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Posted to issues@spark.apache.org by "Asher Krim (JIRA)" <ji...@apache.org> on 2017/06/29 21:20:00 UTC
[jira] [Commented] (SPARK-20797) mllib lda's LocalLDAModel's save:
out of memory.
[ https://issues.apache.org/jira/browse/SPARK-20797?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16068988#comment-16068988 ]
Asher Krim commented on SPARK-20797:
------------------------------------
This looks like a duplicate of https://issues.apache.org/jira/browse/SPARK-19294?
> mllib lda's LocalLDAModel's save: out of memory.
> -------------------------------------------------
>
> Key: SPARK-20797
> URL: https://issues.apache.org/jira/browse/SPARK-20797
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Affects Versions: 1.6.1, 1.6.3, 2.0.0, 2.0.2, 2.1.1
> Reporter: d0evi1
>
> when i try online lda model with large text data(nearly 1 billion chinese news' abstract), the training step went well, but the save step failed. something like below happened (etc. 1.6.1):
> problem 1.bigger than spark.kryoserializer.buffer.max. (turning bigger the param can fix problem 1, but next will lead problem 2),
> problem 2. exceed spark.akka.frameSize. (turning this param too bigger will fail for the reason out of memory, kill it, version > 2.0.0, exceeds max allowed: spark.rpc.message.maxSize).
> when topics num is large(set topic num k=200 is ok, but set k=300 failed), and vocab size is large(nearly 1000,000) too. this problem will appear.
> so i found word2vec's save function is similar to the LocalLDAModel's save function :
> word2vec's problem (use repartition(1) to save) has been fixed [https://github.com/apache/spark/pull/9989,], but LocalLDAModel still use: repartition(1). use single partition when save.
> word2vec's save method from latest code:
> https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala:
> val approxSize = (4L * vectorSize + 15) * numWords
> val nPartitions = ((approxSize / bufferSize) + 1).toInt
> val dataArray = model.toSeq.map { case (w, v) => Data(w, v) }
> spark.createDataFrame(dataArray).repartition(nPartitions).write.parquet(Loader.dataPath(path))
> but the code in mllib.clustering.LDAModel's LocalLDAModel's save:
> https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/clustering/LDAModel.scala
> you'll see:
> val topicsDenseMatrix = topicsMatrix.asBreeze.toDenseMatrix
> val topics = Range(0, k).map { topicInd =>
> Data(Vectors.dense((topicsDenseMatrix(::, topicInd).toArray)), topicInd)
> }
> spark.createDataFrame(topics).repartition(1).write.parquet(Loader.dataPath(path))
> refer to word2vec's save (repartition(nPartitions)), i replace numWords to topic K, repartition(nPartitions) in the LocalLDAModel's save method, recompile the code, deploy the new lda's project with large data on our machine cluster, it works.
> hopes it will fixed in the next version.
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