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https://issues.apache.org/jira/browse/SPARK-10821?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14940370#comment-14940370
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Jay Luan commented on SPARK-10821:
----------------------------------
Thank you for the insight, do you know what the status of the new
implementation of decision tree is or a possible ETA for when it will be ready?
Maybe I can help with either testing or contributing to the code.
> RandomForest serialization OOM during findBestSplits
> ----------------------------------------------------
>
> Key: SPARK-10821
> URL: https://issues.apache.org/jira/browse/SPARK-10821
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Affects Versions: 1.4.0, 1.5.0
> Environment: Amazon EC2 Linux
> Reporter: Jay Luan
> Labels: OOM, out-of-memory
>
> I am getting OOM during serialization for a relatively small dataset for a
> RandomForest. Even with spark.serializer.objectStreamReset at 1, It is still
> running out of memory when attempting to serialize my data.
> Stack Trace:
> Traceback (most recent call last):
> File "/root/random_forest/random_forest_spark.py", line 198, in <module>
> main()
> File "/root/random_forest/random_forest_spark.py", line 166, in main
> trainModel(dset)
> File "/root/random_forest/random_forest_spark.py", line 191, in trainModel
> impurity='gini', maxDepth=4, maxBins=32)
> File "/root/spark/python/lib/pyspark.zip/pyspark/mllib/tree.py", line 352,
> in trainClassifier
> File "/root/spark/python/lib/pyspark.zip/pyspark/mllib/tree.py", line 270,
> in _train
> File "/root/spark/python/lib/pyspark.zip/pyspark/mllib/common.py", line
> 130, in callMLlibFunc
> File "/root/spark/python/lib/pyspark.zip/pyspark/mllib/common.py", line
> 123, in callJavaFunc
> File "/root/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py",
> line 538, in __call__
> File "/root/spark/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py", line
> 300, in get_return_value
> py4j.protocol.Py4JJavaError15/09/25 00:44:41 DEBUG BlockManagerSlaveEndpoint:
> Done removing RDD 7, response is 0
> 15/09/25 00:44:41 DEBUG BlockManagerSlaveEndpoint: Sent response: 0 to
> AkkaRpcEndpointRef(Actor[akka://sparkDriver/temp/$Mj])
> : An error occurred while calling o89.trainRandomForestModel.
> : java.lang.OutOfMemoryError
> at
> java.io.ByteArrayOutputStream.hugeCapacity(ByteArrayOutputStream.java:123)
> at java.io.ByteArrayOutputStream.grow(ByteArrayOutputStream.java:117)
> at
> java.io.ByteArrayOutputStream.ensureCapacity(ByteArrayOutputStream.java:93)
> at java.io.ByteArrayOutputStream.write(ByteArrayOutputStream.java:153)
> at
> java.io.ObjectOutputStream$BlockDataOutputStream.drain(ObjectOutputStream.java:1876)
> at
> java.io.ObjectOutputStream$BlockDataOutputStream.setBlockDataMode(ObjectOutputStream.java:1785)
> at
> java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1188)
> at java.io.ObjectOutputStream.writeObject(ObjectOutputStream.java:347)
> at
> org.apache.spark.serializer.JavaSerializationStream.writeObject(JavaSerializer.scala:44)
> at
> org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:84)
> at
> org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:301)
> at
> org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:294)
> at
> org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:122)
> at org.apache.spark.SparkContext.clean(SparkContext.scala:2021)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:703)
> at
> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:702)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:147)
> at
> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:108)
> at org.apache.spark.rdd.RDD.withScope(RDD.scala:306)
> at org.apache.spark.rdd.RDD.mapPartitions(RDD.scala:702)
> at
> org.apache.spark.mllib.tree.DecisionTree$.findBestSplits(DecisionTree.scala:625)
> at
> org.apache.spark.mllib.tree.RandomForest.run(RandomForest.scala:235)
> at
> org.apache.spark.mllib.tree.RandomForest$.trainClassifier(RandomForest.scala:291)
> at
> org.apache.spark.mllib.api.python.PythonMLLibAPI.trainRandomForestModel(PythonMLLibAPI.scala:742)
> 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 py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
> at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)
> at py4j.Gateway.invoke(Gateway.java:259)
> at
> py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
> at py4j.commands.CallCommand.execute(CallCommand.java:79)
> at py4j.GatewayConnection.run(GatewayConnection.java:207)
> at java.lang.Thread.run(Thread.java:745)
> Details:
> My RDD is type MLLIB LabeledPoint objects, with each holding sparse vectors
> inside. This RDD has a total size of roughly 45MB. My sparse vector has a
> total length of ~15 million while only about 3000 or so are non-zeros. Works
> fine for up to sparse vector size 10 million.
> My cluster is setup on AWS such that my master is a r3.8xlarge along with two
> r3.4xlarge workers. Driver has ~190GB allocated to it while my RDD is ~45MB.
> Configurations as follows:
> spark version: 1.5.0
> -----------------------------------
> spark.executor.memory 32000m
> spark.driver.memory 230000m
> spark.driver.cores 10
> spark.executor.cores 5
> spark.executor.instances 17
> spark.driver.maxResultSize 0
> spark.storage.safetyFraction 1
> spark.storage.memoryFraction 0.9
> spark.storage.shuffleFraction 0.05
> spark.default.parallelism 128
> spark.serializer.objectStreamReset 1
> My original code is in python which I tried on 1.4.0 and 1.5.0, so I thought
> that maybe running something in scala may resolve the problem. I wrote a toy
> scala example and tested it on the same system yielding the same errors. Note
> the test code will most likely eventually throw an error due to the fact
> certain features are always 0 and MLLIB currently errors out during this
> operation.
> Running the following using spark-shell with my spark configuration gives me
> the OOM:
> --------------------------------------------------------------------------
> import scala.util.Random
> import scala.collection.mutable.ArrayBuffer
> import org.apache.spark.mllib.tree.RandomForest
> import org.apache.spark.mllib.tree.model.RandomForestModel
> import org.apache.spark.mllib.util.MLUtils
> import org.apache.spark.mllib.linalg.Vectors
> import org.apache.spark.mllib.regression.LabeledPoint
> val r = Random
> var size = 15000000
> var count = 3000
> val indptr = (1 to size by size/count).toArray
> val data = Seq.fill(count)(r.nextDouble()).toArray
> var dset = ArrayBuffer[LabeledPoint]()
> for (i <- 1 to 10) {
> dset += LabeledPoint(r.nextInt(2), Vectors.sparse(size, indptr, data));
> }
> val distData = sc.parallelize(dset)
> val splits = distData.randomSplit(Array(0.7, 0.3))
> val (trainingData, testData) = (splits(0), splits(1))
> // Train a RandomForest model.
> // Empty categoricalFeaturesInfo indicates all features are continuous.
> val numClasses = 2
> val categoricalFeaturesInfo = Map[Int, Int]()
> val numTrees = 3 // Use more in practice.
> val featureSubsetStrategy = "auto" // Let the algorithm choose.
> val impurity = "gini"
> val maxDepth = 4
> val maxBins = 32
> val model = RandomForest.trainClassifier(trainingData, numClasses,
> categoricalFeaturesInfo,
> numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins)
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