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From "Ulanov, Alexander" <alexander.ula...@hp.com>
Subject RE: Loading previously serialized object to Spark
Date Mon, 09 Mar 2015 19:25:34 GMT
Thanks so much! It works! Is it the standard way for Mllib models to be serialized?

Btw. The example I pasted below works if one implements a TestSuite with MLlibTestSparkContext.

-----Original Message-----
From: Xiangrui Meng [mailto:mengxr@gmail.com] 
Sent: Monday, March 09, 2015 12:10 PM
To: Ulanov, Alexander
Cc: Akhil Das; dev
Subject: Re: Loading previously serialized object to Spark

Could you try `sc.objectFile` instead?

sc.parallelize(Seq(model), 1).saveAsObjectFile("path") val sameModel = sc.objectFile[NaiveBayesModel]("path").first()

-Xiangrui

On Mon, Mar 9, 2015 at 11:52 AM, Ulanov, Alexander <alexander.ulanov@hp.com> wrote:
> Just tried, the same happens if I use the internal Spark serializer:
> val serializer = SparkEnv.get.closureSerializer.newInstance
>
>
> -----Original Message-----
> From: Ulanov, Alexander
> Sent: Monday, March 09, 2015 10:37 AM
> To: Akhil Das
> Cc: dev
> Subject: RE: Loading previously serialized object to Spark
>
> Below is the code with standard MLlib class. Apparently this issue can happen in the
same Spark instance.
>
> import java.io._
>
> import org.apache.spark.mllib.classification.NaiveBayes
> import org.apache.spark.mllib.classification.NaiveBayesModel
> import org.apache.spark.mllib.util.MLUtils
>
> val data = MLUtils.loadLibSVMFile(sc, 
> "hdfs://myserver:9000/data/mnist.scale")
> val nb = NaiveBayes.train(data)
> // RDD map works fine
> val predictionAndLabels = data.map( lp => 
> (nb.classifierModel.predict(lp.features), lp.label))
>
> // serialize the model to file and immediately load it val oos = new 
> ObjectOutputStream(new FileOutputStream("/home/myuser/nb.bin"))
> oos.writeObject(nb)
> oos.close
> val ois = new ObjectInputStream(new 
> FileInputStream("/home/myuser/nb.bin"))
> val nbSerialized = ois.readObject.asInstanceOf[NaiveBayesModel]
> ois.close
> // RDD map fails
> val predictionAndLabels = data.map( lp => 
> (nbSerialized.predict(lp.features), lp.label))
> org.apache.spark.SparkException: Task not serializable
>         at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:166)
>         at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:158)
>         at org.apache.spark.SparkContext.clean(SparkContext.scala:1453)
>         at org.apache.spark.rdd.RDD.map(RDD.scala:273)
>
>
> From: Akhil Das [mailto:akhil@sigmoidanalytics.com]
> Sent: Sunday, March 08, 2015 3:17 AM
> To: Ulanov, Alexander
> Cc: dev
> Subject: Re: Loading previously serialized object to Spark
>
> Can you paste the complete code?
>
> Thanks
> Best Regards
>
> On Sat, Mar 7, 2015 at 2:25 AM, Ulanov, Alexander <alexander.ulanov@hp.com<mailto:alexander.ulanov@hp.com>>
wrote:
> Hi,
>
> I've implemented class MyClass in MLlib that does some operation on LabeledPoint. MyClass
extends serializable, so I can map this operation on data of RDD[LabeledPoints], such as data.map(lp
=> MyClass.operate(lp)). I write this class in file with ObjectOutputStream.writeObject.
Then I stop and restart Spark. I load this class from file with ObjectInputStream.readObject.asInstanceOf[MyClass].
When I try to map the same operation of this class to RDD, Spark throws not serializable exception:
> org.apache.spark.SparkException: Task not serializable
>         at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:166)
>         at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:158)
>         at org.apache.spark.SparkContext.clean(SparkContext.scala:1453)
>         at org.apache.spark.rdd.RDD.map(RDD.scala:273)
>
> Could you suggest why it throws this exception while MyClass is serializable by definition?
>
> Best regards, Alexander
>
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