[GitHub] szha closed pull request #11276: [MXNET-540] update python gpu build from source instructions on windows

[GitBox <gi...@apache.org>]

szha closed pull request #11276: [MXNET-540] update python gpu build from source instructions on windows
URL: https://github.com/apache/incubator-mxnet/pull/11276
 
 
   

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diff --git a/docs/install/index.md b/docs/install/index.md
index 4b966b62067..0a102394ec7 100644
--- a/docs/install/index.md
+++ b/docs/install/index.md
@@ -1703,7 +1703,7 @@ msbuild mxnet.sln /p:Configuration=Release;Platform=x64 /maxcpucount
 
 To build and install MXNet yourself using [Microsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/), you need the following dependencies. Install the required dependencies:
 
-1. If [Microsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/) is not already installed, download and install it. You can download and install the free community edition.
+1. If [Microsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/) is not already installed, download and install it. You can download and install the free community edition. At least Update 3 of Microsoft Visual Studio 2015 is required to build MXNet from source. Upgrade via it's ```Tools -> Extensions and Updates... | Product Updates``` menu.
 2. Download and install [CMake](https://cmake.org/) if it is not already installed.
 3. Download and install [OpenCV](http://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.0.0/opencv-3.0.0.exe/download).
 4. Unzip the OpenCV package.
@@ -1711,10 +1711,12 @@ To build and install MXNet yourself using [Microsoft Visual Studio 2015](https:/
 6. If you don't have the Intel Math Kernel Library (MKL) installed, download and install [OpenBlas](http://sourceforge.net/projects/openblas/files/v0.2.14/).
 7. Set the environment variable ```OpenBLAS_HOME``` to point to the ```OpenBLAS``` directory that contains the ```include``` and ```lib``` directories. Typically, you can find the directory in ```C:\Program files (x86)\OpenBLAS\```.
 8. Download and install [CUDA](https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64) and [cuDNN](https://developer.nvidia.com/cudnn). To get access to the download link, register as an NVIDIA community user.
+9. Set the environment variable ```CUDACXX``` to point to the ```CUDA Compiler```(```C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.1\bin\nvcc.exe``` for example).
+10. Set the environment variable ```CUDNN_ROOT``` to point to the ```cuDNN``` directory that contains the ```include```,  ```lib``` and ```bin``` directories (```C:\Downloads\cudnn-9.1-windows7-x64-v7\cuda``` for example).
 
 After you have installed all of the required dependencies, build the MXNet source code:
 
-1. Download the MXNet source code from [GitHub](https://github.com/apache/incubator-mxnet).
+1. Download the MXNet source code from [GitHub](https://github.com/apache/incubator-mxnet) (make sure you also download third parties submodules e.g. ```git clone --recurse-submodules```).
 2. Use [CMake](https://cmake.org/) to create a Visual Studio solution in ```./build```.
 3. In Visual Studio, open the solution file,```.sln```, and compile it.
 These commands produce a library called ```mxnet.dll``` in the ```./build/Release/``` or ```./build/Debug``` folder.


 

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