tgravescs commented on a change in pull request #25047: [SPARK-27371][CORE]
Support GPU-aware resources scheduling in Standalone
URL: https://github.com/apache/spark/pull/25047#discussion_r312111378
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File path: core/src/main/scala/org/apache/spark/deploy/master/Master.scala
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@@ -683,8 +702,7 @@ private[deploy] class Master(
if (app.coresLeft >= coresPerExecutor) {
// Filter out workers that don't have enough resources to launch an
executor
val usableWorkers = workers.toArray.filter(_.state ==
WorkerState.ALIVE)
- .filter(worker => worker.memoryFree >= app.desc.memoryPerExecutorMB
&&
- worker.coresFree >= coresPerExecutor)
+ .filter(canLaunchExecutor(_, app.desc))
.sortBy(_.coresFree).reverse
Review comment:
Right if something changes (other app, other workers, etc) it retries, but
if I'm the only app on the cluster its not clear why the app isn't launching.
The one thing I don't want is it to be to noisy though either. I thought about
that before making the comment, because like you said if its just out of
resources because other apps are running we don't really want to print
anything. I think for now we should just limit it to resources and perhaps
just say no Workers are configured with the resources you requested. If we can
do that without much performance impact lets do it. If not maybe we just file a
separate jira for it and look at it there
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