address comments

scala-package/infer/src/main/scala/ml/dmlc/mxnet/infer/MXNetHandler.scala

@@ -36,20 +36,26 @@ private[infer] object MXNetHandlerType extends Enumeration {
   val OneThreadPerModelHandler = Value("MXNetOneThreadPerModelHandler")
 }
 
-private[infer] class MXNetThreadPoolHandler(numThreads: Option[Int] = Some(1))
+private[infer] class MXNetThreadPoolHandler(numThreads: Int = 1)
   extends MXNetHandler {
+
+  require(numThreads > 0, "numThreads should be a positive number, you passed:%d".
+    format(numThreads))
+
   private val logger = LoggerFactory.getLogger(classOf[MXNetThreadPoolHandler])
+  private var threadCount: Int = 0
 
   private val threadFactory = new ThreadFactory {
 
     override def newThread(r: Runnable): Thread = new Thread(r) {
       setName(classOf[MXNetThreadPoolHandler].getCanonicalName
-        + "-numThreads: %d".format(numThreads.get))
+        + "-%d".format(threadCount))
+      threadCount += 1
     }
   }
 
   override val executor: ExecutorService =
-    Executors.newFixedThreadPool(numThreads.get, threadFactory)
+    Executors.newFixedThreadPool(numThreads, threadFactory)
 
   private val creatorThread = executor.submit(new Callable[Thread] {
     override def call(): Thread = Thread.currentThread()
@@ -72,22 +78,24 @@ private[infer] class MXNetThreadPoolHandler(numThreads: Option[Int] = Some(1))
       try {
         result.get()
       } catch {
+        case e : InterruptedException => throw e
+        //unwrap the exception thrown by the task
         case e: Exception => throw e.getCause()
       }
     }
   }
 
 }
 
-private[infer] object MXNetSingleThreadHandler extends MXNetThreadPoolHandler(Some(1)) {
+private[infer] object MXNetSingleThreadHandler extends MXNetThreadPoolHandler(1) {
 
 }
 
 private[infer] object MXNetHandler {
 
   def apply(): MXNetHandler = {
     if (handlerType == MXNetHandlerType.OneThreadPerModelHandler) {
-      new MXNetThreadPoolHandler(Some(1))
+      new MXNetThreadPoolHandler(1)
     } else {
       MXNetSingleThreadHandler
     }

scala-package/infer/src/main/scala/ml/dmlc/mxnet/infer/Predictor.scala

@@ -40,7 +40,7 @@ private[infer] trait PredictBase {
 
   /**
    * Predict using NDArray as input. This method is useful when the input is a batch of data
-   * or when multiple operations on the input/output have to performed.
+   * or when multiple operations on the input have to performed.
    * Note: User is responsible for managing allocation/deallocation of NDArrays.
    * @param input: IndexedSequence NDArrays.
    * @return output of Predictions as NDArrays.
@@ -101,7 +101,7 @@ class Predictor(modelPathPrefix: String,
    * NDArray needed for inference. The array will be reshaped based on the input descriptors.
    *
    * @param input : A IndexedSequence of Scala one-dimensional array, An IndexedSequence is
-   *              is needed when the model has more than one input/output
+   *              is needed when the model has more than one input
    * @return IndexedSequence array of outputs.
    */
   override def predict(input: IndexedSeq[Array[Float]])
@@ -131,7 +131,8 @@ class Predictor(modelPathPrefix: String,
         forTraining = false))
     }
 
-    val resultND = mxNetHandler.execute(mod.predict(new NDArrayIter(inputND.toIndexedSeq)))
+    val resultND = mxNetHandler.execute(mod.predict(new NDArrayIter(
+      inputND.toIndexedSeq, dataBatchSize = 1)))
 
     val result = resultND.map((f : NDArray) => f.toArray)
 
@@ -148,8 +149,7 @@ class Predictor(modelPathPrefix: String,
 
   /**
    * Predict using NDArray as input. This method is useful when the input is a batch of data
-   * or when multiple operations on the input/output have to performed.
-   * Note: User is responsible for managing allocation/deallocation of NDArrays.
+   * Note: User is responsible for managing allocation/deallocation of input/output NDArrays.
    *
    * @param inputBatch : IndexedSequence NDArrays.
    * @return output of Predictions as NDArrays.
@@ -179,7 +179,8 @@ class Predictor(modelPathPrefix: String,
         forTraining = false))
     }
 
-    val resultND = mxNetHandler.execute(mod.predict(new NDArrayIter(inputBatch)))
+    val resultND = mxNetHandler.execute(mod.predict(new NDArrayIter(
+      inputBatch, dataBatchSize = inputBatchSize)))
 
     if (batchSize != inputBatchSize) {
       mxNetHandler.execute(mod.bind(iDescriptors, forceRebind = true,