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Deron Eriksson commented on SYSTEMML-869: ----------------------------------------- Awesome! Thank for you fixing this [~mboehm7]! > 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}}. -- This message was sent by Atlassian JIRA (v6.3.4#6332)