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https://issues.apache.org/jira/browse/SPARK-37208?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Dongjoon Hyun resolved SPARK-37208.
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Fix Version/s: 3.2.1
3.3.0
Resolution: Fixed
Issue resolved by pull request 34485
[https://github.com/apache/spark/pull/34485]
> Support mapping Spark gpu/fpga resource types to custom YARN resource type
> --------------------------------------------------------------------------
>
> Key: SPARK-37208
> URL: https://issues.apache.org/jira/browse/SPARK-37208
> Project: Spark
> Issue Type: Improvement
> Components: YARN
> Affects Versions: 3.0.0
> Reporter: Thomas Graves
> Assignee: Thomas Graves
> Priority: Major
> Fix For: 3.3.0, 3.2.1
>
>
> Currently Spark supports gpu/fpga resource scheduling and specifically on
> YARN it knows how to map gpu/fpga to the YARN resource types yarn.io/gpu and
> yarn.io/fpga. YARN also supports custom resource types and in Hadoop 3.3.1
> made it easier for users to plugin in custom resource types. This means users
> may create a custom resource type that represents a GPU or FPGAs because they
> want additional logic that YARN the built in versions don't have. Ideally
> Spark users still just use the generic "gpu" or "fpga" types in Spark. So we
> should add the ability to change the Spark internal mappings.
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