sxjscience closed pull request #10363: Fix windows setup doc using VS 2017 URL: https://github.com/apache/incubator-mxnet/pull/10363
This is a PR merged from a forked repository. As GitHub hides the original diff on merge, it is displayed below for the sake of provenance: As this is a foreign pull request (from a fork), the diff is supplied below (as it won't show otherwise due to GitHub magic): diff --git a/docs/install/index.md b/docs/install/index.md index d9d78dd3693..da687451837 100644 --- a/docs/install/index.md +++ b/docs/install/index.md @@ -992,7 +992,67 @@ Refer to [#8671](https://github.com/apache/incubator-mxnet/issues/8671) for stat </div> <div class="build-from-source"> <br/> -To build and install MXNet yourself, you need the following dependencies. Install the required dependencies: + +We provide both options to build and install MXNet yourself using [Microsoft Visual Studio 2017](https://www.visualstudio.com/downloads/), and [Microsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/). + +**Option 1** + +To build and install MXNet yourself using [Microsoft Visual Studio 2017](https://www.visualstudio.com/downloads/), you need the following dependencies. Install the required dependencies: + +1. If [Microsoft Visual Studio 2017](https://www.visualstudio.com/downloads/) is not already installed, download and install it. You can download and install the free community edition. +2. Download and install [CMake](https://cmake.org/files/v3.11/cmake-3.11.0-rc4-win64-x64.msi) if it is not already installed. +3. Download and install [OpenCV](https://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.4.1/opencv-3.4.1-vc14_vc15.exe/download). +4. Unzip the OpenCV package. +5. Set the environment variable ```OpenCV_DIR``` to point to the ```OpenCV build directory``` (e.g., ```OpenCV_DIR = C:\utils\opencv\build```). +6. If you don’t have the Intel Math Kernel Library (MKL) installed, download and install [OpenBlas](https://sourceforge.net/projects/openblas/files/v0.2.20/OpenBLAS%200.2.20%20version.zip/download). +7. 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```). +8. Download and install CUDA: Install [CUDA](https://developer.nvidia.com/cuda-downloads?target_os=Windows&target_arch=x86_64&target_version=10&target_type=exelocal), and Download the base installer (e.g., ```cuda_9.1.85_win10.exe```). +9. 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. +10. Download and install [git](https://git-for-windows.github.io/). + +After you have installed all of the required dependencies, build the MXNet source code: + +1. Start ```cmd``` in windows. + +2. Download the MXNet source code from GitHub by using following command: + +```r +cd C:\ +git clone https://github.com/apache/incubator-mxnet.git --recursive +``` + +3. Follow [this link](https://docs.microsoft.com/en-us/visualstudio/install/modify-visual-studio) to modify ```Individual components```, and check ```VC++ 2017 version 15.4 v14.11 toolset```, and click ```Modify```. + +4. Change the version of the Visual studio 2017 to v14.11 using the following command (by default the VS2017 is installed in the following path): + +```r +"C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\VC\Auxiliary\Build\vcvars64.bat" -vcvars_ver=14.11 +``` + +5. Create a build dir using the following command and go to the directory, for example: + +```r +mkdir C:\build +cd C:\build +``` + +6. CMake the MXNet source code by using following command: + +```r +cmake -G "Visual Studio 15 2017 Win64" -T cuda=9.1,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 -DCUDA_ARCH_LIST=Common -DCUDA_TOOLSET=9.1 -DCUDNN_INCLUDE=C:\cuda\include -DCUDNN_LIBRARY=C:\cuda\lib\x64\cudnn.lib "C:\incubator-mxnet" +``` + +NOTE: make sure the DCUDNN_INCLUDE and DCUDNN_LIBRARY pointing to the “include” and “cudnn.lib” of your CUDA installed location, and the ```C:\incubator-mxnet``` is the location of the source code you just git in the previous step + +7. After the CMake successfully completed, compile the the MXNet source code by using following command: + +```r +msbuild mxnet.sln /p:Configuration=Release;Platform=x64 /maxcpucount +``` + +**Option 2** + +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. 2. Download and install [CMake](https://cmake.org/) if it is not already installed. @@ -1010,8 +1070,6 @@ After you have installed all of the required dependencies, build the MXNet sourc 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. - - Next, we install the ```graphviz``` library that we use for visualizing network graphs that you build on MXNet. We will also install [Jupyter Notebook](http://jupyter.readthedocs.io/) which is used for running MXNet tutorials and examples. - Install the ```graphviz``` by downloading the installer from the [Graphviz Download Page](https://graphviz.gitlab.io/_pages/Download/Download_windows.html). ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
