Ray
Ray is a framework maintained by PyTorch Foundation (hosted project)56. Its source code is public1 under the Apache License 2.02.
Ray is an open-source distributed computing framework for AI workloads, with a core distributed runtime and libraries for data processing, training, and serving. It was developed at UC Berkeley's RISELab and became a PyTorch Foundation-hosted project in October 2025, contributed by Anyscale.185
- Repository: Repository (external site: github.com)
- Documentation: Documentation (external site: docs.ray.io)
- License: LICENSE (external site: github.com)
- Release notes: Releases (external site: github.com)
Availability and license
Overall availability
Documented as available to the general public. Access conditions and license terms may still apply.12
Availability is separate from permission: read the license before using or redistributing.
The LICENSE file applies the Apache License 2.0 to Ray and appends notices for code adapted from other projects under their own licenses (for example, MIT-licensed code from OpenAI).2
Component reuse rights
- weights
- Unknown — no complete fact-level rights review
- code
- Unknown — no complete fact-level rights review
- data
- Unknown — no complete fact-level rights review
- documentation
- Unknown — no complete fact-level rights review
No complete system-rights review is recorded for this release.
Public materials checklist
| Item | Status | Notes and evidence |
|---|---|---|
| Source codeIs the source code publicly readable? | Public | 1 |
| DocumentationIs user documentation published? | Public | 3 |
| InstallationAre installation instructions or packages publicly available? | Public | Installable with pip; community-maintained conda packages and Docker images from the rayproject/ray Docker Hub repository are documented.3 |
| Supported platformsAre supported operating systems or hardware documented? | Public | Documented as officially supporting Linux on x86_64 and aarch64 and Apple silicon; Windows support is in beta.3 |
| Release statusAre versioned releases published? | Public | Versioned releases are published on GitHub.4 |
What it is useful for
Scaling Python data processing, model training, and model serving workloads from a single machine to a cluster.5
Organization context
In the news
Ray 2.59.0 makes Ray Data LLM and Ray Serve LLM generally available
The Ray project published Ray 2.59.0 on GitHub on October 2, 2026. The release notes say the Ray Data LLM and Ray Serve LLM APIs graduate to general availability in this release, alongside an upgrade to vLLM 0.27.0. Ray now turns on token authentication by default for local clusters, while remote and multi-node clusters are unchanged in this release; setting RAY_AUTH_MODE=disabled opts out. The notes also list an external, disk-backed mode for the Ray Data shuffle_v2 backend and native ORC file reading. USASI has not tested this release.
U.S. eligibility
Eligible · basis: U.S.-governed project
Ray is a PyTorch Foundation-hosted project, announced in October 2025. The PyTorch Foundation describes itself as hosted by the Linux Foundation, whose privacy policy gives a contact mailing address in San Francisco, California. Anyscale (San Francisco) contributed the project. Anyscale's pending acquisition by Nscale Limited, a company incorporated in England and Wales, does not move Ray: the companies' July 2026 announcement states that Ray was donated to the PyTorch Foundation and remains community governed.5678910
Sources
This listing is not an endorsement, a safety assessment, or a federal approval.
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Cite this page
APA style
USASI. (2026, September 29). Ray. United States of America Superintelligence. https://unitedstatesofamericasuperintelligence.com/open/ray/
BibTeX
@misc{usasi_ray,
author = {{USASI}},
title = {Ray},
year = {2026},
month = sep,
howpublished = {\url{https://unitedstatesofamericasuperintelligence.com/open/ray/}},
note = {United States of America Superintelligence. Last updated 2026-09-29}
}The date is when this page was last updated. Add the date you read it if your style needs one.
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