Possible serialization error when executing Spark job from cluster with 1 master and 2 workers

Question

I am executing Spark job on cluster. While it runs perfectly in a local mode, in the cluster it fails in the following line:

val labelsAndPredictions = testData.map(p => (model.predict(p.features), p.label))

I understand that the issue might be related to the fact that the object

model

is unaccessible for workers. Therefore I tried another approach. I created a serializable object

Utils

with two functions:

object Utils {



  var model: GradientBoostedTreesModel = null



  def loadModel(m: GradientBoostedTreesModel): Unit = {

    model = m

  }



  def makePrediction(features: org.apache.spark.mllib.linalg.Vector): Double = {

    println(model.trees)

    val predictedLabel = model.predict(features)

    println("predictedLabel: " + predictedLabel)

    predictedLabel

  }



}

Then I substituted the prediction as follows:

  model = GradientBoostedTrees.train(trainingData, boostingStrategy)

  Utils.loadModel(model)



  val labelsAndPredictions = testData.map { x =>

    val prediction = Utils.makePrediction(x.features)

    (x.label, prediction)

  }

This is the command that I use to submit the Spark job:

spark-submit --master yarn --deploy-mode cluster --driver-memory 10g --executor-memory 10g --num-executors 2 --conf "spark.executor.extraJavaOptions=-XX:+UseG1GC -XX:+AlwaysPreTouch" --class org.test.Runner s3://mybucket/keys/trainer.jar

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Answer 1

The utils object won't solve the problem because the model is initialised static to the driver, not workers.

The last time I checked (1.6.x) models are indeed not serializable.

What you can do is:

model.predict(testData.map(_.features))

But that won't provide you a nice way to assess the model without labels.

Or

testData.toLocalIterator.map { x => (x.label, model.predict(x.features)) }

toLocalIterator iterates the data in each partition, but on the driver rather than workers, giving you access to your model. Unfortunately not parallelised though.

Possible serialization error when executing Spark job from cluster with 1 master and 2 workers

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