[jira] [Comment Edited] (SPARK-4846) When the vocabulary size is large, Word2Vec may yield OutOfMemoryError: Requested array size exceeds VM limit
[ https://issues.apache.org/jira/browse/SPARK-4846?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14295020#comment-14295020 ] Joseph Tang edited comment on SPARK-4846 at 1/28/15 11:26 AM: -- OK. I've added a piece of RuntimeException code and have sent a new PR as below. was (Author: josephtang): OK. I've sent a new PR as below. 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 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) -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Comment Edited] (SPARK-4846) When the vocabulary size is large, Word2Vec may yield OutOfMemoryError: Requested array size exceeds VM limit
[ https://issues.apache.org/jira/browse/SPARK-4846?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14292853#comment-14292853 ] Joseph Tang edited comment on SPARK-4846 at 1/27/15 2:46 AM: - Sorry about the procrastination. I just thought you meant there is no need to implement a dynamic strategy. I'm still working on it and I'd like to quickly fix this issue. Regarding your previous comment, should I throw a customized error in Spark or just an OOM besides the hint about minCount and vectorSize? was (Author: josephtang): Sorry about the procrastination. I'm still working on this. Regarding your previous comment, should I throw a customized error in Spark or just an OOM besides the hint about minCount and vectorSize? 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 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) -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Comment Edited] (SPARK-4846) When the vocabulary size is large, Word2Vec may yield OutOfMemoryError: Requested array size exceeds VM limit
[ https://issues.apache.org/jira/browse/SPARK-4846?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14292853#comment-14292853 ] Joseph Tang edited comment on SPARK-4846 at 1/27/15 2:44 AM: - Sorry about the procrastination. I'm still working on this. Regarding your previous comment, should I throw a customized error in Spark or just an OOM besides the hint about minCount and vectorSize? was (Author: josephtang): Sorry about the procrastination. I'm still working on this. Regarding your previous comment, should I throw an customized error in Spark or just OOM besides the hint about minCount and vectorSize? 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 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) -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
[jira] [Comment Edited] (SPARK-4846) When the vocabulary size is large, Word2Vec may yield OutOfMemoryError: Requested array size exceeds VM limit
[ https://issues.apache.org/jira/browse/SPARK-4846?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanelfocusedCommentId=14292926#comment-14292926 ] Joseph Tang edited comment on SPARK-4846 at 1/27/15 3:42 AM: - I've added some code at https://github.com/jinntrance/spark/compare/w2v-fix?diff=splitname=w2v-fix If it's OK, I would send a new PR to the branch `master`. BTW, sorry for the horrible readability of the difference because of the space indent. was (Author: josephtang): I've added some code at https://github.com/jinntrance/spark/compare/w2v-fix?diff=splitname=w2v-fix If it's OK, I would send a new PR to the branch `master`. 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 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) -- This message was sent by Atlassian JIRA (v6.3.4#6332) - To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org
