Github user WeichenXu123 commented on a diff in the pull request:
https://github.com/apache/spark/pull/19904#discussion_r156375242
--- 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 --
@holdenk Yes, that's what's done in current master code, but, if in this
way, the future have to be split into a `modelFuture` and the following
`metricFuture`, so it cannot avoid the issue that: `modelFuture` still holds
the `model` computed which cause the memory issue.
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