Re-organize scala maven build

1. Automatically detect which platform to build for scala.
2. Remove platform dependend submodules
3. Fix cyclic module dependencies
4. Fix scalatype style check
5. Now mvn can be executed in submodule
6. Maven build can be executed from any directory not only in root project
7. Checkin javah header file, and use verify task to detect native API changes
8. Improve incremental build performance
9. Remove unittest and integration-test profile, use proper task instead
10. Delete generated scala file during maven clean.

Makefile

@@ -678,7 +678,7 @@ scaladeploylocal:
 			-Dlddeps="$(LIB_DEP) $(ROOTDIR)/lib/libmxnet.a")
 
 jnilint:
-	3rdparty/dmlc-core/scripts/lint.py mxnet-jnicpp cpp scala-package/native/src
+	3rdparty/dmlc-core/scripts/lint.py mxnet-jnicpp cpp scala-package/native/src --exclude_path scala-package/native/src/main/native/org_apache_mxnet_native_c_api.h
 
 rclean:
 	$(RM) -r R-package/src/image_recordio.h R-package/NAMESPACE R-package/man R-package/R/mxnet_generated.R \

contrib/clojure-package/project.clj

@@ -29,7 +29,7 @@
                  ;[org.apache.mxnet/mxnet-full_2.11-linux-x86_64-gpu "1.2.1"]
 
                  ;;; CI
-                 [org.apache.mxnet/mxnet-full_2.11-linux-x86_64-cpu "1.5.0-SNAPSHOT"]
+                 [org.apache.mxnet/mxnet-full_2.11 "1.5.0-SNAPSHOT"]
 
                  [org.clojure/tools.logging "0.4.0"]
                  [org.apache.logging.log4j/log4j-core "2.8.1"]

scala-package/README.md

@@ -13,7 +13,7 @@ It brings flexible and efficient GPU/CPU computing and state-of-art deep learnin
   and apply them to tasks such as image classification and data science challenges.
 
 Install
-------------
+-------
 
 Technically, all you need is the `mxnet-full_2.11-{arch}-{xpu}-{version}.jar` in your classpath.
 It will automatically extract the native library to a tempfile and load it.
@@ -26,45 +26,49 @@ Use the following dependency in maven, change the artifactId according to your o
 ```HTML
 <dependency>
   <groupId>org.apache.mxnet</groupId>
-  <artifactId>mxnet-full_2.10-linux-x86_64-gpu</artifactId>
-  <version>0.1.1</version>
-</dependency>
-```
-
-You can also use `mxnet-core_2.10-0.1.1.jar` and put the compiled native library somewhere in your load path.
-
-```HTML
-<dependency>
-  <groupId>org.apache.mxnet</groupId>
-  <artifactId>mxnet-core_2.10</artifactId>
-  <version>0.1.1</version>
+  <artifactId>mxnet-full_2.11-linux-x86_64-gpu</artifactId>
+  <version>1.5.0</version>
 </dependency>
 ```
 
 If you have some native libraries conflict with the ones in the provided 'full' jar (e.g., you use openblas instead of atlas), this is a recommended way.
 Refer to the next section for how to build it from the very source.
 
 Build
-------------
+-----
 
-Checkout the [Installation Guide](http://mxnet.incubator.apache.org/install/index.html) contains instructions to install mxnet.
+### Prerequisites
+
+* Check out MXNet source repository:
+
+    Checkout the [Installation Guide](http://mxnet.incubator.apache.org/install/index.html) contains instructions to install mxnet.
 Then you can compile the Scala Package by
 
+* Build MXNet shared library
+
+    Scala maven build assume you already have a ``lib/libmxnet.so`` file.
+
+* You have JDK installed in your system.
+
+### Build scala package
+
+Scala build will detect your platform (OSX/Linux) and libmxnet.so flavor (CPU/GPU).
+
 ```bash
-make scalapkg
+cd scala-package
+mvn install
 ```
 
 (Optional) run unit/integration tests by
 
 ```bash
-make scalaunittest
-make scalaintegrationtest
+mvn integration-test
 ```
 
 Or run a subset of unit tests by, e.g.,
 
 ```bash
-make SCALA_TEST_ARGS=-Dsuites=org.apache.mxnet.NDArraySuite scalaunittest
+mvn -Dsuites=org.apache.mxnet.NDArraySuite integration-test
 ```
 
 If everything goes well, you will find jars for `assembly`, `core` and `example` modules.
@@ -100,7 +104,7 @@ tracker/dmlc_local.py -n 2 -s 2 \
 Change the arguments and have fun!
 
 Usage
--------
+-----
 Here is a Scala example of what training a simple 3-layer multilayer perceptron on MNIST looks like. You can download the MNIST dataset using [get_mnist_data script](https://github.com/apache/incubator-mxnet/blob/master/scala-package/core/scripts/get_mnist_data.sh).
 
 ```scala