Github user zhengruifeng commented on a diff in the pull request:
https://github.com/apache/spark/pull/17014#discussion_r135692470
--- Diff: mllib/src/main/scala/org/apache/spark/ml/Predictor.scala ---
@@ -85,6 +86,10 @@ abstract class Predictor[
M <: PredictionModel[FeaturesType, M]]
extends Estimator[M] with PredictorParams {
+ protected[spark] var storageLevel = StorageLevel.NONE
+
+ protected def handlePersistence = storageLevel == StorageLevel.NONE
--- End diff --
Thanks a lot for reviewing this!
I am OK to revert `Predictor` and define `handlePersistence` in each algs.
However, I don't quite understand `train different dataset parallelly`, do you
mean using multi-threads to call `fit` in single instance?
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