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_r312166750
##########
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:
that sounds good for now. Lets also leave it called resource since that is
what its called everywhere right now. just leave off the (mem, core,
accelerator) part.
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