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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