title: spark-jobserver and mllib issue running jobs
body: <p>I'm experimenting with the <code>JobServer</code> and would like to use it in our production environment.</p>
  
  <p>I want to use <code>mllib</code> and <code>spark-jobserver</code> together, but i got a error (at the spark-jobserver, when a job is sended).</p>
  
  <pre><code>job-server[ERROR] Uncaught error from thread [JobServer-akka.actor.default-dispatcher-3] shutting down JVM since 'akka.jvm-exit-on-fatal-error' is enabled for ActorSystem[JobServer]
  job-server[ERROR] java.lang.NoClassDefFoundError: org/apache/spark/mllib/stat/Statistics$
  job-server[ERROR]   at SparkCorrelation$.getCorrelation(SparkCorrelation.scala:50)
  job-server[ERROR]   at SparkCorrelation$.runJob(SparkCorrelation.scala:28)
  job-server[ERROR]   at SparkCorrelation$.runJob(SparkCorrelation.scala:11)
  job-server[ERROR]   at spark.jobserver.JobManagerActor$$anonfun$spark$jobserver$JobManagerActor$$getJobFuture$4.apply(JobManagerActor.scala:234)
  </code></pre>
  
  <p>I am using <code>spark-jobserver 0.5.0</code> and <code>spark 1.2</code>.</p>
  
  <p>any idea about it?</p>
  
  <p>Code:</p>
  
  <pre><code>def getCorrelation(sc: SparkContext):Double={
          val pathFile = "hdfs://localhost:9000/user/hduser/correlacion.csv"
          val fileData = getFileData(sc,pathFile)
          val colX = getDoubleColumn(fileData,1)
          val colY = getDoubleColumn(fileData,2)
          Statistics.corr(colX,colY,"pearson")
      }
  
  override def runJob(sc: SparkContext, config: Config): Any = {/*
      val dd = sc.parallelize(config.getString("input.string").split(" ").toSeq)
      dd.map((_, 1)).reduceByKey(_ + _).collect().toMap*/
      getCorrelation(sc)
    }
  </code></pre>