Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/18281#discussion_r122543278
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala ---
@@ -325,8 +350,13 @@ final class OneVsRest @Since("1.4.0") (
multiclassLabeled.persist(StorageLevel.MEMORY_AND_DISK)
}
+ val iters = Range(0, numClasses).par
+ iters.tasksupport = new ForkJoinTaskSupport(
+ new ForkJoinPool(Math.min(getParallelism, numClasses))
+ )
+
// create k columns, one for each binary classifier.
- val models = Range(0, numClasses).par.map { index =>
+ val models = iters.map { index =>
--- End diff --
They don't necessarily add anything to here, but they are a more standard
way of parallelism in Spark over using `TaskSupport` and it's more flexible for
setting an ExecutorService. I'm not sure if you can set `TaskSupport` to
`sameThreadExecutor` or what really happens behind the scenes if you make a
`ThreadPoolTaskSupport` with 1 thread.
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