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https://issues.apache.org/jira/browse/SPARK-32120?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Enrico Minack resolved SPARK-32120.
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Resolution: Not A Bug
Its documented, so it is not a bug. Thanks!
> Single GPU is allocated multiple times
> --------------------------------------
>
> Key: SPARK-32120
> URL: https://issues.apache.org/jira/browse/SPARK-32120
> Project: Spark
> Issue Type: Bug
> Components: Scheduler
> Affects Versions: 3.0.0
> Reporter: Enrico Minack
> Priority: Major
> Attachments: screenshot-2.png, screenshot-3.png
>
>
> I am running Spark in a {{local-cluster[2,1,1024]}} with one GPU per worker,
> task and executor, and two GPUs provided through a GPU discovery script. The
> same GPU is allocated to both executors.
> Discovery script output:
> {code:java}
> {"name": "gpu", "addresses": ["0", "1"]}
> {code}
> Spark local cluster setup through {{spark-shell}}:
> {code:java}
> ./spark-3.0.0-bin-hadoop2.7/bin/spark-shell --master
> "local-cluster[2,1,1024]" --conf
> spark.worker.resource.gpu.discoveryScript=/tmp/gpu.json --conf
> spark.worker.resource.gpu.amount=1 --conf spark.task.resource.gpu.amount=1
> --conf spark.executor.resource.gpu.amount=1
> {code}
> Executor page of this cluster:
> !screenshot-2.png!
> You can see that both executors have the same GPU allocated: {{[1]}}
> Code run in the Spark shell:
> {code:java}
> scala> import org.apache.spark.TaskContext
> import org.apache.spark.TaskContext
> scala> def fn(it: Iterator[java.lang.Long]): Iterator[(String, (String,
> Array[String]))] = { TaskContext.get().resources().mapValues(v => (v.name,
> v.addresses)).iterator }
> fn: (it: Iterator[Long])Iterator[(String, (String, Array[String]))]
> scala> spark.range(0,2,1,2).mapPartitions(fn).collect
> res0: Array[(String, (String, Array[String]))] = Array((gpu,(gpu,Array(1))),
> (gpu,(gpu,Array(1))))
> {code}
> The result shows that each task got GPU {{1}}. The executor page shows that
> each task has been run on different executors (see above screenshot).
> The expected behaviour would have been to have GPU {{0}} assigned to one
> executor and GPU {{1}} to the other executor. Consequently, each partition /
> task should then see a different GPU.
> With Spark 3.0.0-preview2 the allocation was as expected (identical code and
> Spark shell setup):
> {code:java}
> res0: Array[(String, (String, Array[String]))] = Array((gpu,(gpu,Array(0))),
> (gpu,(gpu,Array(1))))
> {code}
> !screenshot-3.png!
> Happy to contribute a patch if this is an accepted bug.
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