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https://issues.apache.org/jira/browse/SPARK-4846?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel=15121503#comment-15121503
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Tung Dang commented on SPARK-4846:
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[~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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