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From Sachin Kamkar <sachinkam...@gmail.com>
Subject Re: Not able to train data
Date Thu, 26 Oct 2017 06:47:51 GMT
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 <vaghawan781@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 <
> abhimanyunagrath@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
>>
>>
>

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