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https://issues.apache.org/jira/browse/SPARK-13048?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15127179#comment-15127179
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holdenk edited comment on SPARK-13048 at 2/1/16 10:43 PM:
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This sounds useful, although we probably wouldn't want to always leave the last
checkpoint around (and we also need to provide a way for the user to cleanup
the last check point).
We could make a getCheckPointedLDAModel or offer a param to the current method
and then add a cleanup function to the resulting LDAModel for the user to call.
Any thoughts [~josephkb] or [~mengxr]? Also an interesting question would be
how to expose something similar in the pipelines API.
was (Author: holdenk):
This sounds useful, although we probably wouldn't want to always leave the last
checkpoint around (and we also need to provide a way for the user to cleanup
the last check point).
We could make a getCheckPointedLDAModel or offer a param to the current method
and then add a cleanup function to the resulting LDAModel for the user to call.
Any thoughts [~josephkb] or [~mengxr]?
> EMLDAOptimizer deletes dependent checkpoint of DistributedLDAModel
> ------------------------------------------------------------------
>
> Key: SPARK-13048
> URL: https://issues.apache.org/jira/browse/SPARK-13048
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Affects Versions: 1.5.2
> Environment: Standalone Spark cluster
> Reporter: Jeff Stein
>
> In EMLDAOptimizer, all checkpoints are deleted before returning the
> DistributedLDAModel.
> The most recent checkpoint is still necessary for operations on the
> DistributedLDAModel under a couple scenarios:
> - The graph doesn't fit in memory on the worker nodes (e.g. very large data
> set).
> - Late worker failures that require reading the now-dependent checkpoint.
> I ran into this problem running a 10M record LDA model in a memory starved
> environment. The model consistently failed in either the {{collect at
> LDAModel.scala:528}} stage (when converting to a LocalLDAModel) or in the
> {{reduce at LDAModel.scala:563}} stage (when calling "describeTopics" on the
> model). In both cases, a FileNotFoundException is thrown attempting to access
> a checkpoint file.
> I'm not sure what the correct fix is here; it might involve a class signature
> change. An alternative simple fix is to leave the last checkpoint around and
> expect the user to clean the checkpoint directory themselves.
> {noformat}
> java.io.FileNotFoundException: File does not exist:
> /hdfs/path/to/checkpoints/c8bd2b4e-27dd-47b3-84ec-3ff0bac04587/rdd-635/part-00071
> {noformat}
> Relevant code is included below.
> LDAOptimizer.scala:
> {noformat}
> override private[clustering] def getLDAModel(iterationTimes:
> Array[Double]): LDAModel = {
> require(graph != null, "graph is null, EMLDAOptimizer not initialized.")
> this.graphCheckpointer.deleteAllCheckpoints()
> // The constructor's default arguments assume gammaShape = 100 to ensure
> equivalence in
> // LDAModel.toLocal conversion
> new DistributedLDAModel(this.graph, this.globalTopicTotals, this.k,
> this.vocabSize,
> Vectors.dense(Array.fill(this.k)(this.docConcentration)),
> this.topicConcentration,
> iterationTimes)
> }
> {noformat}
> PeriodicCheckpointer.scala
> {noformat}
> /**
> * Call this at the end to delete any remaining checkpoint files.
> */
> def deleteAllCheckpoints(): Unit = {
> while (checkpointQueue.nonEmpty) {
> removeCheckpointFile()
> }
> }
> /**
> * Dequeue the oldest checkpointed Dataset, and remove its checkpoint files.
> * This prints a warning but does not fail if the files cannot be removed.
> */
> private def removeCheckpointFile(): Unit = {
> val old = checkpointQueue.dequeue()
> // Since the old checkpoint is not deleted by Spark, we manually delete
> it.
> val fs = FileSystem.get(sc.hadoopConfiguration)
> getCheckpointFiles(old).foreach { checkpointFile =>
> try {
> fs.delete(new Path(checkpointFile), true)
> } catch {
> case e: Exception =>
> logWarning("PeriodicCheckpointer could not remove old checkpoint
> file: " +
> checkpointFile)
> }
> }
> }
> {noformat}
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