Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

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.

Note: This documentation refers to Ant Ray - a fork of Ray maintained by Ant Group. To install this specific version, use:

pip install ant-ray

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

Metadata

Release files for ant-ray-cpp-nightly 3.0.0.dev20260323

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distributions (wheels)

Table of built distributions (wheels) for ant-ray-cpp-nightly 3.0.0.dev20260323
File
ant_ray_cpp_nightly-3.0.0.dev20260323-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.12 CPython 3.12 Linux glibc 2.17+ x86-64 Details
ant_ray_cpp_nightly-3.0.0.dev20260323-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.12 CPython 3.12 Linux glibc 2.17+ ARM64 Details
ant_ray_cpp_nightly-3.0.0.dev20260323-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.11 CPython 3.11 Linux glibc 2.17+ x86-64 Details
ant_ray_cpp_nightly-3.0.0.dev20260323-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.11 CPython 3.11 Linux glibc 2.17+ ARM64 Details
ant_ray_cpp_nightly-3.0.0.dev20260323-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.10 CPython 3.10 Linux glibc 2.17+ x86-64 Details
ant_ray_cpp_nightly-3.0.0.dev20260323-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.10 CPython 3.10 Linux glibc 2.17+ ARM64 Details
ant_ray_cpp_nightly-3.0.0.dev20260323-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl CPython 3.9 CPython 3.9 Linux glibc 2.17+ x86-64 Details
ant_ray_cpp_nightly-3.0.0.dev20260323-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl CPython 3.9 CPython 3.9 Linux glibc 2.17+ ARM64 Details

Total release size: 86.3 MB

Release files / ant_ray_cpp_nightly-3.0.0.dev20260323-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL ant_ray_cpp_nightly-3.0.0.dev20260323-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size 10.9 MB
Tags CPython 3.12 Linux glibc 2.17+ x86-64
SHA-256 checksum
How to use checksums
1a8e691082e8dba49c187785327eaa0777936760d151ccc1a1a7924722cfb0a0
BLAKE2b-256 checksum
How to use checksums
feb550151fd323dd61cfaef5f1391f82e4926514bd1f74a5649890e87d265480
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.7

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Mar 23, 2026.

Transparency log

Release files / ant_ray_cpp_nightly-3.0.0.dev20260323-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

Download URL ant_ray_cpp_nightly-3.0.0.dev20260323-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Size 10.6 MB
Tags CPython 3.12 Linux glibc 2.17+ ARM64
SHA-256 checksum
How to use checksums
c222939a3b176c1466bd0dc865abd0a081895b87337f4a91a5309b84bfe66a81
BLAKE2b-256 checksum
How to use checksums
2053d4c4d64966423b3caae9cbe08b7c9512f83f8d0553c224e9324da5b7c728
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.7

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Mar 23, 2026.

Transparency log

Release files / ant_ray_cpp_nightly-3.0.0.dev20260323-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL ant_ray_cpp_nightly-3.0.0.dev20260323-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size 10.9 MB
Tags CPython 3.11 Linux glibc 2.17+ x86-64
SHA-256 checksum
How to use checksums
01d9d6adc4b299b60f118e78af7d5ace0ab397bfe67f12e7afc1d9c5d3240f0b
BLAKE2b-256 checksum
How to use checksums
210a0136cde80be6f4696c70283d4886ec977b7b08afb83cb0ab30debf52a8bd
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.7

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Mar 23, 2026.

Transparency log

Release files / ant_ray_cpp_nightly-3.0.0.dev20260323-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

Download URL ant_ray_cpp_nightly-3.0.0.dev20260323-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Size 10.6 MB
Tags CPython 3.11 Linux glibc 2.17+ ARM64
SHA-256 checksum
How to use checksums
33b188116ecd33b31a5f9f2671b009332a8618ba323875b939a431ae1cc9c7b5
BLAKE2b-256 checksum
How to use checksums
2e881eeab97acd95b9abbb7e8b3e556be8f17ed4ea6707a8b0c5a0e7e0600c9f
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.7

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Mar 23, 2026.

Transparency log

Release files / ant_ray_cpp_nightly-3.0.0.dev20260323-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL ant_ray_cpp_nightly-3.0.0.dev20260323-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size 10.9 MB
Tags CPython 3.10 Linux glibc 2.17+ x86-64
SHA-256 checksum
How to use checksums
089c85c9c5f3a461075e1719fbb597499b61e5f4bc8b2dc27c3a2c78fb0137ae
BLAKE2b-256 checksum
How to use checksums
2df568a01403b23530932835fee58bbe7be80f27af0285e7e3a4f86df308485a
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.7

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Mar 23, 2026.

Transparency log

Release files / ant_ray_cpp_nightly-3.0.0.dev20260323-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

Download URL ant_ray_cpp_nightly-3.0.0.dev20260323-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Size 10.6 MB
Tags CPython 3.10 Linux glibc 2.17+ ARM64
SHA-256 checksum
How to use checksums
3ddd892593f1527032c15c468e1091ee956d0e9e1a2a308583fa386a2ffa7ad9
BLAKE2b-256 checksum
How to use checksums
22116e7ef0c6a366c70493c13cb7a8dcecaf0490e6e2f8704d09de3a69c42365
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.7

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Mar 23, 2026.

Transparency log

Release files / ant_ray_cpp_nightly-3.0.0.dev20260323-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl

Download URL ant_ray_cpp_nightly-3.0.0.dev20260323-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Size 10.9 MB
Tags CPython 3.9 Linux glibc 2.17+ x86-64
SHA-256 checksum
How to use checksums
ce3f8c6e7d88edd1a88a84894bbeaeee4166f207d376e77361dcf56bd79932e6
BLAKE2b-256 checksum
How to use checksums
53308f6d7598ef1c259e7cbae9a149268702e0cc3b47e59c364e1eea1ad9bdd7
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.7

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Mar 23, 2026.

Transparency log

Release files / ant_ray_cpp_nightly-3.0.0.dev20260323-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl

Download URL ant_ray_cpp_nightly-3.0.0.dev20260323-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Size 10.6 MB
Tags CPython 3.9 Linux glibc 2.17+ ARM64
SHA-256 checksum
How to use checksums
221855223042d3b49fa11a1b87894788f6ce69875e569e521ce01f3e774159c2
BLAKE2b-256 checksum
How to use checksums
3604661704b624972c26cfb3cac75eba2ec5ee4f3a994007e0a67350b11e8465
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.7

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Mar 23, 2026.

Transparency log

Release history Release notifications | RSS feed

This release

2.51.2

8 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page