Orca openvino estimator (#3011)

* init

* add distripredict

* javardd

* java spark context

* test

* fix

* remove tes

* remove

* add type check

* meet comments

* fix import

* fix

* fix style

* combine testcase

* fix style

* replaceable variable

scala/orca/src/main/scala/com/intel/analytics/bigdl/orca/python/PythonOrca.scala

@@ -0,0 +1,50 @@
+/*
+ * Copyright 2018 Analytics Zoo Authors.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.intel.analytics.zoo.orca.python
+
+import com.intel.analytics.zoo.pipeline.inference.InferenceModel
+import org.apache.spark.api.java.{JavaRDD, JavaSparkContext}
+import java.util.{List => JList}
+
+import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
+import com.intel.analytics.zoo.common.PythonZoo
+
+import scala.reflect.ClassTag
+
+object PythonOrca {
+
+  def ofFloat(): PythonOrca[Float] = new PythonOrca[Float]()
+
+  def ofDouble(): PythonOrca[Double] = new PythonOrca[Double]()
+}
+
+class PythonOrca[T: ClassTag](implicit ev: TensorNumeric[T]) extends PythonZoo[T] {
+  def inferenceModelDistriPredict(model: InferenceModel, sc: JavaSparkContext,
+                                  inputs: JavaRDD[JList[com.intel.analytics.bigdl.python.api
+                                  .JTensor]],
+                                  inputIsTable: Boolean): JavaRDD[JList[Object]] = {
+    val broadcastModel = sc.broadcast(model)
+    inputs.rdd.mapPartitions(partition => {
+      val localModel = broadcastModel.value
+      partition.map(inputs => {
+        val inputActivity = jTensorsToActivity(inputs, inputIsTable)
+        val outputActivity = localModel.doPredict(inputActivity)
+        activityToList(outputActivity)
+      })
+    })
+  }
+}