Spark ALS with Sparse Implicit Dataset - apache-spark

Question

I am trying to run the MovieALS example from Spark with an implicit dataset and am receiving this error:
Got 3856988 ratings from 144250 users on 378937 movies.
Training: 3085522, test: 771466.
15/07/13 10:43:07 WARN BLAS: Failed to load implementation from: com.github.fommil.netlib.NativeSystemBLAS
15/07/13 10:43:07 WARN BLAS: Failed to load implementation from: com.github.fommil.netlib.NativeRefBLAS
15/07/13 10:43:10 WARN TaskSetManager: Lost task 3.0 in stage 29.0 (TID 192, 10.162.45.33): java.lang.AssertionError: assertion failed: lapack.dppsv returned 1.
at scala.Predef$.assert(Predef.scala:179)
at org.apache.spark.ml.recommendation.ALS$CholeskySolver.solve(ALS.scala:386)
at org.apache.spark.ml.recommendation.ALS$$anonfun$org$apache$spark$ml$recommendation$ALS$$computeFactors$1.apply(ALS.scala:1163)
at org.apache.spark.ml.recommendation.ALS$$anonfun$org$apache$spark$ml$recommendation$ALS$$computeFactors$1.apply(ALS.scala:1124)
at org.apache.spark.rdd.PairRDDFunctions$$anonfun$mapValues$1$$anonfun$apply$41$$anonfun$apply$42.apply(PairRDDFunctions.scala:700)
at org.apache.spark.rdd.PairRDDFunctions$$anonfun$mapValues$1$$anonfun$apply$41$$anonfun$apply$42.apply(PairRDDFunctions.scala:700)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at org.apache.spark.storage.MemoryStore.unrollSafely(MemoryStore.scala:277)
at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:171)
at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:78)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:242)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:63)
at org.apache.spark.scheduler.Task.run(Task.scala:70)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
15/07/13 10:43:10 ERROR TaskSetManager: Task 12 in stage 29.0 failed 4 times; aborting job
Exception in thread "main" org.apache.spark.SparkException: Job aborted due to stage failure: Task 12 in stage 29.0 failed 4 times, most recent failure: Lost task 12.3 in stage 29.0 (TID 249, 10.162.45.33): java.lang.AssertionError: assertion failed: lapack.dppsv returned 1.
at scala.Predef$.assert(Predef.scala:179)
at org.apache.spark.ml.recommendation.ALS$CholeskySolver.solve(ALS.scala:386)
at org.apache.spark.ml.recommendation.ALS$$anonfun$org$apache$spark$ml$recommendation$ALS$$computeFactors$1.apply(ALS.scala:1163)
at org.apache.spark.ml.recommendation.ALS$$anonfun$org$apache$spark$ml$recommendation$ALS$$computeFactors$1.apply(ALS.scala:1124)
at org.apache.spark.rdd.PairRDDFunctions$$anonfun$mapValues$1$$anonfun$apply$41$$anonfun$apply$42.apply(PairRDDFunctions.scala:700)
at org.apache.spark.rdd.PairRDDFunctions$$anonfun$mapValues$1$$anonfun$apply$41$$anonfun$apply$42.apply(PairRDDFunctions.scala:700)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at org.apache.spark.storage.MemoryStore.unrollSafely(MemoryStore.scala:277)
at org.apache.spark.CacheManager.putInBlockManager(CacheManager.scala:171)
at org.apache.spark.CacheManager.getOrCompute(CacheManager.scala:78)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:242)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:35)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:277)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:244)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:63)
at org.apache.spark.scheduler.Task.run(Task.scala:70)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:745)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1266)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1257)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1256)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1256)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:730)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:730)
at scala.Option.foreach(Option.scala:236)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:730)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1450)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1411)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
I am unsure if this is due to the sparsity of the dataset. It works fine when it is trained explicitly. However, since the dataset is binary, the error rate for obtained from the explicit ALS model is not accurate.
Would it be possible to help me out?
Thank you, Ben