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https://issues.apache.org/jira/browse/SYSTEMML-1370?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Glenn Weidner updated SYSTEMML-1370:
------------------------------------
Fix Version/s: (was: SystemML 1.0)
SystemML 0.14
> Py4JError: An error occurred while calling
> z:org.apache.sysml.runtime.instructions.spark.utils.RDDConverterUtilsExt.convertPy4JArrayToMB.
> -----------------------------------------------------------------------------------------------------------------------------------------
>
> Key: SYSTEMML-1370
> URL: https://issues.apache.org/jira/browse/SYSTEMML-1370
> Project: SystemML
> Issue Type: Bug
> Components: APIs
> Affects Versions: Not Applicable
> Environment: pyspark with local Spark 2.1
> Reporter: Berthold Reinwald
> Fix For: SystemML 0.14
>
>
> Do we have undocumented limits for RDDConverterUtilsExt.convertPy4JArrayToMB?
> Below simple script works for 23100 rows, while 46900 fails. This is how to
> easily and consistently reproduce.
> START:
> $pyspark --master local --jars $SYSTEMML_HOME/SystemML.jar --driver-memory 8G
> --executor-memory 2G
> PYTHON SCRIPT:
> from systemml import MLContext, dml
> import pandas as pd
> sc.version
> ml = MLContext(sc)
> print "Spark Version:", sc.version
> print "SystemML Version:", ml.version()
> print "SystemML Built-Time:", ml.buildTime()
> # !! number of rows 23100 works, while 46900 fails
> nr = 46900
> X_pd = pd.DataFrame(range(1, (nr*784)+1,1),dtype=float).values.reshape(nr,784)
> script ="""
> write(X, $Xfile, format="csv")
> """
> prog = dml(script).input(X=X_pd).input(**{"$Xfile":"/tmp/X_pd.csv"})
> ml.execute(prog)
> OUTPUT:
> Spark Version: 2.1.0
> SystemML Version: 0.14.0-incubating-SNAPSHOT
> SystemML Built-Time: 2017-03-03 07:33:40 UTC
> ---------------------------------------------------------------------------
> Py4JError Traceback (most recent call last)
> .......
> Py4JError: An error occurred while calling
> z:org.apache.sysml.runtime.instructions.spark.utils.RDDConverterUtilsExt.convertPy4JArrayToMB.
> Trace:
> java.lang.NegativeArraySizeException
> at py4j.Base64.decode(Base64.java:321)
> at py4j.Protocol.getBytes(Protocol.java:173)
> at py4j.Protocol.getObject(Protocol.java:294)
> at py4j.commands.AbstractCommand.getArguments(AbstractCommand.java:82)
> at py4j.commands.CallCommand.execute(CallCommand.java:77)
> at py4j.GatewayConnection.run(GatewayConnection.java:214)
> at java.lang.Thread.run(Thread.java:745)
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