[FLINK] remove nWorker from API

doc/jvm/xgboost4j-intro.md

@@ -147,7 +147,7 @@ val trainData = MLUtils.readLibSVM(env, "/path/to/data/agaricus.txt.train")
 Model Training can be done as follows
 
 ```scala
-val xgboostModel = XGBoost.train(trainData, paramMap, round, nWorkers)
+val xgboostModel = XGBoost.train(trainData, paramMap, round)
 
 ```
 

jvm-packages/README.md

@@ -72,7 +72,7 @@ object DistTrainWithSpark {
       "eta" -> 0.1f,
       "max_depth" -> 2,
       "objective" -> "binary:logistic").toMap
-    // use 5 distributed workers to train the model 
+    // use 5 distributed workers to train the model
     val model = XGBoost.train(trainRDD, paramMap, numRound, nWorkers = 5)
     // save model to HDFS path
     model.saveModelToHadoop(outputModelPath)
@@ -100,9 +100,8 @@ object DistTrainWithFlink {
       "objective" -> "binary:logistic").toMap
     // number of iterations
     val round = 2
-    val nWorkers = 5
     // train the model
-    val model = XGBoost.train(trainData, paramMap, round, nWorkers)
+    val model = XGBoost.train(trainData, paramMap, round)
     val predTrain = model.predict(trainData.map{x => x.vector})
     model.saveModelToHadoop("file:///path/to/xgboost.model")
   }

jvm-packages/xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/flink/DistTrainWithFlink.scala

@@ -33,9 +33,8 @@ object DistTrainWithFlink {
       "objective" -> "binary:logistic").toMap
     // number of iterations
     val round = 2
-    val nWorkers = 5
     // train the model
-    val model = XGBoost.train(trainData, paramMap, round, 5)
+    val model = XGBoost.train(trainData, paramMap, round)
     val predTest = model.predict(testData.map{x => x.vector})
     model.saveModelAsHadoopFile("file:///path/to/xgboost.model")
   }

jvm-packages/xgboost4j-flink/src/main/scala/ml/dmlc/xgboost4j/scala/flink/XGBoost.scala

@@ -82,9 +82,9 @@ object XGBoost {
     * @param params The parameters to XGBoost.
     * @param round Number of rounds to train.
     */
-  def train(dtrain: DataSet[LabeledVector], params: Map[String, Any], round: Int, nWorkers: Int):
+  def train(dtrain: DataSet[LabeledVector], params: Map[String, Any], round: Int):
       XGBoostModel = {
-    val tracker = new RabitTracker(nWorkers)
+    val tracker = new RabitTracker(dtrain.getExecutionEnvironment.getParallelism)
     if (tracker.start()) {
       dtrain
         .mapPartition(new MapFunction(params, round, tracker.getWorkerEnvs))