.. meta::
  :description: Ray documentation
  :keywords: Ray, ROCm, documentation, reinforcement learning, deep learning, framework, GPU

.. _ray-documentation-index:

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Ray on ROCm documentation
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Use Ray on ROCm to orchestrate distributed GPU workloads across AMD Instinct
clusters, enabling elastic hyperparameter tuning, reinforcement learning,
and scalable end-to-end machine learning pipelines.

Ray is a unified framework, consisting of `a core distributed 
runtime  <https://docs.ray.io/en/latest/ray-core/walkthrough.html>`__ and a set of 
`AI libraries <https://docs.ray.io/en/latest/ray-air/getting-started.html>`__ for 
simplifying machine learning computations.

Ray is part of the `AMD LLM Extension toolkit
<https://rocm.docs.amd.com/projects/rocm-llmext/en/docs-26.09/>`__.

The Ray public repository is located at `https://github.com/AMD-Ecosystem/ray <https://github.com/AMD-Ecosystem/ray>`__.

.. grid:: 2
  :gutter: 3

  .. grid-item-card:: Install

    * :doc:`Install Ray <install/ray-install>`

  .. grid-item-card:: Examples

      * `Ray examples (upstream) <https://docs.ray.io/en/latest/ray-overview/examples/index.html>`__
      * `Ray use cases (upstream) <https://docs.ray.io/en/latest/ray-overview/use-cases.html>`__

  .. grid-item-card:: Reference

      * `Get started with Ray (upstream) <https://docs.ray.io/en/latest/ray-overview/getting-started.html>`__
      * `Ray Core API (upstream) <https://docs.ray.io/en/latest/ray-core/api/index.html>`__

To contribute to the documentation, refer to
`Contributing to Ray <https://github.com/AMD-Ecosystem/ray/blob/master/CONTRIBUTING.rst>`__.

You can find licensing information on the :doc:`Licensing <about/license>` page.