Hi, the Random Forest implementation (1.2.1) is repeatably crashing when I increase the depth to 20. I generate random synthetic data (36 workers, 1,000,000 examples per worker, 30 features per example) as follows:
val data = sc.parallelize(1 to 36, 36).mapPartitionsWithIndex((i, _) => { Array.tabulate(1000000){ _ => new LabeledPoint(Math.random(), Vectors.dense(Array.fill(30)(math.random))) }.toIterator }).cache() ...and then train on a Random Forest with 50 trees, to depth 20: val strategy = new Strategy(Regression, Variance, 20, maxMemoryInMB = 1000) RandomForest.trainRegressor(data, strategy, 50, "sqrt", 1) ...and run on my EC2 cluster (36 slaves, master has 122GB of memory). After number crunching for a couple of hours, I get the following error: [sparkDriver-akka.actor.default-dispatcher-3] shutting down ActorSystem [sparkDriver] 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$BlockDataOutputStream.drain(ObjectOutputStream.java:1876) at java.io.ObjectOutputStream$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:42) at org.apache.spark.serializer.JavaSerializerInstance.serialize(JavaSerializer.scala:73) at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitMissingTasks(DAGScheduler.scala:834) at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitStage(DAGScheduler.scala:778) at org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$submitStage$4.apply(DAGScheduler.scala:781) at org.apache.spark.scheduler.DAGScheduler$$anonfun$org$apache$spark$scheduler$DAGScheduler$$submitStage$4.apply(DAGScheduler.scala:780) at scala.collection.immutable.List.foreach(List.scala:318) at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitStage(DAGScheduler.scala:780) at org.apache.spark.scheduler.DAGScheduler.handleJobSubmitted(DAGScheduler.scala:762) at org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1389) at akka.actor.Actor$class.aroundReceive(Actor.scala:465) at org.apache.spark.scheduler.DAGSchedulerEventProcessActor.aroundReceive(DAGScheduler.scala:1375) 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$AkkaForkJoinTask.exec(AbstractDispatcher.scala:393) at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260) at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339) at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979) at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107) 15/03/11 15:45:51 INFO scheduler.DAGScheduler: Job 92 failed: collectAsMap at DecisionTree.scala:653, took 46.062487 s -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Scaling-problem-in-RandomForest-tp22002.html Sent from the Apache Spark User List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org