Github user dding3 commented on a diff in the pull request:
https://github.com/apache/spark/pull/15125#discussion_r101823330
--- Diff: graphx/src/main/scala/org/apache/spark/graphx/Pregel.scala ---
@@ -155,6 +169,8 @@ object Pregel extends Logging {
i += 1
}
messages.unpersist(blocking = false)
+ graphCheckpointer.deleteAllCheckpoints()
+ messageCheckpointer.deleteAllCheckpoints()
--- End diff --
I think when there is an exception during training, if we keep the
checkpoints, there is a chance for user to recover from it. I checked in
RandomForest/GBT in spark, looks like they only delete the checkpoints when the
training successful finished.
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