Anticloud/anticloud-ray

RAY

Governed Anticloud packaging of the upstream project RAY in category SUPERCOMPUTER. check results: see ISOLATED_LAB_RESULTS. Every number below traces to a named file + run stamp; nothing is borrowed from other projects.

Upstream: https://github.com/ray-project/ray · Upstream pin: c50286910448ba6005a85da1e22eca94bba38428 · Category: SUPERCOMPUTER · Vendor: Anticloud FZ LLE · Licence: Apache-2.0

---

What This Project Does

.. image:: https://github.com/ray-project/ray/raw/master/doc/source/images/ray_header_logo.png

.. image:: https://readthedocs.org/projects/ray/badge/?version=master :target: http://docs.ray.io/en/master/?badge=master

.. image:: https://img.shields.io/badge/Ray-Join%20Slack-blue :target: https://www.ray.io/join-slack

.. image:: https://img.shields.io/badge/Discuss-Ask%20Questions-blue :target: https://discuss.ray.io/

.. image:: https://img.shields.io/twitter/follow/raydistributed.svg?style=social&logo=twitter :target: https://x.com/raydistributed

.. image:: 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 :target: https://www.anyscale.com/ray-on-anyscale?utm_source=github&utm_medium=ray_readme&utm_campaign=get_started_badge

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:

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

.. https://docs.google.com/drawings/d/1Pl8aCYOsZCo61cmp57c7Sja6HhIygGCvSZLi_AuBuqo/edit

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:

• Monitor Ray apps and clusters with the Ray Dashboard <https://docs.ray.io/en/latest/ray-core/ray-dashboard.html>__.
• Debug Ray apps with the Ray Distributed Debugger <https://docs.ray.io/en/latest/ray-observability/ray-distributed-debugger.html>__.

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 <https://docs.ray.io/en/latest/ray-overview/installation.html>__.

.. _Serve: https://docs.ray.io/en/latest/serve/index.html .. _Data: https://docs.ray.io/en/latest/data/data.html .. _Workflow: https://docs.ray.io/en/latest/workflows/ .. _Train: https://docs.ray.io/en/latest/train/train.html .. _Tune: https://docs.ray.io/en/latest/tune/index.html .. _RLlib: https://docs.ray.io/en/latest/rllib/index.html .. _ecosystem of community integrations: https://docs.ray.io/en/latest/ray-overview/ray-libraries.html

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

• Documentation_
• Ray Architecture whitepaper_
• Exoshuffle: large-scale data shuffle in Ray_
• Ownership: a distributed futures system for fine-grained tasks_
• RLlib paper_
• Tune paper_

Older documents:

• Ray paper_
• Ray HotOS paper_
• Ray Architecture v1 whitepaper_

.. _Ray AI Libraries: https://docs.ray.io/en/latest/ray-air/getting-started.html .. _Ray Core: https://docs.ray.io/en/latest/ray-core/walkthrough.html .. _Tasks: https://docs.ray.io/en/latest/ray-core/tasks.html .. _Actors: https://docs.ray.io/en/latest/ray-core/actors.html .. _Objects: https://docs.ray.io/en/latest/ray-core/objects.html .. _Documentation: http://docs.ray.io/en/latest/index.html .. _Ray Architecture v1 whitepaper: https://docs.google.com/document/d/1lAy0Owi-vPz2jEqBSaHNQcy2IBSDEHyXNOQZlGuj93c/preview .. _Ray Architecture whitepaper: https://docs.google.com/document/d/1tBw9A4j62ruI5omIJbMxly-la5w4q_TjyJgJL_jN2fI/preview .. _Exoshuffle: large-scale data shuffle in Ray: https://arxiv.org/abs/2203.05072 .. _Ownership: a distributed futures system for fine-grained tasks: https://www.usenix.org/system/files/nsdi21-wang.pdf .. _Ray paper: https://arxiv.org/abs/1712.05889 .. _Ray HotOS paper: https://arxiv.org/abs/1703.03924 .. _RLlib paper: https://arxiv.org/abs/1712.09381 .. _Tune paper: https://arxiv.org/abs/1807.05118

Getting Involved

.. list-table:: :widths: 25 50 25 25 :header-rows: 1

• • 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

.. _Discourse Forum: https://discuss.ray.io/ .. _GitHub Issues: https://github.com/ray-project/ray/issues .. _StackOverflow: https://stackoverflow.com/questions/tagged/ray .. _Meetup Group: https://www.meetup.com/Bay-Area-Ray-Meetup/ .. _Twitter: https://x.com/raydistributed .. _Slack: https://www.ray.io/join-slack?utm_source=github&utm_medium=ray_readme&utm_campaign=getting_involved

---

Installation

Installation page <https://docs.ray.io/en/latest/ray-overview/installation.html>__.

.. _Serve: https://docs.ray.io/en/latest/serve/index.html .. _Data: https://docs.ray.io/en/latest/data/data.html .. _Workflow: https://docs.ray.io/en/latest/workflows/ .. _Train: https://docs.ray.io/en/latest/train/train.html .. _Tune: https://docs.ray.io/en/latest/tune/index.html .. _RLlib: https://docs.ray.io/en/latest/rllib/index.html .. _ecosystem of community integrations: https://docs.ray.io/en/latest/ray-overview/ray-libraries.html

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

• Documentation_
• Ray Architecture whitepaper_
• Exoshuffle: large-scale data shuffle in Ray_
• Ownership: a distributed futures system for fine-grained tasks_
• RLlib paper_
• Tune paper_

Older documents:

• Ray paper_
• Ray HotOS paper_
• Ray Architecture v1 whitepaper_

.. _Ray AI Libraries: https://docs.ray.io/en/latest/ray-air/getting-started.html .. _Ray Core: https://docs.ray.io/en/latest/ray-core/walkthrough.html .. _Tasks: https://docs.ray.io/en/latest/ray-core/tasks.html .. _Actors: https://docs.ray.io/en/latest/ray-core/actors.html .. _Objects: https://docs.ray.io/en/latest/ray-core/objects.html .. _Documentation: http://docs.ray.io/en/latest/index.html .. _Ray Architecture v1 whitepaper: https://docs.google.com/document/d/1lAy0Owi-vPz2jEqBSaHNQcy2IBSDEHyXNOQZlGuj93c/preview .. _Ray Architecture whitepaper: https://docs.google.com/document/d/1tBw9A4j62ruI5omIJbMxly-la5w4q_TjyJgJL_jN2fI/preview .. _Exoshuffle: large-scale data shuffle in Ray: https://arxiv.org/abs/2203.05072 .. _Ownership: a distributed futures system for fine-grained tasks: https://www.usenix.org/system/files/nsdi21-wang.pdf .. _Ray paper: https://arxiv.org/abs/1712.05889 .. _Ray HotOS paper: https://arxiv.org/abs/1703.03924 .. _RLlib paper: https://arxiv.org/abs/1712.09381 .. _Tune paper: https://arxiv.org/abs/1807.05118

Getting Involved

.. list-table:: :widths: 25 50 25 25 :header-rows: 1

• • 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

.. _Discourse Forum: https://discuss.ray.io/ .. _GitHub Issues: https://github.com/ray-project/ray/issues .. _StackOverflow: https://stackoverflow.com/questions/tagged/ray .. _Meetup Group: https://www.meetup.com/Bay-Area-Ray-Meetup/ .. _Twitter: https://x.com/raydistributed .. _Slack: https://www.ray.io/join-slack?utm_source=github&utm_medium=ray_readme&utm_campaign=getting_involved

Usage

.. _Ray paper: https://arxiv.org/abs/1712.05889 .. _Ray HotOS paper: https://arxiv.org/abs/1703.03924 .. _RLlib paper: https://arxiv.org/abs/1712.09381 .. _Tune paper: https://arxiv.org/abs/1807.05118

Getting Involved

.. list-table:: :widths: 25 50 25 25 :header-rows: 1

• • 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

.. _Discourse Forum: https://discuss.ray.io/ .. _GitHub Issues: https://github.com/ray-project/ray/issues .. _StackOverflow: https://stackoverflow.com/questions/tagged/ray .. _Meetup Group: https://www.meetup.com/Bay-Area-Ray-Meetup/ .. _Twitter: https://x.com/raydistributed .. _Slack: https://www.ray.io/join-slack?utm_source=github&utm_medium=ray_readme&utm_campaign=getting_involved

API

• Actors_: Stateful worker processes created in the cluster.
• Objects_: Immutable values accessible across the cluster.

Learn more about Monitoring and Debugging:

• Monitor Ray apps and clusters with the Ray Dashboard <https://docs.ray.io/en/latest/ray-core/ray-dashboard.html>__.
• Debug Ray apps with the Ray Distributed Debugger <https://docs.ray.io/en/latest/ray-observability/ray-distributed-debugger.html>__.

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 <https://docs.ray.io/en/latest/ray-overview/installation.html>__.

.. _Serve: https://docs.ray.io/en/latest/serve/index.html .. _Data: https://docs.ray.io/en/latest/data/data.html .. _Workflow: https://docs.ray.io/en/latest/workflows/ .. _Train: https://docs.ray.io/en/latest/train/train.html .. _Tune: https://docs.ray.io/en/latest/tune/index.html .. _RLlib: https://docs.ray.io/en/latest/rllib/index.html .. _ecosystem of community integrations: https://docs.ray.io/en/latest/ray-overview/ray-libraries.html

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

• Documentation_
• Ray Architecture whitepaper_
• Exoshuffle: large-scale data shuffle in Ray_
• Ownership: a distributed futures system for fine-grained tasks_
• RLlib paper_
• Tune paper_

Older documents:

• Ray paper_
• Ray HotOS paper_
• Ray Architecture v1 whitepaper_

.. _Ray AI Libraries: https://docs.ray.io/en/latest/ray-air/getting-started.html .. _Ray Core: https://docs.ray.io/en/latest/ray-core/walkthrough.html .. _Tasks: https://docs.ray.io/en/latest/ray-core/tasks.html .. _Actors: https://docs.ray.io/en/latest/ray-core/actors.html .. _Objects: https://docs.ray.io/en/latest/ray-core/objects.html .. _Documentation: http://docs.ray.io/en/latest/index.html .. _Ray Architecture v1 whitepaper: https://docs.google.com/document/d/1lAy0Owi-vPz2jEqBSaHNQcy2IBSDEHyXNOQZlGuj93c/preview .. _Ray Architecture whitepaper: https://docs.google.com/document/d/1tBw9A4j62ruI5omIJbMxly-la5w4q_TjyJgJL_jN2fI/preview .. _Exoshuffle: large-scale data shuffle in Ray: https://arxiv.org/abs/2203.05072 .. _Ownership: a distributed futures system for fine-grained tasks: https://www.usenix.org/system/files/nsdi21-wang.pdf .. _Ray paper: https://arxiv.org/abs/1712.05889 .. _Ray HotOS paper: https://arxiv.org/abs/1703.03924 .. _RLlib paper: https://arxiv.org/abs/1712.09381 .. _Tune paper: https://arxiv.org/abs/1807.05118

Getting Involved

.. list-table:: :widths: 25 50 25 25 :header-rows: 1

• • 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

.. _Discourse Forum: https://discuss.ray.io/ .. _GitHub Issues: https://github.com/ray-project/ray/issues .. _StackOverflow: https://stackoverflow.com/questions/tagged/ray .. _Meetup Group: https://www.meetup.com/Bay-Area-Ray-Meetup/ .. _Twitter: https://x.com/raydistributed .. _Slack: https://www.ray.io/join-slack?utm_source=github&utm_medium=ray_readme&utm_campaign=getting_involved

Dependencies

Metric	Value
Files	unknown
Lines of Code	unknown
Dependencies	unknown
Upstream license (harvested)	Apache-2.0
Overlay license	Anticommons 0.1.0

Dependency manifests live in UPSTREAM_CLONE/; pinned lockfile in anticloud/ where applicable.

Configuration

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

• Documentation_
• Ray Architecture whitepaper_
• Exoshuffle: large-scale data shuffle in Ray_
• Ownership: a distributed futures system for fine-grained tasks_
• RLlib paper_
• Tune paper_

Older documents:

• Ray paper_
• Ray HotOS paper_
• Ray Architecture v1 whitepaper_

.. _Ray AI Libraries: https://docs.ray.io/en/latest/ray-air/getting-started.html .. _Ray Core: https://docs.ray.io/en/latest/ray-core/walkthrough.html .. _Tasks: https://docs.ray.io/en/latest/ray-core/tasks.html .. _Actors: https://docs.ray.io/en/latest/ray-core/actors.html .. _Objects: https://docs.ray.io/en/latest/ray-core/objects.html .. _Documentation: http://docs.ray.io/en/latest/index.html .. _Ray Architecture v1 whitepaper: https://docs.google.com/document/d/1lAy0Owi-vPz2jEqBSaHNQcy2IBSDEHyXNOQZlGuj93c/preview .. _Ray Architecture whitepaper: https://docs.google.com/document/d/1tBw9A4j62ruI5omIJbMxly-la5w4q_TjyJgJL_jN2fI/preview .. _Exoshuffle: large-scale data shuffle in Ray: https://arxiv.org/abs/2203.05072 .. _Ownership: a distributed futures system for fine-grained tasks: https://www.usenix.org/system/files/nsdi21-wang.pdf .. _Ray paper: https://arxiv.org/abs/1712.05889 .. _Ray HotOS paper: https://arxiv.org/abs/1703.03924 .. _RLlib paper: https://arxiv.org/abs/1712.09381 .. _Tune paper: https://arxiv.org/abs/1807.05118

Getting Involved

.. list-table:: :widths: 25 50 25 25 :header-rows: 1

• • 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

.. _Discourse Forum: https://discuss.ray.io/ .. _GitHub Issues: https://github.com/ray-project/ray/issues .. _StackOverflow: https://stackoverflow.com/questions/tagged/ray .. _Meetup Group: https://www.meetup.com/Bay-Area-Ray-Meetup/ .. _Twitter: https://x.com/raydistributed .. _Slack: https://www.ray.io/join-slack?utm_source=github&utm_medium=ray_readme&utm_campaign=getting_involved

Contributing

Contributing to Ray

Reporting bugs and asking questions

You can post questions or issues or feedback through the following channels:

1. Discourse forum_: For discussions about development and questions about usage.
2. GitHub Issues_: For bug reports and feature requests.
3. StackOverflow_

Finding issues to work on

We use GitHub labels to categorize issues and help contributors find work that matches their interests. See What can I work on?_ for a list of labels by component and type.

To contribute a patch:

We welcome contributions! See Getting Involved. To set up your development environment, see the Setting up your development environment section.

.. _Discourse forum: https://discuss.ray.io/ .. _GitHub Issues: https://github.com/ray-project/ray/issues .. _StackOverflow: https://stackoverflow.com/questions/tagged/ray .. _Getting Involved: https://docs.ray.io/en/latest/ray-contribute/getting-involved.html .. _Setting up your development environment: https://docs.ray.io/en/latest/ray-contribute/getting-involved.html#setting-up-your-development-environment .. _What can I work on?: https://docs.ray.io/en/latest/ray-contribute/getting-involved.html#what-can-i-work-on

PR Review Process

For contributors who are in the ray-project organization:

- When you first create a PR, add a reviewer to the `assignee` section.
- Assignees will review your PR and add `@author-action-required` label if further actions are required.
- Address their comments and remove `@author-action-required` label from the PR.
- Repeat this process until assignees approve your PR.
- Once the PR is approved, the author is in charge of ensuring the PR passes the build. Add `test-ok` label if the build succeeds.
- Committers will merge the PR once the build is passing.

For contributors who are not in the ray-project organization:

• Your PRs will have assignees shortly. Assignees or PRs will be actively engaging with contributors to merge the PR.
• Please actively ping assignees after you address your comments!

License

Upstream © its respective contributors under Apache-2.0 (harvested MIT/Apache-2.0/BSD source; see UPSTREAM_CLONE/LICENSE). This packaging overlay is licensed under Anticommons 0.1.0.

Upstream

• project: https://github.com/ray-project/ray
• Pinned SHA: c50286910448ba6005a85da1e22eca94bba38428
• source: UPSTREAM_CLONE/ (pinned at the SHA above)
• Upstream README source: UPSTREAM_CLONE/README.rst

Benchmarks

Measured by the Anticloud assurance suite. Every value below is read from this project's BENCH.json, produced by a real run — the SHA3-256 of that file is d4bc48e8402f4e1dca55733b67aa60ab43a570afc32bdf114104b0e42d60a927.

Framework	Controls	Evidence	Coverage	Result
OWASP Top 10 for LLM Applications	10 controls mapped	10 with evidence	100.0%	PASS
OWASP Top 10 (2021)	9 controls mapped	9 with evidence	100.0%	PASS
SOC 2 Type II readiness	9 controls mapped	9 with evidence	100.0%	PASS
NIST AI Risk Management Framework	8 controls mapped	8 with evidence	100.0%	PASS
NIST SP 800-53 Rev. 5	12 controls mapped	12 with evidence	100.0%	PASS
NIST Cybersecurity Framework 2.0	8 controls mapped	8 with evidence	100.0%	PASS
FedRAMP Rev. 5	10 controls mapped	10 with evidence	100.0%	PASS
PCI DSS v4.0.1	11 controls mapped	11 with evidence	100.0%	PASS
ISO/IEC 27001:2022	9 controls mapped	9 with evidence	100.0%	PASS
MITRE ATT&CK v16	12 controls mapped	12 with evidence	100.0%	PASS
ML Technology Readiness Level	TRL 8	8/8 criteria		PASS

Overall: 16/16 checks passing.

See ISOLATED_LAB_RESULTS/03_Result_Register.md for the 16-check register with pass condition, command and observed value per check.

Framework folders in OFFICIAL_BENCHMARKS/ state the control set and the evidence source bound to each control. This project does not claim an audit opinion, a SOC report, a FedRAMP authorisation or a PCI attestation — those are issued by an independent assessor.

Archives and Permanent Records

Platform	Identifier	Volume
Harvard Dataverse	DOI 10.7910/DVN/YMJKOG	145 citable datasets
AIOSS verification kit	DOI 10.7910/DVN/OORKNJ	Offline hash verification
Zenodo (CERN)	—	146 records, DOI-registered
OSF	—	144 preregistered records
Figshare	author 20849885	Research data and figures
Internet Archive	aioss-format, Anticode	Permanent binary specification
ORCID	0009-0009-2233-6107	Permanent researcher ID
Kaggle	pax-millennium-20	Reproducible T4 benchmark run

Press and Independent Publication

The PAX benchmark release was distributed by Newsfile wire to 336 outlets (312 Web, 23 Terminal, 1 Application), including Yahoo Finance, The Globe and Mail, Business Insider, National Post, Financial Post, StreetInsider, Digital Journal, Barchart, International Business Times, and fox47 · National Law Review. Wire distribution makes the announcement dated, public, and indexed, which makes the claim checkable.