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new 2567c90 Add Windows MKLDNN Building Instruction (#10613)
2567c90 is described below
commit 2567c90941f2943f22bb93e6f7db563a4e7c7cc5
Author: XinyuChen <xinyu1.c...@intel.com>
AuthorDate: Fri May 18 20:59:50 2018 +0800
Add Windows MKLDNN Building Instruction (#10613)
* add windows mkldnn instruction
* update readme
* typo full mkl to mkldnn
* update blas
* update mxnet url
* update mkl build
* intel mkl liscence
* retrigger
---
MKL_README.md | 96 ++++++++++++++++++++++++++++++++++---------
docs/install/windows_setup.md | 4 +-
2 files changed, 79 insertions(+), 21 deletions(-)
diff --git a/MKL_README.md b/MKL_README.md
index 5374adb..a5c63b0 100644
--- a/MKL_README.md
+++ b/MKL_README.md
@@ -1,19 +1,77 @@
-# Full MKL Installation
-
-## Build/Install MXNet with a full MKL installation:
-Installing and enabling the full MKL installation enables MKL support for all
operators under the linalg namespace.
-
- 1. Download and install the latest full MKL version following instructions
on the [intel
website.](https://software.intel.com/en-us/articles/intel-mkl-111-install-guide)
-
- 2. Set USE_BLAS=mkl in make/config.mk
-
- 1.1 Set ADD_LDFLAGS=-L<path/to/mkl/lib/folder> (ex.
ADD_LDFLAGS=-L/opt/intel/compilers_and_libraries_2018.0.128/linux/mkl/lib)
-
- 1.1 Set ADD_CFLAGS=-I<path/to/mkl/include/folder> (ex.
ADD_CFLAGS=-L/opt/intel/compilers_and_libraries_2018.0.128/linux/mkl/include)
-
- 3. Run 'make -j ${nproc}'
-
- 4. Navigate into the python directory
-
- 5. Run 'sudo python setup.py install'
-
+## Build/Install MXNet with a full MKL installation:
+
+To make it convenient for customers, Intel introduced a new license called
[Intel® Simplified
license](https://software.intel.com/en-us/license/intel-simplified-software-license)
that allows to redistribute not only dynamic libraries but also headers,
examples and static libraries.
+
+Installing and enabling the full MKL installation enables MKL support for all
operators under the linalg namespace.
+
+ 1. Download and install the latest full MKL version following instructions
on the [intel website.](https://software.intel.com/en-us/mkl)
+
+ 2. Run 'make -j ${nproc} USE_BLAS=mkl'
+
+ 3. Navigate into the python directory
+
+ 4. Run 'sudo python setup.py install'
+
+
+## Build/Install MXNet with MKLDNN on Windows:
+
+To build and install MXNet yourself, 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.
+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.
+5. Set the environment variable ```OpenCV_DIR``` to point to the ```OpenCV
build directory``` (```C:\opencv\build\x64\vc14``` for example). Also, you need
to add the OpenCV bin directory (```C:\opencv\build\x64\vc14\bin``` for
example) to the ``PATH`` variable.
+6. If you have Intel Math Kernel Library (MKL) installed, set ```MKL_ROOT```
to point to ```MKL``` directory that contains the ```include``` and ```lib```.
If you want to use MKL blas, you should set ```-DUSE_BLAS=mkl``` when cmake.
Typically, you can find the directory in
+```C:\Program Files
(x86)\IntelSWTools\compilers_and_libraries_2018\windows\mkl```.
+7. 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/). Note that
you should also download ```mingw64.dll.zip`` along with openBLAS and add them
to PATH.
+8. 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\```.
+
+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). Don't forget to pull the
submodules:
+```
+ git clone https://github.com/apache/incubator-mxnet.git --recursive
+```
+
+2. Copy file `3rdparty/mkldnn/config_template.vcxproj` to incubator-mxnet root.
+
+3. Start a Visual Studio command prompt.
+
+4. Use [CMake](https://cmake.org/) to create a Visual Studio solution in
```./build``` or some other directory. Make sure to specify the architecture in
the
+[CMake](https://cmake.org/) command:
+```
+ mkdir build
+ cd build
+ cmake -G "Visual Studio 14 Win64" .. -DUSE_CUDA=0 -DUSE_CUDNN=0
-DUSE_NVRTC=0 -DUSE_OPENCV=1 -DUSE_OPENMP=1 -DUSE_PROFILER=1 -DUSE_BLAS=open
-DUSE_LAPACK=1 -DUSE_DIST_KVSTORE=0 -DCUDA_ARCH_NAME=All -DUSE_MKLDNN=1
-DCMAKE_BUILD_TYPE=Release
+```
+
+5. In Visual Studio, open the solution file,```.sln```, and compile it.
+These commands produce a library called ```libmxnet.dll``` in the
```./build/Release/``` or ```./build/Debug``` folder.
+Also ```libmkldnn.dll``` with be in the
```./build/3rdparty/mkldnn/src/Release/```
+
+6. Make sure that all the dll files used above(such as `libmkldnn.dll`,
`libmklml.dll`, `libiomp5.dll`, `libopenblas.dll`, etc) are added to the system
PATH. For convinence, you can put all of them to ```\windows\system32```. Or
you will come across `Not Found Dependencies` when loading mxnet.
+
+## Install MXNet for Python
+
+1. Install ```Python``` using windows installer available
[here](https://www.python.org/downloads/release/python-2712/).
+2. Install ```Numpy``` using windows installer available
[here](http://scipy.org/install.html).
+3. Next, we install Python package interface for MXNet. You can find the
Python interface package for [MXNet on
GitHub](https://github.com/dmlc/mxnet/tree/master/python/mxnet).
+
+```CMD
+ cd python
+ python setup.py install
+```
+Done! We have installed MXNet with Python interface. Run below commands to
verify our installation is successful.
+```CMD
+ # Open Python terminal
+ python
+
+ # You should be able to import mxnet library without any issues.
+ >>> import mxnet as mx;
+ >>> a = mx.nd.ones((2, 3));
+ >>> print ((a*2).asnumpy());
+ [[ 2. 2. 2.]
+ [ 2. 2. 2.]]
+```
+We actually did a small tensor computation using MXNet! You are all set with
MKLDNN MXNet on your Windows machine.
diff --git a/docs/install/windows_setup.md b/docs/install/windows_setup.md
index 09a39e2..07027ad 100755
--- a/docs/install/windows_setup.md
+++ b/docs/install/windows_setup.md
@@ -34,9 +34,9 @@ To build and install MXNet yourself, you need the following
dependencies. Instal
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/dmlc/mxnet). Don't forget to pull the submodules:
+1. Download the MXNet source code from
[GitHub](https://github.com/apache/incubator-mxnet). Don't forget to pull the
submodules:
```
- git clone https://github.com/apache/incubator-mxnet.git ~/mxnet --recursive
+ git clone https://github.com/apache/incubator-mxnet.git --recursive
```
2. Start a Visual Studio command prompt.
3. Use [CMake](https://cmake.org/) to create a Visual Studio solution in
```./build``` or some other directory. Make sure to specify the architecture in
the
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