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[docs][core]Accelerator link #48609

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merged 14 commits into from
Nov 6, 2024
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[docs][core]Accelerator link #48609

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c8a256a
link to file in repo with list of accelerator types
angelinalg Oct 19, 2023
559967d
move noqa comment to end of docstring
angelinalg Oct 31, 2023
0ba98a0
trying to fix lint error
angelinalg Oct 31, 2023
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use literalinclude to create versioned page
angelinalg Nov 1, 2023
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remove lingering noqa statements
angelinalg Nov 1, 2023
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angelinalg Nov 1, 2023
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fix toc tree error
angelinalg Nov 1, 2023
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remove trailing white spaces
angelinalg Nov 1, 2023
a041303
link to accelerator page
angelinalg Nov 6, 2024
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weird
angelinalg Nov 6, 2024
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Update doc/source/ray-core/accelerator-types.rst
angelinalg Nov 6, 2024
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Update doc/source/ray-core/accelerator-types.rst
angelinalg Nov 6, 2024
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angelinalg Nov 6, 2024
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6 changes: 3 additions & 3 deletions doc/source/ray-core/scheduling/accelerators.rst
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Expand Up @@ -4,7 +4,7 @@
Accelerator Support
===================

Accelerators (e.g. GPUs) are critical for many machine learning applications.
Accelerators like GPUs are critical for many machine learning apps.
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@pcmoritz pcmoritz Nov 6, 2024 •
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"machine learning workloads" would be the better term here than "machine learning apps" I think :)

Since these are things like training, finetuning, serving, which are more like workloads than apps

Ray Core natively supports many accelerators as pre-defined :ref:`resource <core-resources>` types and allows tasks and actors to specify their accelerator :ref:`resource requirements <resource-requirements>`.

The accelerators natively supported by Ray Core are:
Expand Down Expand Up @@ -37,7 +37,7 @@ The accelerators natively supported by Ray Core are:
- NPU
- Experimental, supported by the community

Starting Ray Nodes with Accelerators
Starting Ray nodes with accelerators
------------------------------------

By default, Ray sets the quantity of accelerator resources of a node to the physical quantities of accelerators auto detected by Ray.
Expand Down Expand Up @@ -637,4 +637,4 @@ This also lets the multi-node-type autoscaler know that there is demand for that

ray.get(train.remote(1))

See ``ray.util.accelerators`` for available accelerator types.
See :ref:`ray.util.accelerators <accelerator_types>` for available accelerator types.
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