From e395e16867dc69d83d4694a933cc664787127e78 Mon Sep 17 00:00:00 2001 From: Yasser Zamani Date: Thu, 14 Jun 2018 12:28:31 +0430 Subject: [PATCH] update python gpu build from source instructions on windows See Also: MXNET-540 --- docs/install/index.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/docs/install/index.md b/docs/install/index.md index 4b966b620675..b3e6d0b8393c 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).+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). 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.