Refine mxnet python installation

docs/install/windows_setup.md

@@ -141,10 +141,10 @@ To build and install MXNet yourself using [VS2017](https://www.visualstudio.com/
 1. Set the environment variable `OpenCV_DIR` to point to the OpenCV build directory that you just unzipped. Start ```cmd``` and type `set OpenCV_DIR=C:\utils\opencv\build`.
 1. If you don’t have the Intel Math Kernel Library (MKL) installed, you can install it and follow the [MKLDNN_README](https://github.com/apache/incubator-mxnet/blob/master/MKLDNN_README.md) from here, or you can use OpenBLAS. These instructions will assume you're using OpenBLAS.
 1. Download the [OpenBlas](https://sourceforge.net/projects/openblas/files/v0.2.19/OpenBLAS-v0.2.19-Win64-int32.zip/download) package. Later versions of OpenBLAS are available, but you would need to build from source. v0.2.19 is the most recent version that ships with binaries. Contributions of more recent binaries would be appreciated.
-1. Unzip the file. You can place the unzipped files and folders in another directory if you wish.
+1. Unzip the file, rename it to ```OpenBLAS``` and put it under `C:\utils`. You can place the unzipped files and folders in another directory if you wish.
 1. Set the environment variable `OpenBLAS_HOME` to point to the OpenBLAS directory that contains the `include` and `lib` directories and type `set OpenBLAS_HOME=C:\utils\OpenBLAS` on the command prompt(```cmd```).
 1. Download and install [CUDA](https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exelocal). If you already had CUDA, then installed VS2017, you should reinstall CUDA now so that you get the CUDA toolkit components for VS2017 integration. Note that the lastest CUDA version supported by MXNet is 9.2. You can find it on the `Legacy Releases`.
-1. Download and install cuDNN. To get access to the download link, register as an NVIDIA community user. Then Follow the [link](http://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#install-windows) to install the cuDNN and put those libraries into ```C:\cuda```.
+1. Download and install cuDNN. To get access to the download link, register as an NVIDIA community user. Then follow the [link](http://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#install-windows) to install the cuDNN and put those libraries into ```C:\cuda```.
 1. Download and install [git](https://git-for-windows.github.io/) if you haven't already.
 
 After you have installed all of the required dependencies, build the MXNet source code:
@@ -165,6 +165,7 @@ cd C:\incubator-mxnet\build
 ```
 cmake -G "Visual Studio 15 2017 Win64" -T cuda=9.2,host=x64 -DUSE_CUDA=1 -DUSE_CUDNN=1 -DUSE_NVRTC=1 -DUSE_OPENCV=1 -DUSE_OPENMP=1 -DUSE_BLAS=open -DUSE_LAPACK=1 -DUSE_DIST_KVSTORE=0 -DUSE_MKL_IF_AVAILABLE=OFF -DCUDA_ARCH_LIST=Common -DCUDA_TOOLSET=9.2 -DCUDNN_INCLUDE=C:\cuda\include -DCUDNN_LIBRARY=C:\cuda\lib\x64\cudnn.lib "C:\incubator-mxnet"
 ```
+* Make sure you set the environment variables correctly (OpenBLAS_HOME, OpenCV_DIR) and change the version of the Visual studio 2017 to v14.11 before enter above command.
 6. After the CMake successfully completed, compile the the MXNet source code by using following command:
 ```
 msbuild mxnet.sln /p:Configuration=Release;Platform=x64 /maxcpucount
@@ -221,9 +222,6 @@ libgfortran-3.dll (in mingw64_dll)
 libquadmath-0.dll (in mingw64_dll)
 libopenblas.dll (in OpenBlas folder you download)
 opencv_world341.dll (in OpenCV folder you download)
-kernel32.dll
-vcomp140.dll
-nvcuda.dll (only if you use CUDA)
 ```
 6. Add ```C:\common``` to Environment Variables.
  * Type ```control sysdm.cpl``` on ```cmp```