MKL_README.md

Build/Install MXNet with a full MKL installation:

To make it convenient for customers, Intel introduced a new license called Intel® Simplified 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.

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 is not already installed, download and install it. You can download and install the free community edition.
2. Download and Install CMake if it is not already installed.
3. Download and install OpenCV.
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. 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. 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 to create a Visual Studio solution in ./build or some other directory. Make sure to specify the architecture in the CMake 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.
2. Install Numpy using windows installer available here.
3. Next, we install Python package interface for MXNet. You can find the Python interface package for MXNet on GitHub.

    cd python
    python setup.py install

Done! We have installed MXNet with Python interface. Run below commands to verify our installation is successful.

    # 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.