.. _data:

==================================
Ray Data: Scalable Datasets for ML
==================================

.. toctree::
    :hidden:

    Overview <overview>
    quickstart
    user-guide
    examples
    api/api
    data-internals

Ray Data is a scalable data processing library for ML workloads. It provides flexible and performant APIs for scaling :ref:`Offline batch inference <batch_inference_overview>` and :ref:`Data preprocessing and ingest for ML training <ml_ingest_overview>`. Ray Data uses `streaming execution <https://www.anyscale.com/blog/streaming-distributed-execution-across-cpus-and-gpus>`__ to efficiently process large datasets.

..
  https://docs.google.com/drawings/d/16AwJeBNR46_TsrkOmMbGaBK7u-OPsf_V8fHjU-d2PPQ/edit

Install Ray Data
----------------

To install Ray Data, run:

.. code-block:: console

    $ pip install -U 'ray[data]'

To learn more about installing Ray and its libraries, see
:ref:`Installing Ray <installation>`.

Learn more
----------

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        **Ray Data Overview**
        ^^^

        Get an overview of Ray Data, the workloads that it supports, and how it compares to alternatives.

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        .. button-ref:: data_overview
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            Ray Data Overview

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        **Quickstart**
        ^^^

        Understand the key concepts behind Ray Data. Learn what
        Datasets are and how they're used.

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            Quickstart

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        **User Guides**
        ^^^

        Learn how to use Ray Data, from basic usage to end-to-end guides.

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        .. button-ref:: data_user_guide
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            Learn how to use Ray Data

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        **Examples**
        ^^^

        Find both simple and scaling-out examples of using Ray Data.

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            Ray Data Examples

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        **API**
        ^^^

        Get more in-depth information about the Ray Data API.

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            Read the API Reference

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        **Ray Blogs**
        ^^^

        Get the latest on engineering updates from the Ray team and how companies are using Ray Data.

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            Read the Ray blogs