I am new to predictionIO . I am using template https://github.com/EmergentOrder/template-scala-probabilistic-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
- 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$$failJobAndIndependentStages(DAGScheduler.scala:1435)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1423)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1422)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1422)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:802)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1650)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1605)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1594)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.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$$anonfun$take$1.apply(RDD.scala:1353)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
at org.apache.spark.rdd.RDD.take(RDD.scala:1326)
at org.example.classification.LogisticRegressionWithLBFGSAlgorithm.train(LogisticRegressionWithLBFGSAlgorithm.scala:28)
at org.example.classification.LogisticRegressionWithLBFGSAlgorithm.train(LogisticRegressionWithLBFGSAlgorithm.scala:21)
at org.apache.predictionio.controller.P2LAlgorithm.trainBase(P2LAlgorithm.scala:49)
at org.apache.predictionio.controller.Engine$$anonfun$18.apply(Engine.scala:692)
at org.apache.predictionio.controller.Engine$$anonfun$18.apply(Engine.scala:692)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.immutable.List.foreach(List.scala:381)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.immutable.List.map(List.scala:285)
at org.apache.predictionio.controller.Engine$.train(Engine.scala:692)
at org.apache.predictionio.controller.Engine.train(Engine.scala:177)
at org.apache.predictionio.workflow.CoreWorkflow$.runTrain(CoreWorkflow.scala:67)
at org.apache.predictionio.workflow.CreateWorkflow$.main(CreateWorkflow.scala:250)
at org.apache.predictionio.workflow.CreateWorkflow.main(CreateWorkflow.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:498)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:738)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:187)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:212)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:126)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
- I started Spark standalone cluster with 1 master and 3 workers and executed the following command:
pio train -- --master spark://...:7077 --driver-memory 50G --executor-memory 50G
And after sometime I am getting the following error:
Executor failed to connect with master and training gets stopped.
I changed the feature count from 6500 - > 500 and still the condition is same. So can anyone suggest me am I missing something?
Meanwhile, during training, the program gets continuous warnings like :
[WARN] [ScannerCallable] Ignore, probably already closed
