{"action":"created","comment":{"url":"https://api.github.com/repos/apache/incubator-mxnet/pulls/comments/196632864","pull_request_review_id":130222157,"id":196632864,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDE5NjYzMjg2NA==","diff_hunk":"@@ -0,0 +1,287 @@\n+# Build/Install MXNet with MKL-DNN\r\n+\r\n+<h2 id=\"0\">Contents</h2>\r\n+\r\n+* [1. Linux](#1)\r\n+* [2. MacOS](#2)\r\n+* [3. Windows](#3)\r\n+* [4. Verify MXNet with python](#4)\r\n+* [5. Enable MKL BLAS](#5)\r\n+\r\n+<h2 id=\"1\">Linux</h2>\r\n+\r\n+### Prerequisites\r\n+\r\n+```\r\n+apt-get update && apt-get install -y build-essential git libopencv-dev curl gcc libopenblas-dev python python-pip python-dev python-opencv graphviz python-scipy python-sklearn\r\n+```\r\n+\r\n+### Clone MXNet sources\r\n+\r\n+```\r\n+git clone --recursive https://github.com/apache/incubator-mxnet.git\r\n+cd incubator-mxnet\r\n+git submodule update --recursive --init\r\n+```\r\n+\r\n+### Build MXNet with MKL-DNN\r\n+\r\n+```\r\n+make -j $(nproc) USE_OPENCV=1 USE_MKLDNN=1 USE_BLAS=mkl USE_INTEL_PATH=/opt/intel\r\n+```\r\n+\r\n+If you don't have full MKL library installed, you can use OpenBLAS by setting `USE_BLAS=openblas`.\r\n+\r\n+<h2 id=\"2\">MacOS</h2>\r\n+\r\n+### Prerequisites\r\n+\r\n+Install the dependencies, required for MXNet, with the following commands:\r\n+\r\n+- [Homebrew](https://brew.sh/)\r\n+- gcc (clang in macOS does not support OpenMP)\r\n+- OpenCV (for computer vision operations)\r\n+\r\n+```\r\n+# Paste this command in Mac terminal to install Homebrew\r\n+/usr/bin/ruby -e \"$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)\"\r\n+\r\n+# install dependency\r\n+brew update\r\n+brew install pkg-config\r\n+brew install graphviz\r\n+brew tap homebrew/core\r\n+brew install opencv\r\n+brew tap homebrew/versions\r\n+brew install gcc49\r\n+brew link gcc49\r\n+```\r\n+\r\n+### Enable OpenMP for MacOS\r\n+\r\n+If you want to enable OpenMP for better performance, you should modify these two files:\r\n+\r\n+1. Makefile L138:\r\n+\r\n+```\r\n+ifeq ($(USE_OPENMP), 1)\r\n+# ifneq ($(UNAME_S), Darwin)\r\n+    CFLAGS += -fopenmp\r\n+# endif\r\n+endif\r\n+```\r\n+\r\n+2. prepare_mkldnn.sh L96:\r\n+\r\n+```\r\n+CC=gcc-4.9 CXX=g++-4.9 cmake $MKLDNN_ROOTDIR -DCMAKE_INSTALL_PREFIX=$MKLDNN_INSTALLDIR -B$MKLDNN_BUILDDIR -DARCH_OPT_FLAGS=\"-mtune=generic\" -DWITH_TEST=OFF -DWITH_EXAMPLE=OFF >&2\r\n+```\r\n+\r\n+### Build MXNet with MKL-DNN\r\n+\r\n+```\r\n+make -j $(sysctl -n hw.ncpu) CC=gcc-4.9 CXX=g++-4.9 USE_OPENCV=0 USE_OPENMP=1 USE_MKLDNN=1 USE_BLAS=apple USE_PROFILER=1\r\n+```\r\n+\r\n+*Note: Temporarily disable OPENCV.*\r\n+\r\n+<h2 id=\"3\">Windows</h2>\r\n+\r\n+We recommend to build and install MXNet yourself using [Microsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/), or you can also try experimentally the latest [Microsoft Visual Studio 2017](https://www.visualstudio.com/downloads/).\r\n+\r\n+**Visual Studio 2015**\r\n+\r\n+To build and install MXNet yourself, you need the following dependencies. Install the required dependencies:\r\n+\r\n+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.\r\n+2. Download and Install [CMake](https://cmake.org/) if it is not already installed.\r\n+3. Download and install [OpenCV](http://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.0.0/opencv-3.0.0.exe/download).\r\n+4. Unzip the OpenCV package.\r\n+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.\r\n+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\r\n+```C:\\Program Files (x86)\\IntelSWTools\\compilers_and_libraries_2018\\windows\\mkl```.\r\n+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.\r\n+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\\```. \r\n+\r\n+After you have installed all of the required dependencies, build the MXNet source code:\r\n+\r\n+1. Download the MXNet source code from [GitHub](https://github.com/apache/incubator-mxnet). Don't forget to pull the submodules:\r\n+```\r\n+git clone --recursive https://github.com/apache/incubator-mxnet.git\r\n+```\r\n+\r\n+2. Copy file `3rdparty/mkldnn/config_template.vcxproj` to incubator-mxnet root.\r\n+\r\n+3. Start a Visual Studio command prompt.\r\n+\r\n+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 \r\n+[CMake](https://cmake.org/) command:\r\n+```\r\n+mkdir build\r\n+cd build\r\n+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\r\n+```\r\n+\r\n+5. In Visual Studio, open the solution file,```.sln```, and compile it.\r\n+These commands produce a library called ```libmxnet.dll``` in the ```./build/Release/``` or ```./build/Debug``` folder.\r\n+Also ```libmkldnn.dll``` with be in the ```./build/3rdparty/mkldnn/src/Release/```\r\n+\r\n+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.\r\n+\r\n+**Visual Studio 2017**\r\n+\r\n+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:\r\n+\r\n+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.\r\n+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.\r\n+3. Download and install [OpenCV](https://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.4.1/opencv-3.4.1-vc14_vc15.exe/download).\r\n+4. Unzip the OpenCV package.\r\n+5. Set the environment variable ```OpenCV_DIR``` to point to the ```OpenCV build directory``` (e.g., ```OpenCV_DIR = C:\\utils\\opencv\\build```).\r\n+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).\r\n+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```).\r\n+\r\n+After you have installed all of the required dependencies, build the MXNet source code:\r\n+\r\n+1. Start ```cmd``` in windows.\r\n+\r\n+2. Download the MXNet source code from GitHub by using following command:\r\n+\r\n+```r\r\n+cd C:\\\r\n+git clone https://github.com/apache/incubator-mxnet.git 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linux and macos MKLDNN Building 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is a instruction for users to build mxnet with mkldnn from source.\r\n\r\n## Checklist ##\r\n### Essentials ###\r\nPlease feel free to remove inapplicable items for your PR.\r\n- [x] The PR title starts with [MXNET-$JIRA_ID], where $JIRA_ID refers to the relevant [JIRA issue](https://issues.apache.org/jira/projects/MXNET/issues) created (except PRs with tiny changes)\r\n- [x] Changes are complete (i.e. I finished coding on this PR)\r\n- [x] All changes have test coverage:\r\n- Unit tests are added for small changes to verify correctness (e.g. adding a new operator)\r\n- Nightly tests are added for complicated/long-running ones (e.g. changing distributed kvstore)\r\n- Build tests will be added for build configuration changes (e.g. adding a new build option with NCCL)\r\n- [x] Code is well-documented: \r\n- For user-facing API changes, API doc string has been updated. \r\n- For new C++ functions in header files, their functionalities and arguments are documented. \r\n- For new examples, README.md is added to explain the what the example does, the source of the dataset, expected performance on test set and reference to the original paper if applicable\r\n- Check the API doc at http://mxnet-ci-doc.s3-accelerate.dualstack.amazonaws.com/PR-$PR_ID/$BUILD_ID/index.html\r\n- [x] To the my best knowledge, examples are either not affected by this change, or have been fixed to be compatible with this change\r\n\r\n## Known issues ##\r\n\r\nCan not enable opencv when build with gcc on macOS. #11048 \r\n\r\n@pengzhao-intel @zheng-da 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