[jira] [Commented] (SPARK-4846) When the vocabulary size is large, Word2Vec may yield "OutOfMemoryError: Requested array size exceeds VM limit"

["Tung Dang (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-4846?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15121503#comment-15121503 ] 

Tung Dang commented on SPARK-4846:
----------------------------------

[~josephkb]: I have changed the mode to yarn-cluster, however it seems that the implementation of word2vec has some problem with memory management. I give you some details about my experiment:

The dataset is only 2.8GB big with about 700K different words and vector length is only 200, so syn0Global and syn1Global should be around 1.2GB. For spark 1.5.1, I contantly receive this exception even with 100GB for driver (-Xmx80G), 120GB for each worker (10 total). I then switched to 1.6.0, it worked with just 8G for driver and 20GB for each worker (what I expected). However, if I increase the vector length to 400, I receive this exception again even with 100GB driver and 120GB worker.

The word2vec model should not be that big. Could you please give me some hint how I could solve this problem?

> When the vocabulary size is large, Word2Vec may yield "OutOfMemoryError: Requested array size exceeds VM limit"
> ---------------------------------------------------------------------------------------------------------------
>
>                 Key: SPARK-4846
>                 URL: https://issues.apache.org/jira/browse/SPARK-4846
>             Project: Spark
>          Issue Type: Bug
>          Components: MLlib
>    Affects Versions: 1.1.1, 1.2.0
>         Environment: Use Word2Vec to process a corpus(sized 3.5G) with one partition.
> The corpus contains about 300 million words and its vocabulary size is about 10 million.
>            Reporter: Joseph Tang
>            Assignee: Joseph Tang
>            Priority: Minor
>             Fix For: 1.3.0
>
>
> Exception in thread "Driver" java.lang.reflect.InvocationTargetException
>     at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
>     at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
>     at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
>     at java.lang.reflect.Method.invoke(Method.java:606)
>     at org.apache.spark.deploy.yarn.ApplicationMaster$$anon$2.run(ApplicationMaster.scala:162)
> Caused by: java.lang.OutOfMemoryError: Requested array size exceeds VM limit 
>     at java.util.Arrays.copyOf(Arrays.java:2271)
>     at java.io.ByteArrayOutputStream.grow(ByteArrayOutputStream.java:113)
>     at java.io.ByteArrayOutputStream.ensureCapacity(ByteArrayOutputStream.java:93)
>     at java.io.ByteArrayOutputStream.write(ByteArrayOutputStream.java:140)
>     at java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1870)
>     at java.io.ObjectOutputStream$BlockDataOutputStream.setBlockDataMode(ObjectOutputStream.java:1779)
>     at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1186)
>     at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:347)
>     at org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:42)
>     at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:73)
>     at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:164)
>     at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:158)
>     at org.apache.spark.SparkContext.clean(SparkContext.scala:1242)
>     at org.apache.spark.rdd.RDD.mapPartitionsWithIndex(RDD.scala:610)
>     at org.apache.spark.mllib.feature.Word2Vec$$anonfun$fit$1.apply$mcVI$sp(Word2Vec.scala:291)
>     at scala.collection.immutable.Range.foreach$mVc$sp(Range.scala:141)
>     at org.apache.spark.mllib.feature.Word2Vec.fit(Word2Vec.scala:290)



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