systemml-issues mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "Deron Eriksson (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (SYSTEMML-869) Error converting Matrix to Spark DataFrame with MLContext After Subsequent Executions
Date Mon, 12 Sep 2016 17:05:21 GMT

    [ https://issues.apache.org/jira/browse/SYSTEMML-869?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15484646#comment-15484646
] 

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)

Mime
View raw message