Thanks for your answer. *You could explicitly do *
*pio train -- --master spark://localhost:7077 --driver-memory 16G --executor-memory 24G * *and change the spark master url and the memories configuration. And see if that works. * Yes that is the command I use to launch the train, except I am on a cluster, so Spark is not local. Here is mine: pio train -- --master spark://master:7077 --driver-memory 4g --executor-memory 10g The train works with different datasets, it also works with this dataset when I skip the event type *view*. So my guess is that there is something about this event type, either in the data but the data looks fine to me, or maybe there is a problem when I use more than two types of event (this is the first time I have more than two, however I can't believe that the problem is related the a number of event types). The spelling is the same in the event sent to the eventserver ( *view *) and in the engine.json ( *view *). I am reading the code to figure out where this error comes from. 2017-06-07 10:17 GMT+02:00 Vaghawan Ojha <vaghawan...@gmail.com>: > You could explicitly do > > pio train -- --master spark://localhost:7077 --driver-memory 16G > --executor-memory 24G > > and change the spark master url and the memories configuration. And see if > that works. > > Thanks > > On Wed, Jun 7, 2017 at 1:55 PM, Bruno LEBON <b.le...@redfakir.fr> wrote: > >> Hi, >> >> Using UR with PIO 0.10 I am trying to train my dataset. In return I get >> the following error: >> >> *...* >> *[INFO] [DataSource] Received events List(facet, view, search)* >> *[INFO] [DataSource] Number of events List(5, 4, 6)* >> *[INFO] [Engine$] org.template.TrainingData does not support data sanity >> check. Skipping check.* >> *[INFO] [Engine$] org.template.PreparedData does not support data sanity >> check. Skipping check.* >> *[INFO] [URAlgorithm] Actions read now creating correlators* >> *[WARN] [TaskSetManager] Lost task 0.0 in stage 56.0 (TID 50, >> ip-172-31-40-139.eu-west-1.com >> <http://ip-172-31-40-139.eu-west-1.com>pute.internal): >> java.lang.NegativeArraySizeException* >> * at >> org.apache.mahout.math.DenseVector.<init>(DenseVector.java:57)* >> * at >> org.apache.mahout.sparkbindings.SparkEngine$$anonfun$5.apply(SparkEngine.scala:73)* >> * at >> org.apache.mahout.sparkbindings.SparkEngine$$anonfun$5.apply(SparkEngine.scala:72)* >> * at >> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)* >> * at >> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)* >> * at >> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)* >> * at >> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)* >> * at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)* >> * at >> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)* >> * at org.apache.spark.scheduler.Task.run(Task.scala:89)* >> * at >> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:227)* >> * 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:748)* >> >> *[ERROR] [TaskSetManager] Task 0 in stage 56.0 failed 4 times; aborting >> job* >> *Exception in thread "main" org.apache.spark.SparkException: Job aborted >> due to stage failure: Task 0 in stage 56.0 failed 4 times, most recent >> failure: Lost task 0.3 in stage 56.0 (TID 56, >> ip-172-1-1-1.eu-west-1.compute.internal): >> java.lang.NegativeArraySizeException* >> * at >> org.apache.mahout.math.DenseVector.<init>(DenseVector.java:57)* >> * at >> org.apache.mahout.sparkbindings.SparkEngine$$anonfun$5.apply(SparkEngine.scala:73)* >> * at >> org.apache.mahout.sparkbindings.SparkEngine$$anonfun$5.apply(SparkEngine.scala:72)* >> * at >> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)* >> * at >> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)* >> * at >> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)* >> * at >> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)* >> * at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)* >> * at >> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)* >> * at org.apache.spark.scheduler.Task.run(Task.scala:89)* >> * at >> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:227)* >> * at >> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)* >> * at >> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)* >> * at java.lang.Thread.run(Thread.java:748)* >> >> *Driver stacktrace:* >> * at org.apache.spark.scheduler.DAGScheduler.org >> <http://org.apache.spark.scheduler.DAGScheduler.org>$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431)* >> * at >> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)* >> * at >> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)* >> * at >> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)* >> * at >> scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)* >> * at >> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)* >> * at >> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)* >> * at >> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)* >> * at scala.Option.foreach(Option.scala:236)* >> * at >> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)* >> * at >> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)* >> * at >> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)* >> * at >> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)* >> * at >> org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)* >> * at >> org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)* >> * at org.apache.spark.SparkContext.runJob(SparkContext.scala:1832)* >> * at org.apache.spark.SparkContext.runJob(SparkContext.scala:1952)* >> * at >> org.apache.spark.rdd.RDD$$anonfun$reduce$1.apply(RDD.scala:1025)* >> * at >> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)* >> * at >> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:111)* >> * at org.apache.spark.rdd.RDD.withScope(RDD.scala:316)* >> * at org.apache.spark.rdd.RDD.reduce(RDD.scala:1007)* >> * at >> org.apache.mahout.sparkbindings.SparkEngine$.numNonZeroElementsPerColumn(SparkEngine.scala:81)* >> * at >> org.apache.mahout.math.drm.CheckpointedOps.numNonZeroElementsPerColumn(CheckpointedOps.scala:36)* >> * at org.apache.mahout.math.cf >> <http://org.apache.mahout.math.cf>.SimilarityAnalysis$.sampleDownAndBinarize(SimilarityAnalysis.scala:397)* >> * at org.apache.mahout.math.cf >> <http://org.apache.mahout.math.cf>.SimilarityAnalysis$$anonfun$cooccurrences$1.apply(SimilarityAnalysis.scala:101)* >> * at org.apache.mahout.math.cf >> <http://org.apache.mahout.math.cf>.SimilarityAnalysis$$anonfun$cooccurrences$1.apply(SimilarityAnalysis.scala:95)* >> * at >> scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)* >> * at >> scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)* >> * at org.apache.mahout.math.cf >> <http://org.apache.mahout.math.cf>.SimilarityAnalysis$.cooccurrences(SimilarityAnalysis.scala:95)* >> * at org.apache.mahout.math.cf >> <http://org.apache.mahout.math.cf>.SimilarityAnalysis$.cooccurrencesIDSs(SimilarityAnalysis.scala:147)* >> * at org.template.URAlgorithm.calcAll(URAlgorithm.scala:280)* >> * at org.template.URAlgorithm.train(URAlgorithm.scala:251)* >> * at org.template.URAlgorithm.train(URAlgorithm.scala:169)* >> * at >> org.apache.predictionio.controller.P2LAlgorithm.trainBase(P2LAlgorithm.scala:49)* >> * at >> org.apache.predictionio.controller.Engine$$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:244)* >> * at >> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)* >> * at scala.collection.immutable.List.foreach(List.scala:318)* >> * at >> scala.collection.TraversableLike$class.map(TraversableLike.scala:244)* >> * at >> scala.collection.AbstractTraversable.map(Traversable.scala:105)* >> * at >> org.apache.predictionio.controller.Engine$.train(Engine.scala:692)* >> * at >> org.apache.predictionio.controller.Engine.train(Engine.scala:177)* >> * at >> org.apache.predictionio.workflow.CoreWorkflow$.runTrain(CoreWorkflow.scala:67)* >> * at >> org.apache.predictionio.workflow.CreateWorkflow$.main(CreateWorkflow.scala:250)* >> * at >> org.apache.predictionio.workflow.CreateWorkflow.main(CreateWorkflow.scala)* >> * at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)* >> * at >> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)* >> * at >> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)* >> * at java.lang.reflect.Method.invoke(Method.java:498)* >> * at >> org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731)* >> * at >> org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)* >> * at >> org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)* >> * at >> org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)* >> * at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)* >> *Caused by: java.lang.NegativeArraySizeException* >> * at >> org.apache.mahout.math.DenseVector.<init>(DenseVector.java:57)* >> * at >> org.apache.mahout.sparkbindings.SparkEngine$$anonfun$5.apply(SparkEngine.scala:73)* >> * at >> org.apache.mahout.sparkbindings.SparkEngine$$anonfun$5.apply(SparkEngine.scala:72)* >> * at >> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)* >> * at >> org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$20.apply(RDD.scala:710)* >> * at >> org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)* >> * at >> org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:306)* >> * at org.apache.spark.rdd.RDD.iterator(RDD.scala:270)* >> * at >> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)* >> * at org.apache.spark.scheduler.Task.run(Task.scala:89)* >> * at >> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:227)* >> * 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:748)* >> >> >> Now usually this message NegativeArraySizeException tells me that one of >> the events defined in engine.json doesn't exist in my dataset. However this >> is not the case here, my three events are present in my dataset. Here the >> proves: >> http://x.x.x.x:7070/events.json?accessKey=df8ef7dd-0165-4b6f >> -a008-d1550adbb3df&startTime=2017-06-2T0:0:00.321Z&limit=1&event=facet >> >> [{"eventId":"AYDE4TYMjU2dFGWVAYyUYwAAAVx5_afdpSyQHw_eNT0","event":"facet","entityType":"user","entityId":"92ec6a38-9fee-4c99-92a5-46677ad9ca48","targetEntityType":"item","targetEntityId":"alfa-romeo-marque","properties":{},"eventTime":"2017-06-05T20:41:25.725Z","creationTime":"2017-06-05T20:41:25.725Z"}] >> >> http://x.x.x.x:7070/events.json?accessKey=df8ef7dd-0165-4b6f-a008-d1550adbb3df&startTime=2017-06-2T0:0:00.321Z&limit=1&event=view >> >> [{"eventId":"IjuMNR7h40l_sylo-uqEsAAAAVxoIcPqnumP2B_qWAk","event":"view","entityType":"user","entityId":"bbc5bd25-b1ac-41e0-b771-43fe65a8827e","targetEntityType":"item","targetEntityId":"citroen-marque","properties":{},"eventTime":"2017-06-02T09:27:42.314Z","creationTime":"2017-06-02T09:27:42.314Z"}] >> >> http://x.x.x.x:7070/events.json?accessKey=df8ef7dd-0165-4b6f-a008-d1550adbb3df&startTime=2017-06-2T0:0:00.321Z&limit=1&event=search >> >> [{"eventId":"AI6NF05NJa3fP2bRpKUxAwAAAVxymnYYjm6nNt3TsGY","event":"search","entityType":"user","entityId":"b2c77901-0824-4583-9999-3cd56c1f34c9","targetEntityType":"item","targetEntityId":"peugeot-marque","properties":{},"eventTime":"2017-06-04T10:15:44.408Z","creationTime":"2017-06-04T10:15:44.408Z"}] >> >> >> I selected only one event per type but there are more. >> >> >> If I keep only the event types *facet *and *search*, then it works, the >> train succeeds and I have my model. However as soon as I add the event type >> *view*, it fails. I tried putting *view *as a primary event and it doesnt >> change anything. Not sure why it would change anything but I tried anyway. >> >> >> Here is my engine.json: >> >> *{ >> "comment":"", >> "id": "car", >> "description": "settings", >> "engineFactory": "org.template.RecommendationEngine", >> "datasource": { >> "params" : { >> "name": "sample-handmade-data.txt", >> "appName": "piourcar", >> "eventNames": ["facet","view","search"] >> } >> }, >> "sparkConf": { >> "spark.serializer": "org.apache.spark.serializer.KryoSerializer", >> "spark.kryo.registrator": "org.apache.mahout.sparkbindings.io >> <http://sparkbindings.io>.MahoutKryoRegistrator", >> "spark.kryo.referenceTracking": "false", >> "spark.kryoserializer.buffer": "300m", >> "es.index.auto.create": "true", >> "es.nodes":"espionode1:9200,espionode2:9200,espionode3:9200" >> }, >> "algorithms": [ >> { >> "name": "ur", >> "params": { >> "appName": "piourcar", >> "indexName": "urindex_car", >> "typeName": "items", >> "eventNames": ["facet","view","search"], >> "blacklistEvents": [], >> "maxEventsPerEventType": 50000, >> "maxCorrelatorsPerEventType": 100, >> "maxQueryEvents": 10, >> "num": 5, >> "userBias": 2, >> "returnSelf": true >> } >> } >> ] >> }* >> >> Thanks in advance for your help, regards, >> Bruno >> >> >> >> >> >> >
