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https://issues.apache.org/jira/browse/SYSTEMML-869?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15504777#comment-15504777
 ] 

Matthias Boehm commented on SYSTEMML-869:
-----------------------------------------

thanks again for catching this [~mwdus...@us.ibm.com] - I just delivered the 
fix. Could you please give it another try?

> Error converting Matrix to Spark DataFrame with MLContext After Subsequent 
> Executions
> -------------------------------------------------------------------------------------
>
>                 Key: SYSTEMML-869
>                 URL: https://issues.apache.org/jira/browse/SYSTEMML-869
>             Project: SystemML
>          Issue Type: Bug
>          Components: APIs
>            Reporter: Mike Dusenberry
>            Assignee: Matthias Boehm
>            Priority: Blocker
>             Fix For: SystemML 0.11
>
>
> Running the LeNet deep learning example notebook with the new {{MLContext}} 
> API in Python results in the below error when converting the resulting 
> {{Matrix}} to a Spark {{DataFrame}} via the {{toDF()}} call.  This only 
> occurs with the large LeNet example, and not for the similar "Softmax 
> Classifier" example that has a smaller model. 
> {code}
> Py4JJavaError: An error occurred while calling o34.asDataFrame.
> : org.apache.hadoop.mapred.InvalidInputException: Input path does not exist: 
> file:/Users/mwdusenb/Documents/Code/systemML/deep_learning/examples/scratch_space/_p85157_9.31.116.142/_t0/temp816_133
>     at 
> org.apache.hadoop.mapred.FileInputFormat.listStatus(FileInputFormat.java:251)
>     at 
> org.apache.hadoop.mapred.SequenceFileInputFormat.listStatus(SequenceFileInputFormat.java:45)
>     at 
> org.apache.hadoop.mapred.FileInputFormat.getSplits(FileInputFormat.java:270)
>     at org.apache.spark.rdd.HadoopRDD.getPartitions(HadoopRDD.scala:199)
>     at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
>     at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
>     at scala.Option.getOrElse(Option.scala:120)
>     at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
>     at 
> org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
>     at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
>     at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
>     at scala.Option.getOrElse(Option.scala:120)
>     at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
>     at 
> org.apache.spark.rdd.MapPartitionsRDD.getPartitions(MapPartitionsRDD.scala:35)
>     at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:239)
>     at org.apache.spark.rdd.RDD$$anonfun$partitions$2.apply(RDD.scala:237)
>     at scala.Option.getOrElse(Option.scala:120)
>     at org.apache.spark.rdd.RDD.partitions(RDD.scala:237)
>     at org.apache.spark.Partitioner$.defaultPartitioner(Partitioner.scala:65)
>     at 
> org.apache.spark.rdd.PairRDDFunctions$$anonfun$groupByKey$3.apply(PairRDDFunctions.scala:642)
>     at 
> org.apache.spark.rdd.PairRDDFunctions$$anonfun$groupByKey$3.apply(PairRDDFunctions.scala:642)
>     at 
> org.apache.spark.rdd.RDDOperationScope$.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.PairRDDFunctions.groupByKey(PairRDDFunctions.scala:641)
>     at org.apache.spark.api.java.JavaPairRDD.groupByKey(JavaPairRDD.scala:538)
>     at 
> org.apache.sysml.runtime.instructions.spark.utils.RDDConverterUtilsExt.binaryBlockToDataFrame(RDDConverterUtilsExt.java:502)
>     at 
> org.apache.sysml.api.mlcontext.MLContextConversionUtil.matrixObjectToDataFrame(MLContextConversionUtil.java:762)
>     at org.apache.sysml.api.mlcontext.Matrix.asDataFrame(Matrix.java:111)
>     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:497)
>     at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
>     at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:381)
>     at py4j.Gateway.invoke(Gateway.java:259)
>     at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
>     at py4j.commands.CallCommand.execute(CallCommand.java:79)
>     at py4j.GatewayConnection.run(GatewayConnection.java:209)
>     at java.lang.Thread.run(Thread.java:745)
> {code}
> To setup, I used the instructions [here | 
> https://github.com/dusenberrymw/systemml-nn/tree/master/examples], running 
> the {{Example - MNIST LeNet.ipynb}} notebook.  Additionally, to speed up the 
> actual training time, I modified [line 84 & 85 of mnist_lenet.dml | 
> https://github.com/dusenberrymw/systemml-nn/blob/master/examples/mnist_lenet.dml#L84]
>  to set the {{epochs = 1}}, and {{iters = 1}}.



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