xgboost: [jvm-packages] Persist CrossValidator model with xgboost4j-spark error

Environment info

Operating System: redhat 6.5（with spark-2.1.0） Compiler:

Package: jvm, xgboost4j-spark lastest xgboost version used

I want to save CrossValidator model ，but i got a error

java.lang.UnsupportedOperationException: Pipeline write will fail on this Pipeline because it contains a stage which does not implement Writable. Non-Writable stage: XGBoostEstimator_88624dc1e519 of type class ml.dmlc.xgboost4j.scala.spark.XGBoostEstimator
  at org.apache.spark.ml.Pipeline$SharedReadWrite$$anonfun$validateStages$1.apply(Pipeline.scala:231)
  at org.apache.spark.ml.Pipeline$SharedReadWrite$$anonfun$validateStages$1.apply(Pipeline.scala:228)
  at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
  at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
  at org.apache.spark.ml.Pipeline$SharedReadWrite$.validateStages(Pipeline.scala:228)
  at org.apache.spark.ml.Pipeline$PipelineWriter.<init>(Pipeline.scala:202)
  at org.apache.spark.ml.Pipeline.write(Pipeline.scala:188)
  at org.apache.spark.ml.util.MLWritable$class.save(ReadWrite.scala:154)
  at org.apache.spark.ml.Pipeline.save(Pipeline.scala:96)
  at org.apache.spark.ml.tuning.ValidatorParams$.saveImpl(ValidatorParams.scala:148)
  at org.apache.spark.ml.tuning.CrossValidatorModel$CrossValidatorModelWriter.saveImpl(CrossValidator.scala:256)
  at org.apache.spark.ml.util.MLWriter.save(ReadWrite.scala:111)
  ... 50 elided

my code :

import org.apache.spark.ml.Pipeline
import ml.dmlc.xgboost4j.scala.spark.XGBoostEstimator
import org.apache.spark.ml.evaluation._
import org.apache.spark.ml.feature.{HashingTF, Tokenizer}
import org.apache.spark.ml.linalg.Vector
import org.apache.spark.ml.tuning.{CrossValidator, ParamGridBuilder}
import org.apache.spark.ml.feature.VectorAssembler
import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
import org.apache.spark.sql.Row
import scala.collection.mutable
import scala.util.Random

// xgboost parameters
def get_param(): mutable.HashMap[String, Any] = {
  val params = new mutable.HashMap[String, Any]()
  params += "eta" -> 0.1
  params += "max_depth" -> 4
  params += "min_child_weight" -> 4
  params += "num_rounds" -> 20
  params += "silent" -> 1
  params += "objective" -> "binary:logistic"
  params += "booster" -> "gbtree"
  params += "gamma" -> 0.0
  params += "colsample_bylevel" -> 1

  return params
}

val r = new Random(0)

val training = spark.createDataFrame(
  Seq.fill(10000)(r.nextInt(2)).map(i => (i, i))
).toDF("feature", "label")

val test = spark.createDataFrame(
  Seq.fill(10000)(r.nextInt(2)).map(i => (i, i))
).toDF("feature", "label")


// create pipeline
val assembler = new VectorAssembler()
  .setInputCols(Array("feature"))
  .setOutputCol("features")

val xgb = new XGBoostEstimator(get_param().toMap)
  .setFeaturesCol("features")

val pipeline = new Pipeline()
  .setStages(Array(assembler, xgb))


// grid
val paramGrid = new ParamGridBuilder()
//  .addGrid(hashingTF.numFeatures, Array(10, 100, 1000))
  .addGrid(xgb.round, Array(10, 30, 50,100))
 .addGrid(xgb.maxDepth,  Array(5, 6, 8,10))
 .addGrid(xgb.minChildWeight, Array(0.5, 0.7, 1.0))
  .build()
// cv
// val evaluator = new BinaryClassificationEvaluator().setRawPredictionCol("probabilities")
val evaluator = new RegressionEvaluator().setLabelCol("label")
val cv = new CrossValidator()
  .setEstimator(pipeline)
   .setEvaluator(new RegressionEvaluator()
      .setLabelCol("label")
      .setPredictionCol("prediction")
      .setMetricName("rmse")
      )
  .setEstimatorParamMaps(paramGrid)
  .setNumFolds(5)


// Run cross-validation, and choose the best set of parameters.
val cvModel = cv.fit(training)
cvModel.write.overwrite.save("/tmp/xgbModel")