[R][DOC] update R installation guide (#6457)

docs/get_started/install.md

@@ -648,8 +648,200 @@ You could also run distributed deeplearning with *MXNet* on AWS using [Cloudform
 
 <!-- END - Cloud Python Installation Instructions -->
 
+
+<!-- START - MacOS R CPU Installation Instructions -->
+
+<div class="macos">
+  <div class="r">
+    <div class="cpu">
+
+The CPU version of MXNet R package can be installed in R like other packages
+
+```r
+install.packages("drat")
+drat::addRepo("dmlc")
+install.packages("mxnet")
+```
+
+
+</div>
+
+
+<div class="gpu">
+
+Will be available soon.
+
+</div>
+
+</div>
+</div>
+<!-- END - MacOS R CPU Installation Instructions -->
+
+
+<div class="linux">
+  <div class="r">
+    <div class="cpu">
+<br/>
+
+Building *MXNet* from source is a 2 step process.
+1. Build the *MXNet* core shared library, `libmxnet.so`, from the C++ sources.
+2. Build the language specific bindings.
+
+**Minimum Requirements**
+1. [GCC 4.8](https://gcc.gnu.org/gcc-4.8/) or later to compile C++ 11.
+2. [GNU Make](https://www.gnu.org/software/make/)
+
+<br/>
+
+**Build the MXNet core shared library**
+
+**Step 1** Install build tools and git.
+```bash
+$ sudo apt-get update
+$ sudo apt-get install -y build-essential git
+```
+
+**Step 2** Install OpenBLAS.
+
+*MXNet* uses [BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) library for accelerated numerical computations on CPU machine. There are several flavors of BLAS libraries - [OpenBLAS](http://www.openblas.net/), [ATLAS](http://math-atlas.sourceforge.net/) and [MKL](https://software.intel.com/en-us/intel-mkl). In this step we install OpenBLAS. You can choose to install ATLAS or MKL.
+```bash
+$ sudo apt-get install -y libopenblas-dev
+```
+
+**Step 3** Install OpenCV.
+
+*MXNet* uses [OpenCV](http://opencv.org/) for efficient image loading and augmentation operations.
+```bash
+$ sudo apt-get install -y libopencv-dev
+```
+
+**Step 4** Download MXNet sources and build MXNet core shared library.
+
+```bash
+$ git clone --recursive https://github.com/dmlc/mxnet
+$ cd mxnet
+$ make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas
+```
+
+*Note* - USE_OPENCV and USE_BLAS are make file flags to set compilation options to use OpenCV and BLAS library. You can explore and use more compilation options in `make/config.mk`.
+
+<br/>
+
+**Build and install the MXNet R binding**
+
+
+```bash
+$ make rpkg
+$ R CMD INSTALL mxnet_current_r.tar.gz
+```
+
+
+</div>
+
+<div class="gpu">
+
+The following installation instructions have been tested on Ubuntu 14.04 and 16.04.
+
+
+**Prerequisites**
+
+Install the following NVIDIA libraries to setup *MXNet* with GPU support:
+
+1. Install CUDA 8.0 following the NVIDIA's [installation guide](http://docs.nvidia.com/cuda/cuda-installation-guide-linux/).
+2. Install cuDNN 5 for CUDA 8.0 following the NVIDIA's [installation guide](https://developer.nvidia.com/cudnn). You may need to register with NVIDIA for downloading the cuDNN library.
+
+**Note:** Make sure to add CUDA install path to `LD_LIBRARY_PATH`.
+
+Example - *export LD_LIBRARY_PATH=/usr/local/cuda/lib64/:$LD_LIBRARY_PATH*
+
+<br/>
+
+Building *MXNet* from source is a 2 step process.
+1. Build the *MXNet* core shared library, `libmxnet.so`, from the C++ sources.
+2. Build the language specific bindings.
+
+**Minimum Requirements**
+1. [GCC 4.8](https://gcc.gnu.org/gcc-4.8/) or later to compile C++ 11.
+2. [GNU Make](https://www.gnu.org/software/make/)
+
+<br/>
+
+**Build the MXNet core shared library**
+
+**Step 1** Install build tools and git.
+```bash
+$ sudo apt-get update
+$ sudo apt-get install -y build-essential git
+```
+**Step 2** Install OpenBLAS.
+
+*MXNet* uses [BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) library for accelerated numerical computations. There are several flavors of BLAS libraries - [OpenBLAS](http://www.openblas.net/), [ATLAS](http://math-atlas.sourceforge.net/) and [MKL](https://software.intel.com/en-us/intel-mkl). In this step we install OpenBLAS. You can choose to install ATLAS or MKL.
+```bash
+$ sudo apt-get install -y libopenblas-dev
+```
+
+**Step 3** Install OpenCV.
+
+*MXNet* uses [OpenCV](http://opencv.org/) for efficient image loading and augmentation operations.
+```bash
+$ sudo apt-get install -y libopencv-dev
+```
+
+**Step 4** Download MXNet sources and build MXNet core shared library.
+
+```bash
+$ git clone --recursive https://github.com/dmlc/mxnet
+$ cd mxnet
+$ make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas USE_CUDA=1 USE_CUDA_PATH=/usr/local/cuda USE_CUDNN=1
+```
+
+*Note* - USE_OPENCV, USE_BLAS, USE_CUDA, USE_CUDA_PATH AND USE_CUDNN are make file flags to set compilation options to use OpenCV, OpenBLAS, CUDA and cuDNN libraries. You can explore and use more compilation options in `make/config.mk`. Make sure to set USE_CUDA_PATH to right CUDA installation path. In most cases it is - */usr/local/cuda*.
+
+<br/>
+
+**Build and install the MXNet R binding**
+
+```bash
+$ make rpkg
+$ R CMD INSTALL mxnet_current_r.tar.gz
+```
+
+</div>
+
+</div>
+</div>
+
+
+<!-- START - Windows R CPU Installation Instructions -->
+
+<div class="windows">
+  <div class="r">
+    <div class="cpu">
+
+The CPU version of MXNet R package can be installed in R like other packages
+
+
+```r
+install.packages("drat")
+drat::addRepo("dmlc")
+install.packages("mxnet")
+```
+
+
+</div>
+
+<div class="gpu">
+
+Will be available soon.
+
+</div>
+</div>
+</div>
+
+<!-- END - Windows R CPU Installation Instructions -->
+
 <div class="linux">
-  <div class="scala r julia perl">
+  <div class="scala julia perl">
     <div class="cpu gpu">
 
 Follow the installation instructions [in this guide](./ubuntu_setup.md) to set up MXNet.
@@ -659,7 +851,7 @@ Follow the installation instructions [in this guide](./ubuntu_setup.md) to set u
 </div>
 
 <div class="macos">
-  <div class="scala r julia perl">
+  <div class="scala julia perl">
     <div class="cpu gpu">
 
 Follow the installation instructions [in this guide](./osx_setup.md) to set up MXNet.
@@ -669,8 +861,8 @@ Follow the installation instructions [in this guide](./osx_setup.md) to set up M
 </div>
 
 <div class="windows">
-  <div class="python scala r julia perl">
-    <div class="cpu gpu">
+  <div class="python scala julia perl">
+    <div class="gpu">
 
 Follow the installation instructions [in this guide](./windows_setup.md) to set up MXNet.
 
@@ -1068,7 +1260,7 @@ Start the python terminal.
 ```bash
 $ python
 ```
-<!-- Example code for CPU -->
+<!-- Example Python code for CPU -->
 
 <div class="cpu">
 
@@ -1092,7 +1284,7 @@ $
 
 </div>
 
-<!-- Example code for CPU -->
+<!-- Example Python code for CPU -->
 
 <div class="gpu">
 
@@ -1111,8 +1303,47 @@ array([[ 3.,  3.,  3.],
 
 </div>
 
+<!-- Example R code for CPU -->
+
+<div class="linux macos windows">
+  <div class="r">
+    <div class="cpu">
+
+Run a short *MXNet* python program to create a 2X3 matrix of ones, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3.
+
+```r
+library(mxnet)
+a <- mx.nd.ones(c(2,3), ctx = mx.cpu())
+b <- a * 2 + 1
+b
+```
+
+</div>
+</div>
+</div>
+
+<!-- Example R code for GPU -->
+
+<div class="linux macos windows">
+  <div class="r">
+    <div class="gpu">
+
+Run a short *MXNet* python program to create a 2X3 matrix of ones *a* on a *GPU*, multiply each element in the matrix by 2 followed by adding 1. We expect the output to be a 2X3 matrix with all elements being 3. We use *mx.gpu()*, to set *MXNet* context to be GPUs.
+
+```r
+library(mxnet)
+a <- mx.nd.ones(c(2,3), ctx = mx.gpu())
+b <- a * 2 + 1
+b
+```
+
+</div>
+</div>
+</div>
+
+
 <div class="linux">
-  <div class="scala r julia perl">
+  <div class="scala julia perl">
     <div class="cpu gpu">
 
 Will be available soon.
@@ -1122,7 +1353,7 @@ Will be available soon.
 </div>
 
 <div class="macos">
-  <div class="scala r julia perl">
+  <div class="scala julia perl">
     <div class="cpu gpu">
 
 Will be available soon.
@@ -1132,7 +1363,7 @@ Will be available soon.
 </div>
 
 <div class="windows">
-  <div class="python scala r julia perl">
+  <div class="python scala julia perl">
     <div class="cpu gpu">
 
 Will be available soon.