Ngone51 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_r312094080
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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:
Actually, it does retry when other executors or drivers finish. But, we can
warn if executor or driver requires more resources than any of workers could
have. BTW, I'm thinking do we have the same issue for memory and cores ? For
example, a Worker has 10 cores at most while an executor ask for 20 cores ?
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