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new 28cb133 Fix windows setup doc using VS 2017 (#10363)
28cb133 is described below
commit 28cb133ef0db27b9e8c809c6d10aab90ecb9e465
Author: cgwang <wangcg....@gmail.com>
AuthorDate: Wed Apr 4 18:15:05 2018 -0700
Fix windows setup doc using VS 2017 (#10363)
update windows gpu setup
---
docs/install/index.md | 64 ++++++++++++++++++++++++++++++++++++++++++++++++---
1 file changed, 61 insertions(+), 3 deletions(-)
diff --git a/docs/install/index.md b/docs/install/index.md
index d9d78dd..da68745 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).
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