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_r311779752
##########
File path: core/src/main/scala/org/apache/spark/deploy/master/Master.scala
##########
@@ -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:
We have an issue here if no Workers have the resources required by the
executor/task requirements, then it doesn't warn/error and it doesn't retry.
Basically I started a Worker without GPUs and then said I need gpus for my
executor task and it end up hanging. I suppose one could argue this is ok as
someone could start another Worker that has the resources, but I think we at
least need to Warn about it
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