[
https://issues.apache.org/jira/browse/SPARK-5743?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Guillaume Charhon updated SPARK-5743:
-------------------------------------
Description:
I am running a training a model with a RamdomForest using this code snippet:
model = RandomForest.trainRegressor(trainingData, categoricalFeaturesInfo={},
numTrees=5, featureSubsetStrategy="auto",
impurity='variance', maxDepth=4)
The trainingData RDD looks like :
trainingData.take(2)
[LabeledPoint(0.0, [0.02,0.0,0.05,0.02,0.0,0.05,18.0,123675.0,189.09]),
LabeledPoint(0.0, [0.04,0.1,0.1,0.04,0.1,0.1,4.0,631180.0,157.36])]
which seems ok.
The csv file before loading is 30 MB.
When I run my code snippet, I get this error:
15/02/11 14:31:46 ERROR ActorSystemImpl: Uncaught fatal error from thread
[sparkDriver-akka.actor.default-dispatcher-4] shutting down ActorSystem
[sparkDriver]
java.lang.OutOfMemoryError: Java heap space
at java.util.Arrays.copyOf(Arrays.java:2271)
at
java.io.ByteArrayOutputStream.toByteArray(ByteArrayOutputStream.java:178)
at
org.apache.spark.scheduler.Task$.serializeWithDependencies(Task.scala:143)
at
org.apache.spark.scheduler.TaskSetManager.resourceOffer(TaskSetManager.scala:461)
at
org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$3$$anonfun$apply$7$$anonfun$apply$2.apply$mcVI$sp(TaskSchedulerImpl.scala:259)
at scala.collection.immutable.Range.foreach$mVc$sp(Range.scala:141)
at
org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$3$$anonfun$apply$7.apply(TaskSchedulerImpl.scala:255)
at
org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$3$$anonfun$apply$7.apply(TaskSchedulerImpl.scala:252)
at
scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
at
org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$3.apply(TaskSchedulerImpl.scala:252)
at
org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$3.apply(TaskSchedulerImpl.scala:252)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at
org.apache.spark.scheduler.TaskSchedulerImpl.resourceOffers(TaskSchedulerImpl.scala:252)
at
org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverActor.makeOffers(CoarseGrainedSchedulerBackend.scala:163)
at
org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverActor$$anonfun$receiveWithLogging$1.applyOrElse(CoarseGrainedSchedulerBackend.scala:127)
at
scala.runtime.AbstractPartialFunction$mcVL$sp.apply$mcVL$sp(AbstractPartialFunction.scala:33)
at
scala.runtime.AbstractPartialFunction$mcVL$sp.apply(AbstractPartialFunction.scala:33)
at
scala.runtime.AbstractPartialFunction$mcVL$sp.apply(AbstractPartialFunction.scala:25)
at
org.apache.spark.util.ActorLogReceive$$anon$1.apply(ActorLogReceive.scala:53)
at
org.apache.spark.util.ActorLogReceive$$anon$1.apply(ActorLogReceive.scala:42)
at scala.PartialFunction$class.applyOrElse(PartialFunction.scala:118)
at
org.apache.spark.util.ActorLogReceive$$anon$1.applyOrElse(ActorLogReceive.scala:42)
at akka.actor.Actor$class.aroundReceive(Actor.scala:465)
at
org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverActor.aroundReceive(CoarseGrainedSchedulerBackend.scala:72)
at akka.actor.ActorCell.receiveMessage(ActorCell.scala:516)
at akka.actor.ActorCell.invoke(ActorCell.scala:487)
at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:238)
at akka.dispatch.Mailbox.run(Mailbox.scala:220)
at
akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:393)
at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
15/02/11 14:31:46 INFO DAGScheduler: Job 2 failed: count at
DataValidators.scala:38, took 4.793047 s
PS : I am using Spark 1.2.0 with Python on machines of 8 cores and 32 GB of
RAM. I have 1 master and 4 worker nodes. I have also tried with up to 30
machines of 16 cores and 104 GB each.
was:
I am running a training a model with a RamdomForest using this code snippet:
model = RandomForest.trainRegressor(trainingData, categoricalFeaturesInfo={},
numTrees=5, featureSubsetStrategy="auto",
impurity='variance', maxDepth=4)
The trainingData RDD looks like :
trainingData.take(2)
[LabeledPoint(0.0, [0.02,0.0,0.05,0.02,0.0,0.05,18.0,123675.0,189.09]),
LabeledPoint(0.0, [0.04,0.1,0.1,0.04,0.1,0.1,4.0,631180.0,157.36])]
which seems ok.
The csv file before loading is 30 MB.
When I run my code snippet, I get this error:
15/02/11 14:31:46 ERROR ActorSystemImpl: Uncaught fatal error from thread
[sparkDriver-akka.actor.default-dispatcher-4] shutting down ActorSystem
[sparkDriver]
java.lang.OutOfMemoryError: Java heap space
at java.util.Arrays.copyOf(Arrays.java:2271)
at
java.io.ByteArrayOutputStream.toByteArray(ByteArrayOutputStream.java:178)
at
org.apache.spark.scheduler.Task$.serializeWithDependencies(Task.scala:143)
at
org.apache.spark.scheduler.TaskSetManager.resourceOffer(TaskSetManager.scala:461)
at
org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$3$$anonfun$apply$7$$anonfun$apply$2.apply$mcVI$sp(TaskSchedulerImpl.scala:259)
at scala.collection.immutable.Range.foreach$mVc$sp(Range.scala:141)
at
org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$3$$anonfun$apply$7.apply(TaskSchedulerImpl.scala:255)
at
org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$3$$anonfun$apply$7.apply(TaskSchedulerImpl.scala:252)
at
scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
at
org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$3.apply(TaskSchedulerImpl.scala:252)
at
org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$3.apply(TaskSchedulerImpl.scala:252)
at
scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at
org.apache.spark.scheduler.TaskSchedulerImpl.resourceOffers(TaskSchedulerImpl.scala:252)
at
org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverActor.makeOffers(CoarseGrainedSchedulerBackend.scala:163)
at
org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverActor$$anonfun$receiveWithLogging$1.applyOrElse(CoarseGrainedSchedulerBackend.scala:127)
at
scala.runtime.AbstractPartialFunction$mcVL$sp.apply$mcVL$sp(AbstractPartialFunction.scala:33)
at
scala.runtime.AbstractPartialFunction$mcVL$sp.apply(AbstractPartialFunction.scala:33)
at
scala.runtime.AbstractPartialFunction$mcVL$sp.apply(AbstractPartialFunction.scala:25)
at
org.apache.spark.util.ActorLogReceive$$anon$1.apply(ActorLogReceive.scala:53)
at
org.apache.spark.util.ActorLogReceive$$anon$1.apply(ActorLogReceive.scala:42)
at scala.PartialFunction$class.applyOrElse(PartialFunction.scala:118)
at
org.apache.spark.util.ActorLogReceive$$anon$1.applyOrElse(ActorLogReceive.scala:42)
at akka.actor.Actor$class.aroundReceive(Actor.scala:465)
at
org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverActor.aroundReceive(CoarseGrainedSchedulerBackend.scala:72)
at akka.actor.ActorCell.receiveMessage(ActorCell.scala:516)
at akka.actor.ActorCell.invoke(ActorCell.scala:487)
at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:238)
at akka.dispatch.Mailbox.run(Mailbox.scala:220)
at
akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:393)
at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
15/02/11 14:31:46 INFO DAGScheduler: Job 2 failed: count at
DataValidators.scala:38, took 4.793047 s
PS : I am using Spark 1.2.0 with Python on machines of 8 cores and 32 GB of
RAM. I have 1 master and 4 worker nodes. I have also tried with up to 30
machines of 16 cores and 104 MB each.
> java.lang.OutOfMemoryError: Java heap space with RandomForest
> -------------------------------------------------------------
>
> Key: SPARK-5743
> URL: https://issues.apache.org/jira/browse/SPARK-5743
> Project: Spark
> Issue Type: Bug
> Components: MLlib
> Affects Versions: 1.2.0
> Environment: Debian 7 running on Google Compute Engine
> Reporter: Guillaume Charhon
>
> I am running a training a model with a RamdomForest using this code snippet:
> model = RandomForest.trainRegressor(trainingData, categoricalFeaturesInfo={},
> numTrees=5, featureSubsetStrategy="auto",
> impurity='variance', maxDepth=4)
> The trainingData RDD looks like :
> trainingData.take(2)
> [LabeledPoint(0.0, [0.02,0.0,0.05,0.02,0.0,0.05,18.0,123675.0,189.09]),
> LabeledPoint(0.0, [0.04,0.1,0.1,0.04,0.1,0.1,4.0,631180.0,157.36])]
> which seems ok.
> The csv file before loading is 30 MB.
> When I run my code snippet, I get this error:
> 15/02/11 14:31:46 ERROR ActorSystemImpl: Uncaught fatal error from thread
> [sparkDriver-akka.actor.default-dispatcher-4] shutting down ActorSystem
> [sparkDriver]
> java.lang.OutOfMemoryError: Java heap space
> at java.util.Arrays.copyOf(Arrays.java:2271)
> at
> java.io.ByteArrayOutputStream.toByteArray(ByteArrayOutputStream.java:178)
> at
> org.apache.spark.scheduler.Task$.serializeWithDependencies(Task.scala:143)
> at
> org.apache.spark.scheduler.TaskSetManager.resourceOffer(TaskSetManager.scala:461)
> at
> org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$3$$anonfun$apply$7$$anonfun$apply$2.apply$mcVI$sp(TaskSchedulerImpl.scala:259)
> at scala.collection.immutable.Range.foreach$mVc$sp(Range.scala:141)
> at
> org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$3$$anonfun$apply$7.apply(TaskSchedulerImpl.scala:255)
> at
> org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$3$$anonfun$apply$7.apply(TaskSchedulerImpl.scala:252)
> at
> scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
> at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
> at
> org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$3.apply(TaskSchedulerImpl.scala:252)
> at
> org.apache.spark.scheduler.TaskSchedulerImpl$$anonfun$resourceOffers$3.apply(TaskSchedulerImpl.scala:252)
> at
> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
> at
> org.apache.spark.scheduler.TaskSchedulerImpl.resourceOffers(TaskSchedulerImpl.scala:252)
> at
> org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverActor.makeOffers(CoarseGrainedSchedulerBackend.scala:163)
> at
> org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverActor$$anonfun$receiveWithLogging$1.applyOrElse(CoarseGrainedSchedulerBackend.scala:127)
> at
> scala.runtime.AbstractPartialFunction$mcVL$sp.apply$mcVL$sp(AbstractPartialFunction.scala:33)
> at
> scala.runtime.AbstractPartialFunction$mcVL$sp.apply(AbstractPartialFunction.scala:33)
> at
> scala.runtime.AbstractPartialFunction$mcVL$sp.apply(AbstractPartialFunction.scala:25)
> at
> org.apache.spark.util.ActorLogReceive$$anon$1.apply(ActorLogReceive.scala:53)
> at
> org.apache.spark.util.ActorLogReceive$$anon$1.apply(ActorLogReceive.scala:42)
> at scala.PartialFunction$class.applyOrElse(PartialFunction.scala:118)
> at
> org.apache.spark.util.ActorLogReceive$$anon$1.applyOrElse(ActorLogReceive.scala:42)
> at akka.actor.Actor$class.aroundReceive(Actor.scala:465)
> at
> org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend$DriverActor.aroundReceive(CoarseGrainedSchedulerBackend.scala:72)
> at akka.actor.ActorCell.receiveMessage(ActorCell.scala:516)
> at akka.actor.ActorCell.invoke(ActorCell.scala:487)
> at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:238)
> at akka.dispatch.Mailbox.run(Mailbox.scala:220)
> at
> akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:393)
> at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260)
> 15/02/11 14:31:46 INFO DAGScheduler: Job 2 failed: count at
> DataValidators.scala:38, took 4.793047 s
> PS : I am using Spark 1.2.0 with Python on machines of 8 cores and 32 GB of
> RAM. I have 1 master and 4 worker nodes. I have also tried with up to 30
> machines of 16 cores and 104 GB each.
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