It should be correct, as the user got the exception after 3-4 hours of starting. So looks like something else broke. OOM?
With Regards, Sachin ⚜KTBFFH⚜ On Thu, Oct 26, 2017 at 12:15 PM, Vaghawan Ojha <vaghawan...@gmail.com> wrote: > "Executor failed to connect with master ", are you sure the --master > spark://*.*.*.*:7077 is correct? > > Like the one you copied from the spark master's web ui? sometimes having > that wrong fails to connect with the spark master. > > Thanks > > On Thu, Oct 26, 2017 at 12:02 PM, Abhimanyu Nagrath < > abhimanyunagr...@gmail.com> wrote: > >> I am new to predictionIO . I am using template >> https://github.com/EmergentOrder/template-scala-probabilisti >> c-classifier-batch-lbfgs. >> >> My training dataset count is 1184603 having approx 6500 features. I am >> using ec2 r4.8xlarge system (240 GB RAM, 32 Cores, 200 GB Swap). >> >> >> I tried two ways for training >> >> 1. Command ' >> >> > pio train -- --driver-memory 120G --executor-memory 100G -- conf >> > spark.network.timeout=10000000 >> >> ' >> Its throwing exception after 3-4 hours. >> >> >> Exception in thread "main" org.apache.spark.SparkException: Job >> aborted due to stage failure: Task 0 in stage 1.0 failed 1 times, most >> recent failure: Lost task 0.0 in stage 1.0 (TID 15, localhost, executor >> driver): ExecutorLostFailure (executor driver exited caused by one of the >> running tasks) Reason: Executor heartbeat timed out after 181529 ms >> Driver stacktrace: >> at org.apache.spark.scheduler.DAGScheduler.org >> $apache$spark$scheduler$DAGScheduler$$failJobAn >> dIndependentStages(DAGScheduler.scala:1435) >> at org.apache.spark.scheduler.DAG >> Scheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1423) >> at org.apache.spark.scheduler.DAG >> Scheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1422) >> at scala.collection.mutable.Resiz >> ableArray$class.foreach(ResizableArray.scala:59) >> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer. >> scala:48) >> at org.apache.spark.scheduler.DAG >> Scheduler.abortStage(DAGScheduler.scala:1422) >> at org.apache.spark.scheduler.DAG >> Scheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802) >> at org.apache.spark.scheduler.DAG >> Scheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802) >> at scala.Option.foreach(Option.scala:257) >> at org.apache.spark.scheduler.DAG >> Scheduler.handleTaskSetFailed(DAGScheduler.scala:802) >> at org.apache.spark.scheduler.DAG >> SchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1650) >> at org.apache.spark.scheduler.DAG >> SchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1605) >> at org.apache.spark.scheduler.DAG >> SchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1594) >> at org.apache.spark.util.EventLoo >> p$$anon$1.run(EventLoop.scala:48) >> at org.apache.spark.scheduler.DAG >> Scheduler.runJob(DAGScheduler.scala:628) >> at org.apache.spark.SparkContext. >> runJob(SparkContext.scala:1918) >> at org.apache.spark.SparkContext. >> runJob(SparkContext.scala:1931) >> at org.apache.spark.SparkContext. >> runJob(SparkContext.scala:1944) >> at org.apache.spark.rdd.RDD$$anonfun$take$1.apply(RDD.scala: >> 1353) >> at org.apache.spark.rdd.RDDOperat >> ionScope$.withScope(RDDOperationScope.scala:151) >> at org.apache.spark.rdd.RDDOperat >> ionScope$.withScope(RDDOperationScope.scala:112) >> at org.apache.spark.rdd.RDD.withScope(RDD.scala:362) >> at org.apache.spark.rdd.RDD.take(RDD.scala:1326) >> at org.example.classification.Log >> isticRegressionWithLBFGSAlgorithm.train(LogisticRegressionWi >> thLBFGSAlgorithm.scala:28) >> at org.example.classification.Log >> isticRegressionWithLBFGSAlgorithm.train(LogisticRegressionWi >> thLBFGSAlgorithm.scala:21) >> at org.apache.predictionio.controller.P2LAlgorithm.trainBase( >> P2LAlgorithm.scala:49) >> at org.apache.predictionio.contro >> ller.Engine$$anonfun$18.apply(Engine.scala:692) >> at org.apache.predictionio.contro >> ller.Engine$$anonfun$18.apply(Engine.scala:692) >> at scala.collection.TraversableLike$$anonfun$map$1.apply( >> TraversableLike.scala:234) >> at scala.collection.TraversableLike$$anonfun$map$1.apply( >> TraversableLike.scala:234) >> at scala.collection.immutable.List.foreach(List.scala:381) >> at scala.collection.TraversableLi >> ke$class.map(TraversableLike.scala:234) >> at scala.collection.immutable.List.map(List.scala:285) >> at org.apache.predictionio.controller.Engine$.train(Engine. >> scala:692) >> at org.apache.predictionio.controller.Engine.train(Engine. >> scala:177) >> at org.apache.predictionio.workflow.CoreWorkflow$.runTrain( >> CoreWorkflow.scala:67) >> at org.apache.predictionio.workfl >> ow.CreateWorkflow$.main(CreateWorkflow.scala:250) >> at org.apache.predictionio.workfl >> ow.CreateWorkflow.main(CreateWorkflow.scala) >> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native >> Method) >> at sun.reflect.NativeMethodAccess >> orImpl.invoke(NativeMethodAccessorImpl.java:62) >> at sun.reflect.DelegatingMethodAc >> cessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) >> at java.lang.reflect.Method.invoke(Method.java:498) >> at org.apache.spark.deploy.SparkS >> ubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSub >> mit.scala:738) >> at org.apache.spark.deploy.SparkS >> ubmit$.doRunMain$1(SparkSubmit.scala:187) >> at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit. >> scala:212) >> at org.apache.spark.deploy.SparkS >> ubmit$.main(SparkSubmit.scala:126) >> at org.apache.spark.deploy.SparkS >> ubmit.main(SparkSubmit.scala) >> >> 2. I started spark standalone cluster with 1 master and 3 workers and >> executed the command >> >> > pio train -- --master spark://*.*.*.*:7077 --driver-memory 50G >> > --executor-memory 50G >> >> And after some times getting the error . Executor failed to connect with >> master and training gets stopped. >> >> I have changed the feature count from 6500 - > 500 and still the >> condition is same. So can anyone suggest me am I missing something >> >> and In between training getting continuous warnings like : >> [ >> >> > WARN] [ScannerCallable] Ignore, probably already closed >> >> >> Regards, >> Abhimanyu >> >> >