Github user MrBago commented on a diff in the pull request: https://github.com/apache/spark/pull/19904#discussion_r156511049 --- Diff: mllib/src/main/scala/org/apache/spark/ml/tuning/CrossValidator.scala --- @@ -146,25 +147,18 @@ class CrossValidator @Since("1.2.0") (@Since("1.4.0") override val uid: String) val validationDataset = sparkSession.createDataFrame(validation, schema).cache() logDebug(s"Train split $splitIndex with multiple sets of parameters.") + val completeFitCount = new AtomicInteger(0) --- End diff -- You can use futures to do this, you need to use a var for `modelFutures` then map on those futures to `Unit` collect those into a sequence and map on that to unpersist, but why go to the trouble. What's the concern with doing it in the final training thread. Why is this a change in behavior?
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