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https://issues.apache.org/jira/browse/SPARK-32429?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17165903#comment-17165903
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Xiangrui Meng commented on SPARK-32429:
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Couple questions:
1. Which GPU resource name do we use? "spark.task.resource.gpu" does not have
special meaning in the current implemetnation.
2. I think we can do this for PySpark workers if 1) gets resolved. However, for
executors running inside the same JVM, is there a way to set
CUDA_VISIBLE_DEVICES differently per executor thread?
> Standalone Mode allow setting CUDA_VISIBLE_DEVICES on executor launch
> ---------------------------------------------------------------------
>
> Key: SPARK-32429
> URL: https://issues.apache.org/jira/browse/SPARK-32429
> Project: Spark
> Issue Type: Improvement
> Components: Deploy
> Affects Versions: 3.0.0
> Reporter: Thomas Graves
> Priority: Major
>
> It would be nice if standalone mode could allow users to set
> CUDA_VISIBLE_DEVICES before launching an executor. This has multiple
> benefits.
> * kind of an isolation in that the executor can only see the GPUs set there.
> * If your GPU application doesn't support explicitly setting the GPU device
> id, setting this will make any GPU look like the default (id 0) and things
> generally just work without any explicit setting
> * New features are being added on newer GPUs that require explicit setting
> of CUDA_VISIBLE_DEVICES like MIG
> ([https://www.nvidia.com/en-us/technologies/multi-instance-gpu/])
> The code changes to just set this are very small, once we set them we would
> also possibly need to change the gpu addresses as it changes them to start
> from device id 0 again.
> The easiest implementation would just specifically support this and have it
> behind a config and set when the config is on and GPU resources are
> allocated.
> Note we probably want to have this same thing set when we launch a python
> process as well so that it gets same env.
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