file ray-2.47.1-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: ray-2.47.1-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 26.2 MB
  • Tags: CPython 3.12, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.9.12

File hashes

Hashes for ray-2.47.1-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 601f23ba89918b7b3ffebf967328f7bdb605deaf8c103aad7820dc2722fe450c
MD5 786bd6aacda78cadb5c47fe736bda137
BLAKE2b-256 94f6ed91383e0057ad9e3d9c45212a0c7edc5a9d24a2e46da0d55c8233df868c

See more details on using hashes here.

File details

Details for the file ray-2.47.1-cp312-cp312-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for ray-2.47.1-cp312-cp312-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 3eaeaeec3bbe2ca6493e530c30473d84b8580a7ac3256bb9183d8c63def5a92f
MD5 1c4ae7130acb78104c6f590dd0b30317
BLAKE2b-256 5c314545d03ed68eedf42b52e2a8705a584361e262640e145d6ab219ae33969c

See more details on using hashes here.

File details

Details for the file ray-2.47.1-cp312-cp312-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for ray-2.47.1-cp312-cp312-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 cd4e7eb475487364b5209963b17cefedcb7fbd3a816fdb6def7ea533ebd72424
MD5 3cbe32be1cd33f2ee8ae06119fb819fe
BLAKE2b-256 c5326abf17053eb0ae720a2981a17e6b22797cc655782b603a707052b47f64eb

See more details on using hashes here.

File details

Details for the file ray-2.47.1-cp312-cp312-macosx_12_0_x86_64.whl.

File metadata

File hashes

Hashes for ray-2.47.1-cp312-cp312-macosx_12_0_x86_64.whl
Algorithm Hash digest
SHA256 e6d9c78e53ac89cabbc4056aecfec53c506c692e3132af9dae941d6180ef462f
MD5 88b195ba30897314b6c928336891bf05
BLAKE2b-256 cefa23640e58046c91fcc05edd04bd51dd3d6a44cd7b408faf5bb3528a24c13d

See more details on using hashes here.

File details

Details for the file ray-2.47.1-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for ray-2.47.1-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 322049c4546cf67e5efdad90c371c5508acbb193e5aaaf4038103c6c5ce1f578
MD5 fd5ee48657054b268719435f31dfbadf
BLAKE2b-256 96d8833edaf128fb5cdd53818d307bb93df75d943f32ecc5cb0d7b14981265e6

See more details on using hashes here.

File details

Details for the file ray-2.47.1-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: ray-2.47.1-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 26.2 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.9.12

File hashes

Hashes for ray-2.47.1-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 bd1cba64070db06bbf79c0e075cdc4529193e2d0b19564f4f057b4193b29e912
MD5 ffc8ff3696f982aa417e9c9a31f8fcb9
BLAKE2b-256 deddb5dc7d3581e52683259c80014e95074835042ceaf1dea6a400185e0e1947

See more details on using hashes here.

File details

Details for the file ray-2.47.1-cp311-cp311-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for ray-2.47.1-cp311-cp311-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 48961229614b2b56a535be510c8abc76e99a9aa7fa195b5c949bd0c6c69af40a
MD5 5d99054675fc7c25f7d1959a0ad55084
BLAKE2b-256 5f2bd1395192c748b3761a43f2dbd9fa702a56f8e185fc2beee73ba25e801a46

See more details on using hashes here.

File details

Details for the file ray-2.47.1-cp311-cp311-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for ray-2.47.1-cp311-cp311-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 db5ff652e9035f03c65e1742a706b76519f6e8a6744cc005396053ac8766fc46
MD5 09482c9de7eb4ddf8ce2dd94169951f1
BLAKE2b-256 cd2da3fe20b0830ecbe74dac1ae809c265023f713e19a9f6100870d50885f44d

See more details on using hashes here.

File details

Details for the file ray-2.47.1-cp311-cp311-macosx_12_0_x86_64.whl.

File metadata

File hashes

Hashes for ray-2.47.1-cp311-cp311-macosx_12_0_x86_64.whl
Algorithm Hash digest
SHA256 feeba1e715cfd8737d3adcd2018d0cdabb7c6084fa4b093e638e6c7d42f3c956
MD5 eb8c7fc5f4d083092d5f21fb5ff12a59
BLAKE2b-256 5d1005b70d425c46eba22bdd46a77cf7db09328eb9dcbf5952fa32e42c5c28e5

See more details on using hashes here.

File details

Details for the file ray-2.47.1-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for ray-2.47.1-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 a640d447e0e6cf63f85b9220c883ec02bb2b8e40a9c1d84efa012795c769ba68
MD5 ce53b4c0b257a558bb5d8c93e424f780
BLAKE2b-256 828cf763f633a4c80d9ead6c1e9277983c42286a3a83dedccedb15363f3d4c40

See more details on using hashes here.

File details

Details for the file ray-2.47.1-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: ray-2.47.1-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 26.2 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.9.12

File hashes

Hashes for ray-2.47.1-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 44900a1a72cb3bfb331db160a8975737c25945a97f376c70e72ccf35adf3b744
MD5 85a0ba90f889d7219a4ced34a7ffbdec
BLAKE2b-256 b66c80690615b50e5e6f5309d5f0f9fcaaf83170e5240252c0d8bbeedc8dc9bd

See more details on using hashes here.

File details

Details for the file ray-2.47.1-cp310-cp310-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for ray-2.47.1-cp310-cp310-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 84a96b4720175a0000521a48eb7aa915f3b419bb5cd6172d8dee005c3f23b813
MD5 37bb75560eb1a2445c02c4f561e5434c
BLAKE2b-256 6d989289f360deb9b3d32cc9170168dff919c2f5192bf87682d5b72050206dca

See more details on using hashes here.

File details

Details for the file ray-2.47.1-cp310-cp310-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for ray-2.47.1-cp310-cp310-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 6fc7df8657b8df684b77c2d1b643137ad745aa1c12ade34743f06cca79003df0
MD5 5515bc316bc114b845fbacfd26c8d448
BLAKE2b-256 2fa6bad64e886ef74bbcab7d36b617e41c378088fc4852557005c1e227669697

See more details on using hashes here.

File details

Details for the file ray-2.47.1-cp310-cp310-macosx_12_0_x86_64.whl.

File metadata

File hashes

Hashes for ray-2.47.1-cp310-cp310-macosx_12_0_x86_64.whl
Algorithm Hash digest
SHA256 7c03a1e366d3a868a55f8c2f728f5ce35ac85ddf093ac81d0c1a35bf1c25c377
MD5 49a08d895570aabba4cc7842802ba20b
BLAKE2b-256 874a60b0ce7dc1ac04e9c48fc398afed557f0f0cb3fd74c07cb71b567a041157

See more details on using hashes here.

File details

Details for the file ray-2.47.1-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for ray-2.47.1-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 36a30930e8d265e708df96f37f6f1f5484f4b97090d505912f992e045a69d310
MD5 8cffa8bd81c4b90e194a1497f8e3b0ad
BLAKE2b-256 92fe2f1fc21b7a321385fe34fd159c27245c06bad795aba7de71f29e7a00e741

See more details on using hashes here.

File details

Details for the file ray-2.47.1-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: ray-2.47.1-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 26.2 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.9.12

File hashes

Hashes for ray-2.47.1-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 dee02ba9b8cd45c4eadc457183f6d80f1701b85f966d02cdacd5b11867cb7375
MD5 b2e0420f9d336a8af96f3507787a92f5
BLAKE2b-256 201d64e6d6751b11668a634c7983dffc7a739487de4544110a6cacdf33c1da64

See more details on using hashes here.

File details

Details for the file ray-2.47.1-cp39-cp39-manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for ray-2.47.1-cp39-cp39-manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 106817f80087d21d24e63f6e56ea5ab7c387a25105eb65e6b783551f569534ea
MD5 13050aed464fd1b48858b2d7423bdf3e
BLAKE2b-256 b2f1734d5fd9835610c7d00ec5bbfb232a8bb4d560d0030d85f749c0eb0ee0f2

See more details on using hashes here.

File details

Details for the file ray-2.47.1-cp39-cp39-manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for ray-2.47.1-cp39-cp39-manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 21f2689c1bbc688f9cd31a18bae2c9582027e91b508073849441167bb5077816
MD5 654bd019a616b9e27c70b73a2ddb472b
BLAKE2b-256 9b0fcec17486997fae42b00a6e82f007514635a556d32217a38204ed4a3a9530

See more details on using hashes here.

File details

Details for the file ray-2.47.1-cp39-cp39-macosx_12_0_x86_64.whl.

File metadata

  • Download URL: ray-2.47.1-cp39-cp39-macosx_12_0_x86_64.whl
  • Upload date:
  • Size: 68.6 MB
  • Tags: CPython 3.9, macOS 12.0+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.9.12

File hashes

Hashes for ray-2.47.1-cp39-cp39-macosx_12_0_x86_64.whl
Algorithm Hash digest
SHA256 6c7b4abe112c4d698243e30023bcbffe2c2c9a68416b95a6a0d50f9ca5725545
MD5 537b32e702bb84de8686fe5c0c332c41
BLAKE2b-256 985c08e162bf1d83709d4bb3d7cffdd12763f6f62605c5c2ad7f3b071a66353e

See more details on using hashes here.

File details

Details for the file ray-2.47.1-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

  • Download URL: ray-2.47.1-cp39-cp39-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 66.1 MB
  • Tags: CPython 3.9, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.9.12

File hashes

Hashes for ray-2.47.1-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 c21720f283a3df360ddec002a592ddfbaf520faf4cb1b86562a7b7c196ad96a0
MD5 59bbe1b572a79afe84e1ea953c444fa0
BLAKE2b-256 cf84055a43a9157869912b7455afbf83a7f01795def134655f82e681496378ed

See more details on using hashes here.

Supported by

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