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Posted to issues@systemml.apache.org by "Mike Dusenberry (JIRA)" <ji...@apache.org> on 2016/09/11 21:40:21 UTC
[jira] [Resolved] (SYSTEMML-869) Error converting Matrix to Spark
DataFrame with MLContext After Subsequent Executions
[ https://issues.apache.org/jira/browse/SYSTEMML-869?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Mike Dusenberry resolved SYSTEMML-869.
--------------------------------------
Resolution: Fixed
Fix Version/s: SystemML 0.11
> 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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