diff --git a/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala b/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala
index 7a35b85ec..4bbef3cde 100644
--- a/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala
+++ b/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala
@@ -17,6 +17,7 @@
 package ml.dmlc.xgboost4j.scala.spark
 
 import scala.collection.mutable
+import scala.collection.JavaConverters._
 
 import org.apache.commons.logging.LogFactory
 import org.apache.spark.TaskContext
@@ -38,13 +39,13 @@ object XGBoost extends Serializable {
   private[spark] def buildDistributedBoosters(
       trainingData: RDD[LabeledPoint],
       xgBoostConfMap: Map[String, AnyRef],
+      rabitEnv: mutable.Map[String, String],
       numWorkers: Int, round: Int, obj: ObjectiveTrait, eval: EvalTrait): RDD[Booster] = {
     import DataUtils._
     trainingData.repartition(numWorkers).mapPartitions {
       trainingSamples =>
-        Rabit.init(new java.util.HashMap[String, String]() {
-          put("DMLC_TASK_ID", TaskContext.getPartitionId().toString)
-        })
+        rabitEnv.put("DMLC_TASK_ID", TaskContext.getPartitionId().toString)
+        Rabit.init(rabitEnv.asJava)
         val dMatrix = new DMatrix(new JDMatrix(trainingSamples, null))
         val booster = SXGBoost.train(xgBoostConfMap, dMatrix, round,
           watches = new mutable.HashMap[String, DMatrix]{put("train", dMatrix)}.toMap, obj, eval)
@@ -59,7 +60,8 @@ object XGBoost extends Serializable {
     val sc = trainingData.sparkContext
     val tracker = new RabitTracker(numWorkers)
     require(tracker.start(), "FAULT: Failed to start tracker")
-    boosters = buildDistributedBoosters(trainingData, configMap, numWorkers, round, obj, eval)
+    boosters = buildDistributedBoosters(trainingData, configMap,
+      tracker.getWorkerEnvs.asScala, numWorkers, round, obj, eval)
     // force the job
     boosters.foreachPartition(_ => ())
     println("=====finished training=====")