[GitHub] sandeep-krishnamurthy commented on a change in pull request #12696: Refine mxnet python installation

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sandeep-krishnamurthy commented on a change in pull request #12696: Refine mxnet python installation
URL: https://github.com/apache/incubator-mxnet/pull/12696#discussion_r222855153
 
 

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 File path: docs/install/windows_setup.md
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 @@ -137,14 +137,14 @@ To build and install MXNet yourself using [VS2017](https://www.visualstudio.com/
 ```
 1. Download and install [CMake](https://cmake.org/download) if it is not already installed. [CMake v3.12.2](https://cmake.org/files/v3.12/cmake-3.12.2-win64-x64.msi) has been tested with MXNet.
 1. Download and run the  [OpenCV](https://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.4.1/opencv-3.4.1-vc14_vc15.exe/download) package. There are more recent versions of OpenCV, so please create an issue/PR to update this info if you validate one of these later versions.
-1. This will unzip several files. You can place them in another directory if you wish.
-1. Set the environment variable `OpenCV_DIR` to point to the OpenCV build directory that you just unzipped (e.g., `OpenCV_DIR = C:\utils\opencv\build`).
+1. This will unzip several files. You can place them in another directory if you wish. We will use `C:\utils` as our default path.
+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. Set the environment variable `OpenBLAS_HOME` to point to the OpenBLAS directory that contains the `include` and `lib` directories (e.g., `OpenBLAS_HOME = C:\utils\OpenBLAS`).
-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.
-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.
+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`.
 
 Review comment:
   lastest -> latest ?

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