Could you please file a bug report JIRA and also include more info about what you ran? * Random forest Param settings * dataset dimensionality, partitions, etc. Thanks!
On Tue, Oct 4, 2016 at 10:44 PM, Samkit Shah <samkit...@gmail.com> wrote: > Hello folks, > I am running Random Forest from ml from spark 1.6.1 on bimbo[1] dataset > with following configurations: > > "-Xms16384M" "-Xmx16384M" "-Dspark.locality.wait=0s" > "-Dspark.driver.extraJavaOptions=-Xss10240k > -XX:+PrintGCDetails -XX:+PrintGCTimeStamps -XX:+PrintTenuringDistribution > -XX:+UseConcMarkSweepGC -XX:+UseParNewGC -XX:ParallelGCThreads=2 > -XX:-UseAdaptiveSizePolicy -XX:ConcGCThreads=2 -XX:-UseGCOverheadLimit -XX: > CMSInitiatingOccupancyFraction=75 -XX:NewSize=8g -XX:MaxNewSize=8g > -XX:SurvivorRatio=3 -DnumPartitions=36" "-Dspark.submit.deployMode=cluster" > "-Dspark.speculation=true" "-Dspark.speculation.multiplier=2" > "-Dspark.driver.memory=16g" "-Dspark.speculation.interval=300ms" > "-Dspark.speculation.quantile=0.5" "-Dspark.akka.frameSize=768" > "-Dspark.driver.supervise=false" "-Dspark.executor.cores=6" > "-Dspark.executor.extraJavaOptions=-Xss10240k -XX:+PrintGCDetails > -XX:+PrintGCTimeStamps -XX:+PrintTenuringDistribution > -XX:-UseAdaptiveSizePolicy -XX:+UseParallelGC -XX:+UseParallelOldGC > -XX:ParallelGCThreads=6 -XX:NewSize=22g -XX:MaxNewSize=22g > -XX:SurvivorRatio=2 -XX:+PrintAdaptiveSizePolicy -XX:+PrintGCDateStamps" > "-Dspark.rpc.askTimeout=10" "-Dspark.executor.memory=40g" > "-Dspark.driver.maxResultSize=3g" "-Xss10240k" "-XX:+PrintGCDetails" > "-XX:+PrintGCTimeStamps" "-XX:+PrintTenuringDistribution" > "-XX:+UseConcMarkSweepGC" "-XX:+UseParNewGC" "-XX:ParallelGCThreads=2" > "-XX:-UseAdaptiveSizePolicy" "-XX:ConcGCThreads=2" > "-XX:-UseGCOverheadLimit" "-XX:CMSInitiatingOccupancyFraction=75" > "-XX:NewSize=8g" "-XX:MaxNewSize=8g" "-XX:SurvivorRatio=3" > "-DnumPartitions=36" "org.apache.spark.deploy.worker.DriverWrapper" > "spark://Worker@11.0.0.106:56419" > > > I get following error: > 16/10/04 06:55:05 WARN TaskSetManager: Lost task 8.0 in stage 19.0 (TID > 194, 11.0.0.106): java.lang.IllegalArgumentException: Size exceeds > Integer.MAX_VALUE > at sun.nio.ch.FileChannelImpl.map(FileChannelImpl.java:869) > at org.apache.spark.storage.DiskStore$$anonfun$getBytes$2. > apply(DiskStore.scala:127) > at org.apache.spark.storage.DiskStore$$anonfun$getBytes$2. > apply(DiskStore.scala:115) > at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1250) > at org.apache.spark.storage.DiskStore.getBytes(DiskStore.scala:129) > at org.apache.spark.storage.DiskStore.getBytes(DiskStore.scala:136) > at org.apache.spark.storage.BlockManager.doGetLocal( > BlockManager.scala:503) > at org.apache.spark.storage.BlockManager.getLocal(BlockManager.scala:420) > at org.apache.spark.storage.BlockManager.get(BlockManager.scala:625) > at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:154) > at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:78) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:268) > at org.apache.spark.rdd.ZippedPartitionsRDD2.compute( > ZippedPartitionsRDD.scala:88) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:270) > at org.apache.spark.rdd.MapPartitionsRDD.compute( > MapPartitionsRDD.scala:38) > at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306) > at org.apache.spark.rdd.RDD.iterator(RDD.scala:270) > at org.apache.spark.scheduler.ShuffleMapTask.runTask( > ShuffleMapTask.scala:73) > at org.apache.spark.scheduler.ShuffleMapTask.runTask( > ShuffleMapTask.scala:41) > at org.apache.spark.scheduler.Task.run(Task.scala:89) > at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214) > at java.util.concurrent.ThreadPoolExecutor.runWorker( > ThreadPoolExecutor.java:1142) > at java.util.concurrent.ThreadPoolExecutor$Worker.run( > ThreadPoolExecutor.java:617) > at java.lang.Thread.run(Thread.java:745) > > > I have varied number of partitions from 24 to 48. I still get the same > error. How can this problem be tackled? > > > Thanks, > Samkit > > > > > [1]: https://www.kaggle.com/c/grupo-bimbo-inventory-demand >
