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[docs] Minor polish on AIR getting started page #27696

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Aug 9, 2022
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[docs] Minor polish on AIR getting started page #27696

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ericl Aug 9, 2022
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2 changes: 1 addition & 1 deletion README.rst
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Expand Up @@ -14,7 +14,7 @@

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Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads:
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for simplifying ML compute:
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a bit worried that "ML compute" isn't a common term for people;

can you do ML compute workloads?

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I don't think so, that's much less memorable and a bit of a mouthful. ML Compute is clear and unambiguous.


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

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2 changes: 1 addition & 1 deletion doc/source/index.md
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Expand Up @@ -85,7 +85,7 @@ or [Slurm](cluster/slurm) clusters.

Ray is a unified framework for scaling AI and Python applications.
Ray consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for
accelerating ML workloads:
simplifying ML compute:

<img src="images/what-is-ray-padded.svg" alt="what-is-ray">

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10 changes: 5 additions & 5 deletions doc/source/ray-air/getting-started.rst
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Expand Up @@ -7,17 +7,17 @@ Ray AI Runtime (AIR)

AIR is currently in **beta**. Fill out `this short form <https://forms.gle/wCCdbaQDtgErYycT6>`__ to get involved. We'll be holding office hours, development sprints, and other activities as we get closer to the GA release. Join us!

Ray AI Runtime (AIR) is a scalable and unified toolkit for ML applications. AIR enables easy scaling of individual workloads, end-to-end workflows, and popular ecosystem frameworks, all in just Python.
Ray AI Runtime (AIR) is a scalable and unified toolkit for ML applications. AIR enables simple scaling of individual workloads, end-to-end workflows, and popular ecosystem frameworks, all in just Python.

..
https://docs.google.com/drawings/d/1atB1dLjZIi8ibJ2-CoHdd3Zzyl_hDRWyK2CJAVBBLdU/edit

.. image:: images/ray-air.svg

AIR builds on best-in-class Ray libraries for :ref:`Preprocessing <datasets>`, :ref:`Training <train-docs>`, :ref:`Tuning <tune-main>`, :ref:`Scoring <air-predictors>`, :ref:`Serving <rayserve>`, and :ref:`Reinforcement Learning <rllib-index>`, which bring together an ecosystem of integrations.
AIR builds on Ray's best-in-class libraries for :ref:`Preprocessing <datasets>`, :ref:`Training <train-docs>`, :ref:`Tuning <tune-main>`, :ref:`Scoring <air-predictors>`, :ref:`Serving <rayserve>`, and :ref:`Reinforcement Learning <rllib-index>` to bring together an ecosystem of integrations.

Why AIR?
--------
ML Compute, Simplified
----------------------

Ray AIR aims to simplify the ecosystem of machine learning frameworks, platforms, and tools. It does this by leveraging Ray to provide a seamless, unified, and open experience for scalable ML:

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----------

- :ref:`air-key-concepts`
- `Examples <https://github.com/ray-project/ray/tree/master/python/ray/air/examples>`__
- :ref:`air-examples-ref`
- :ref:`Deployment Guide <air-deployment>`
- :ref:`API reference <air-api-ref>`
2 changes: 1 addition & 1 deletion doc/source/ray-air/images/why-air-2.svg
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