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How to save Pyspark model from notebook to managed folder

Hardy75
Level 1
How to save Pyspark model from notebook to managed folder

Hi, 

 

I'm trying to save pyspark model model.save("/opt/dataiku/design/managed_folders/PROJECT_TEST/9KeBcUKy/ML_SAVED")

from  notebook to managed folder but I'm getting the following error:

 

Py4JJavaError: An error occurred while calling o2981.save.
: org.apache.spark.SparkException: Job aborted.
	at org.apache.spark.internal.io.SparkHadoopWriter$.write(SparkHadoopWriter.scala:105)
	at org.apache.spark.rdd.PairRDDFunctions.$anonfun$saveAsHadoopDataset$1(PairRDDFunctions.scala:1090)
	at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
	at org.apache.spark.rdd.RDD.withScope(RDD.scala:414)
	at org.apache.spark.rdd.PairRDDFunctions.saveAsHadoopDataset(PairRDDFunctions.scala:1088)
	at org.apache.spark.rdd.PairRDDFunctions.$anonfun$saveAsHadoopFile$4(PairRDDFunctions.scala:1061)
	at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
	at org.apache.spark.rdd.RDD.withScope(RDD.scala:414)
	at org.apache.spark.rdd.PairRDDFunctions.saveAsHadoopFile(PairRDDFunctions.scala:1026)
	at org.apache.spark.rdd.PairRDDFunctions.$anonfun$saveAsHadoopFile$3(PairRDDFunctions.scala:1008)
	at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
	at org.apache.spark.rdd.RDD.withScope(RDD.scala:414)
	at org.apache.spark.rdd.PairRDDFunctions.saveAsHadoopFile(PairRDDFunctions.scala:1007)
	at org.apache.spark.rdd.PairRDDFunctions.$anonfun$saveAsHadoopFile$2(PairRDDFunctions.scala:964)
	at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
	at org.apache.spark.rdd.RDD.withScope(RDD.scala:414)
	at org.apache.spark.rdd.PairRDDFunctions.saveAsHadoopFile(PairRDDFunctions.scala:962)
	at org.apache.spark.rdd.RDD.$anonfun$saveAsTextFile$2(RDD.scala:1578)
	at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
	at org.apache.spark.rdd.RDD.withScope(RDD.scala:414)
	at org.apache.spark.rdd.RDD.saveAsTextFile(RDD.scala:1578)
	at org.apache.spark.rdd.RDD.$anonfun$saveAsTextFile$1(RDD.scala:1564)
	at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
	at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
	at org.apache.spark.rdd.RDD.withScope(RDD.scala:414)
	at org.apache.spark.rdd.RDD.saveAsTextFile(RDD.scala:1564)
	at org.apache.spark.ml.util.DefaultParamsWriter$.saveMetadata(ReadWrite.scala:413)
	at org.apache.spark.ml.Pipeline$SharedReadWrite$.$anonfun$saveImpl$1(Pipeline.scala:250)
	at org.apache.spark.ml.Pipeline$SharedReadWrite$.$anonfun$saveImpl$1$adapted(Pipeline.scala:247)
	at org.apache.spark.ml.util.Instrumentation$.$anonfun$instrumented$1(Instrumentation.scala:191)
	at scala.util.Try$.apply(Try.scala:213)
	at org.apache.spark.ml.util.Instrumentation$.instrumented(Instrumentation.scala:191)
	at org.apache.spark.ml.Pipeline$SharedReadWrite$.saveImpl(Pipeline.scala:247)
	at org.apache.spark.ml.PipelineModel$PipelineModelWriter.saveImpl(Pipeline.scala:346)
	at org.apache.spark.ml.util.MLWriter.save(ReadWrite.scala:168)
	at org.apache.spark.ml.PipelineModel$PipelineModelWriter.super$save(Pipeline.scala:344)
	at org.apache.spark.ml.PipelineModel$PipelineModelWriter.$anonfun$save$4(Pipeline.scala:344)
	at org.apache.spark.ml.MLEvents.withSaveInstanceEvent(events.scala:174)
	at org.apache.spark.ml.MLEvents.withSaveInstanceEvent$(events.scala:169)
	at org.apache.spark.ml.util.Instrumentation.withSaveInstanceEvent(Instrumentation.scala:42)
	at org.apache.spark.ml.PipelineModel$PipelineModelWriter.$anonfun$save$3(Pipeline.scala:344)
	at org.apache.spark.ml.PipelineModel$PipelineModelWriter.$anonfun$save$3$adapted(Pipeline.scala:344)
	at org.apache.spark.ml.util.Instrumentation$.$anonfun$instrumented$1(Instrumentation.scala:191)
	at scala.util.Try$.apply(Try.scala:213)
	at org.apache.spark.ml.util.Instrumentation$.instrumented(Instrumentation.scala:191)
	at org.apache.spark.ml.PipelineModel$PipelineModelWriter.save(Pipeline.scala:344)
	at sun.reflect.GeneratedMethodAccessor309.invoke(Unknown Source)
	at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
	at java.lang.reflect.Method.invoke(Method.java:498)
	at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
	at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
	at py4j.Gateway.invoke(Gateway.java:282)
	at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
	at py4j.commands.CallCommand.execute(CallCommand.java:79)
	at py4j.GatewayConnection.run(GatewayConnection.java:238)
	at java.lang.Thread.run(Thread.java:750)
Caused by: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 36.0 failed 4 times, most recent failure: Lost task 0.3 in stage 36.0 (TID 72) (10.252.0.243 executor 3): java.io.IOException: Mkdirs failed to create file:/opt/dataiku/design/managed_folders/PROJECT_TEST/9KeBcUKy/ML_SAVED/metadata/_temporary/0/_temporary/attempt_202212132333134281100042615345471_0095_m_000000_3 (exists=false, cwd=file:/home/dataiku) 

 

 

Thank you for any feedback or help that you can share!

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1 Reply
JordanB
Dataiker

Hi @Hardy75,

Assuming you have a locally managed folder, you can simply create an empty managed folder (i.e. the output of your recipe). Then, you just need the full path for the managed folder to pass that path to the keras model.save() function. 

# this is a folder with a server filesystem connection 
folder = dataiku.Folder('model_folder')

# this will return the full path of the managed folder
managed_folder_path = folder.get_path()

# now you can perform a model.save() function on the managed_folder_path
model.save(managed_folder_path + "/my_model.h5")
 
Note that the keras model save() functions saves to a local filesystem, which is why this assumes that your managed folder is pointing to a local filesystem.
 
Thanks!
Jordan
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