diff --git a/docs/tutorials/mkldnn/MKLDNN_README.md b/docs/tutorials/mkldnn/MKLDNN_README.md
index 2a7cd40ac291..b3c9198c13d8 100644
--- a/docs/tutorials/mkldnn/MKLDNN_README.md
+++ b/docs/tutorials/mkldnn/MKLDNN_README.md
@@ -1,340 +1,639 @@
-<!--- Licensed to the Apache Software Foundation (ASF) under one -->
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-<!--- "License"); you may not use this file except in compliance -->
-<!--- with the License.  You may obtain a copy of the License at -->
-
-<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
-
-<!--- Unless required by applicable law or agreed to in writing, -->
-<!--- software distributed under the License is distributed on an -->
-<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
-<!--- KIND, either express or implied.  See the License for the -->
-<!--- specific language governing permissions and limitations -->
-<!--- under the License. -->
-
-# Build/Install MXNet with MKL-DNN
-
-A better training and inference performance is expected to be achieved on Intel-Architecture CPUs with MXNet built with [Intel MKL-DNN](https://github.com/intel/mkl-dnn) on multiple operating system, including Linux, Windows and MacOS.
-In the following sections, you will find build instructions for MXNet with Intel MKL-DNN on Linux, MacOS and Windows.
-
-Please find MKL-DNN optimized operators and other features in the [MKL-DNN operator list](../mkldnn/operator_list.md).
-
-The detailed performance data collected on Intel Xeon CPU with MXNet built with Intel MKL-DNN can be found [here](https://mxnet.incubator.apache.org/faq/perf.html#intel-cpu).
-
-
-<h2 id="0">Contents</h2>
-
-* [1. Linux](#1)
-* [2. MacOS](#2)
-* [3. Windows](#3)
-* [4. Verify MXNet with python](#4)
-* [5. Enable MKL BLAS](#5)
-* [6. Enable graph optimization](#6)
-* [7. Quantization](#7)
-* [8. Support](#8)
-
-<h2 id="1">Linux</h2>
-
-### Prerequisites
-
-```
-sudo apt-get update
-sudo apt-get install -y build-essential git
-sudo apt-get install -y libopenblas-dev liblapack-dev
-sudo apt-get install -y libopencv-dev
-sudo apt-get install -y graphviz
-```
-
-### Clone MXNet sources
-
-```
-git clone --recursive https://github.com/apache/incubator-mxnet.git
-cd incubator-mxnet
-```
-
-### Build MXNet with MKL-DNN
-
-```
-make -j $(nproc) USE_OPENCV=1 USE_MKLDNN=1 USE_BLAS=mkl USE_INTEL_PATH=/opt/intel
-```
-
-If you don't have the full [MKL](https://software.intel.com/en-us/intel-mkl) library installation, you might use OpenBLAS as the blas library, by setting USE_BLAS=openblas.
-
-<h2 id="2">MacOS</h2>
-
-### Prerequisites
-
-Install the dependencies, required for MXNet, with the following commands:
-
-- [Homebrew](https://brew.sh/)
-- llvm (clang in macOS does not support OpenMP)
-- OpenCV (for computer vision operations)
-
-```
-# Paste this command in Mac terminal to install Homebrew
-/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
-
-# install dependency
-brew update
-brew install pkg-config
-brew install graphviz
-brew tap homebrew/core
-brew install opencv
-brew tap homebrew/versions
-brew install llvm
-```
-
-### Clone MXNet sources
-
-```
-git clone --recursive https://github.com/apache/incubator-mxnet.git
-cd incubator-mxnet
-```
-
-### Build MXNet with MKL-DNN
-
-```
-LIBRARY_PATH=$(brew --prefix llvm)/lib/ make -j $(sysctl -n hw.ncpu) CC=$(brew --prefix llvm)/bin/clang CXX=$(brew --prefix llvm)/bin/clang++ USE_OPENCV=1 USE_OPENMP=1 USE_MKLDNN=1 USE_BLAS=apple USE_PROFILER=1
-```
-
-<h2 id="3">Windows</h2>
-
-On Windows, you can use [Micrsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/) and [Microsoft Visual Studio 2017](https://www.visualstudio.com/downloads/) to compile MXNet with Intel MKL-DNN.
-[Micrsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/) is recommended.
-
-**Visual Studio 2015**
-
-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 3](https://cmake.org/) if it is not already installed.
-3. Download and install [OpenCV 3](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 --recursive https://github.com/apache/incubator-mxnet.git
-```
-
-2. Copy file `3rdparty/mkldnn/config_template.vcxproj` to incubator-mxnet root.
-
-3. Start a Visual Studio command prompt.
-
-4. Use [CMake 3](https://cmake.org/) to create a Visual Studio solution in ```./build``` or some other directory. Make sure to specify the architecture in the 
-[CMake 3](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.
-
-**Visual Studio 2017**
-
-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 3](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```).
-
-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 --recursive https://github.com/apache/incubator-mxnet.git
-```
-
-3. Copy file `3rdparty/mkldnn/config_template.vcxproj` to incubator-mxnet root.
-
-4. 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```.
-
-5. 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
-```
-
-6. Create a build dir using the following command and go to the directory, for example:
-
-```r
-mkdir C:\build
-cd C:\build
-```
-
-7. CMake the MXNet source code by using following command:
-
-```r
-cmake -G "Visual Studio 15 2017 Win64" .. -T host=x64 -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
-```
-
-8. 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
-```
-
-9. 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.
-
-<h2 id="4">Verify MXNet with python</h2>
-
-```
-cd python
-sudo python setup.py install
-python -c "import mxnet as mx;print((mx.nd.ones((2, 3))*2).asnumpy());"
-
-Expected Output:
-
-[[ 2.  2.  2.]
- [ 2.  2.  2.]]
-```
-
-### Verify whether MKL-DNN works
-
-After MXNet is installed, you can verify if MKL-DNN backend works well with a single Convolution layer.
-
-```
-import mxnet as mx
-import numpy as np
-
-num_filter = 32
-kernel = (3, 3)
-pad = (1, 1)
-shape = (32, 32, 256, 256)
-
-x = mx.sym.Variable('x')
-w = mx.sym.Variable('w')
-y = mx.sym.Convolution(data=x, weight=w, num_filter=num_filter, kernel=kernel, no_bias=True, pad=pad)
-exe = y.simple_bind(mx.cpu(), x=shape)
-
-exe.arg_arrays[0][:] = np.random.normal(size=exe.arg_arrays[0].shape)
-exe.arg_arrays[1][:] = np.random.normal(size=exe.arg_arrays[1].shape)
-
-exe.forward(is_train=False)
-o = exe.outputs[0]
-t = o.asnumpy()
-```
-
-More detailed debugging and profiling information can be logged by setting the environment variable 'MKLDNN_VERBOSE':
-```
-export MKLDNN_VERBOSE=1
-```
-For example, by running above code snippet, the following debugging logs providing more insights on MKL-DNN primitives `convolution` and `reorder`. That includes: Memory layout, infer shape and the time cost of primitive execution.
-```
-mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_nchw out:f32_nChw16c,num:1,32x32x256x256,6.47681
-mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_oihw out:f32_OIhw16i16o,num:1,32x32x3x3,0.0429688
-mkldnn_verbose,exec,convolution,jit:avx512_common,forward_inference,fsrc:nChw16c fwei:OIhw16i16o fbia:undef fdst:nChw16c,alg:convolution_direct,mb32_g1ic32oc32_ih256oh256kh3sh1dh0ph1_iw256ow256kw3sw1dw0pw1,9.98193
-mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_oihw out:f32_OIhw16i16o,num:1,32x32x3x3,0.0510254
-mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_nChw16c out:f32_nchw,num:1,32x32x256x256,20.4819
-```
-
-<h2 id="5">Enable MKL BLAS</h2>
-
-With MKL BLAS, the performace is expected to furtherly improved with variable range depending on the computation load of the models.
-You can redistribute not only dynamic libraries but also headers, examples and static libraries on accepting the license [Intel Simplified license](https://software.intel.com/en-us/license/intel-simplified-software-license).
-Installing 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`
-
-### Verify whether MKL works
-
-After MXNet is installed, you can verify if MKL BLAS works well with a single dot layer.
-
-```
-import mxnet as mx
-import numpy as np
-
-shape_x = (1, 10, 8)
-shape_w = (1, 12, 8)
-
-x_npy = np.random.normal(0, 1, shape_x)
-w_npy = np.random.normal(0, 1, shape_w)
-
-x = mx.sym.Variable('x')
-w = mx.sym.Variable('w')
-y = mx.sym.batch_dot(x, w, transpose_b=True)
-exe = y.simple_bind(mx.cpu(), x=x_npy.shape, w=w_npy.shape)
-
-exe.forward(is_train=False)
-o = exe.outputs[0]
-t = o.asnumpy()
-```
-
-You can open the `MKL_VERBOSE` flag by setting environment variable:
-```
-export MKL_VERBOSE=1
-```
-Then by running above code snippet, you probably will get the following output message which means `SGEMM` primitive from MKL are called. Layout information and primitive execution performance are also demonstrated in the log message.
-```
-Numpy + Intel(R) MKL: THREADING LAYER: (null)
-Numpy + Intel(R) MKL: setting Intel(R) MKL to use INTEL OpenMP runtime
-Numpy + Intel(R) MKL: preloading libiomp5.so runtime
-MKL_VERBOSE Intel(R) MKL 2018.0 Update 1 Product build 20171007 for Intel(R) 64 architecture Intel(R) Advanced Vector Extensions 512 (Intel(R) AVX-512) enabled processors, Lnx 2.40GHz lp64 intel_thread NMICDev:0
-MKL_VERBOSE SGEMM(T,N,12,10,8,0x7f7f927b1378,0x1bc2140,8,0x1ba8040,8,0x7f7f927b1380,0x7f7f7400a280,12) 8.93ms CNR:OFF Dyn:1 FastMM:1 TID:0  NThr:40 WDiv:HOST:+0.000
-```
-
-<h2 id="6">Enable graph optimization</h2>
-
-Graph optimization by subgraph feature are available in master branch. You can build from source and then use below command to enable this *experimental* feature for better performance:
-
-```
-export MXNET_SUBGRAPH_BACKEND=MKLDNN
-```
-
-When `MKLDNN` backend is enabled, advanced control options are avaliable:
-
-```
-export MXNET_DISABLE_MKLDNN_CONV_OPT=1 # disable MKLDNN convolution optimization pass
-export MXNET_DISABLE_MKLDNN_FC_OPT=1 # disable MKLDNN FullyConnected optimization pass
-```
-
-
-This limitations of this experimental feature are:
-
-- Use this feature only for inference. When training, be sure to turn the feature off by unsetting the `MXNET_SUBGRAPH_BACKEND` environment variable.
-
-- This feature will only run on the CPU, even if you're using a GPU-enabled build of MXNet. 
-
-- [MXNet Graph Optimization and Quantization Technical Information and Performance Details](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Graph+Optimization+and+Quantization+based+on+subgraph+and+MKL-DNN).
-
-<h2 id="7">Quantization and Inference with INT8</h2>
-
-Benefiting from Intel MKL-DNN, MXNet built with Intel MKL-DNN brings outstanding performance improvement on quantization and inference with INT8 Intel CPU Platform on Intel Xeon Scalable Platform.
-
-- [CNN Quantization Examples](https://github.com/apache/incubator-mxnet/tree/master/example/quantization).
-
-<h2 id="8">Next Steps and Support</h2>
-
-- For questions or support specific to MKL, visit the [Intel MKL](https://software.intel.com/en-us/mkl) website.
-
-- For questions or support specific to MKL, visit the [Intel MKLDNN](https://github.com/intel/mkl-dnn) website.
-
-- If you find bugs, please open an issue on GitHub for [MXNet with MKL](https://github.com/apache/incubator-mxnet/labels/MKL) or [MXNet with MKLDNN](https://github.com/apache/incubator-mxnet/labels/MKLDNN).
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Build/Install MXNet with MKL-DNN
+
+A better training and inference performance is expected to be achieved on Intel-Architecture CPUs with MXNet built with [Intel MKL-DNN](https://github.com/intel/mkl-dnn) on multiple operating system, including Linux, Windows and MacOS.
+In the following sections, you will find build instructions for MXNet with Intel MKL-DNN on Linux, MacOS and Windows.
+
+Please find MKL-DNN optimized operators and other features in the [MKL-DNN operator list](../mkldnn/operator_list.md).
+
+The detailed performance data collected on Intel Xeon CPU with MXNet built with Intel MKL-DNN can be found [here](https://mxnet.incubator.apache.org/faq/perf.html#intel-cpu).
+
+
+<h2 id="0">Contents</h2>
+
+* [1. Linux](#1)
+* [2. MacOS](#2)
+* [3. Windows](#3)
+* [4. Verify MXNet with python](#4)
+* [5. Enable MKL BLAS](#5)
+* [6. Enable graph optimization](#6)
+* [7. Quantization](#7)
+* [8. Support](#8)
+
+<h2 id="1">Linux</h2>
+
+### Prerequisites
+
+```
+sudo apt-get update
+sudo apt-get install -y build-essential git
+sudo apt-get install -y libopenblas-dev liblapack-dev
+sudo apt-get install -y libopencv-dev
+sudo apt-get install -y graphviz
+```
+
+### Clone MXNet sources
+
+```
+git clone --recursive https://github.com/apache/incubator-mxnet.git
+cd incubator-mxnet
+```
+
+### Build MXNet with MKL-DNN
+
+```
+make -j $(nproc) USE_OPENCV=1 USE_MKLDNN=1 USE_BLAS=mkl USE_INTEL_PATH=/opt/intel
+```
+
+If you don't have the full [MKL](https://software.intel.com/en-us/intel-mkl) library installation, you might use OpenBLAS as the blas library, by setting USE_BLAS=openblas.
+
+<h2 id="2">MacOS</h2>
+
+### Prerequisites
+
+Install the dependencies, required for MXNet, with the following commands:
+
+- [Homebrew](https://brew.sh/)
+- llvm (clang in macOS does not support OpenMP)
+- OpenCV (for computer vision operations)
+
+```
+# Paste this command in Mac terminal to install Homebrew
+/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
+
+# install dependency
+brew update
+brew install pkg-config
+brew install graphviz
+brew tap homebrew/core
+brew install opencv
+brew tap homebrew/versions
+brew install llvm
+```
+
+### Clone MXNet sources
+
+```
+git clone --recursive https://github.com/apache/incubator-mxnet.git
+cd incubator-mxnet
+```
+
+### Build MXNet with MKL-DNN
+
+```
+LIBRARY_PATH=$(brew --prefix llvm)/lib/ make -j $(sysctl -n hw.ncpu) CC=$(brew --prefix llvm)/bin/clang CXX=$(brew --prefix llvm)/bin/clang++ USE_OPENCV=1 USE_OPENMP=1 USE_MKLDNN=1 USE_BLAS=apple USE_PROFILER=1
+```
+
+<h2 id="3">Windows</h2>
+
+On Windows, you can use [Micrsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/) and [Microsoft Visual Studio 2017](https://www.visualstudio.com/downloads/) to compile MXNet with Intel MKL-DNN.
+[Micrsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/) is recommended.  
+
+**Visual Studio 2015**
+
+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 3](https://cmake.org/files/v3.14/cmake-3.14.0-win64-x64.msi) if it is not already installed.
+3. Download [OpenCV 3](https://sourceforge.net/projects/opencvlibrary/files/3.4.5/opencv-3.4.5-vc14_vc15.exe/download), and unzip the OpenCV package, set the environment variable ```OpenCV_DIR``` to point to the ```OpenCV build directory``` (e.g.,```OpenCV_DIR = C:\opencv\build ```). Also, add the OpenCV bin directory (```C:\opencv\build\x64\vc14\bin``` for example) to the ``PATH`` variable.
+4. If you have Intel Math Kernel Library (Intel 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\windows\mkl```.
+5. 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/), or build the latest version of OpenBLAS from source. Note that you should also download ```mingw64.dll.zip``` along with openBLAS and add them to PATH. 
+6. 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:\Downloads\OpenBLAS\```. 
+
+After you have installed all of the required dependencies, build the MXNet source code:
+
+1. Start a Visual Studio command prompt by click windows Start menu>>Visual Studio 2015>>VS2015 X64 Native Tools Command Prompt, and download the MXNet source code from [GitHub](https://github.com/apache/incubator-mxnet) by the command:
+```
+git clone --recursive https://github.com/apache/incubator-mxnet.git
+cd C:\incubator-mxent
+```
+2. Enable Intel MKL-DNN by -DUSE_MKLDNN=1. Use [CMake 3](https://cmake.org/) to create a Visual Studio solution in ```./build```. Make sure to specify the architecture in the 
+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
+```
+3. Enable Intel MKL-DNN and Intel MKL as BLAS library by the command:  
+```
+>"C:\Program Files (x86)\IntelSWTools\compilers_and_libraries\windows\mkl\bin\mklvars.bat" intel64
+>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=mkl -DUSE_LAPACK=1 -DUSE_DIST_KVSTORE=0 -DCUDA_ARCH_NAME=All -DUSE_MKLDNN=1 -DCMAKE_BUILD_TYPE=Release -DMKL_ROOT="C:\Program Files (x86)\IntelSWTools\compilers_and_libraries\windows\mkl" 
+```
+4. After the CMake successfully completed, in Visual Studio, open the solution file ```.sln``` and compile it, or compile the the MXNet source code by using following command:
+```r
+msbuild mxnet.sln /p:Configuration=Release;Platform=x64 /maxcpucount
+```
+   These commands produce mxnet library called ```libmxnet.dll``` in the ```./build/Release/``` or ```./build/Debug``` folder. Also ```libmkldnn.dll``` with be in the ```./build/3rdparty/mkldnn/src/Release/```
+
+5. 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.
+
+**Visual Studio 2017**
+
+User can follow the same steps of Visual Studio 2015 to build MXNET with MKL-DNN, but change the version related command, for example,```C:\opencv\build\x64\vc15\bin``` and build command is as below:
+
+```
+>cmake -G "Visual Studio 15 Win64" .. -DUSE_CUDA=0 -DUSE_CUDNN=0 -DUSE_NVRTC=0 -DUSE_OPENCV=1 -DUSE_OPENMP=1 -DUSE_PROFILER=1 -DUSE_BLAS=mkl -DUSE_LAPACK=1 -DUSE_DIST_KVSTORE=0 -DCUDA_ARCH_NAME=All -DUSE_MKLDNN=1 -DCMAKE_BUILD_TYPE=Release -DMKL_ROOT="C:\Program Files (x86)\IntelSWTools\compilers_and_libraries\windows\mkl"
+
+```
+
+<h2 id="4">Verify MXNet with python</h2>
+
+Preinstall python and some dependent modules: 
+```
+pip install numpy graphviz
+set PYTHONPATH=[workdir]\incubator-mxnet\python
+```
+or install mxnet
+```
+cd python
+sudo python setup.py install
+python -c "import mxnet as mx;print((mx.nd.ones((2, 3))*2).asnumpy());"
+```
+Expected Output:
+```
+[[ 2.  2.  2.]
+ [ 2.  2.  2.]]
+```
+### Verify whether MKL-DNN works
+
+After MXNet is installed, you can verify if MKL-DNN backend works well with a single Convolution layer.
+```
+import mxnet as mx
+import numpy as np
+
+num_filter = 32
+kernel = (3, 3)
+pad = (1, 1)
+shape = (32, 32, 256, 256)
+
+x = mx.sym.Variable('x')
+w = mx.sym.Variable('w')
+y = mx.sym.Convolution(data=x, weight=w, num_filter=num_filter, kernel=kernel, no_bias=True, pad=pad)
+exe = y.simple_bind(mx.cpu(), x=shape)
+
+exe.arg_arrays[0][:] = np.random.normal(size=exe.arg_arrays[0].shape)
+exe.arg_arrays[1][:] = np.random.normal(size=exe.arg_arrays[1].shape)
+
+exe.forward(is_train=False)
+o = exe.outputs[0]
+t = o.asnumpy()
+```
+
+More detailed debugging and profiling information can be logged by setting the environment variable 'MKLDNN_VERBOSE':
+```
+export MKLDNN_VERBOSE=1
+```
+For example, by running above code snippet, the following debugging logs providing more insights on MKL-DNN primitives `convolution` and `reorder`. That includes: Memory layout, infer shape and the time cost of primitive execution.
+```
+mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_nchw out:f32_nChw16c,num:1,32x32x256x256,6.47681
+mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_oihw out:f32_OIhw16i16o,num:1,32x32x3x3,0.0429688
+mkldnn_verbose,exec,convolution,jit:avx512_common,forward_inference,fsrc:nChw16c fwei:OIhw16i16o fbia:undef fdst:nChw16c,alg:convolution_direct,mb32_g1ic32oc32_ih256oh256kh3sh1dh0ph1_iw256ow256kw3sw1dw0pw1,9.98193
+mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_oihw out:f32_OIhw16i16o,num:1,32x32x3x3,0.0510254
+mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_nChw16c out:f32_nchw,num:1,32x32x256x256,20.4819
+```
+
+<h2 id="5">Enable MKL BLAS</h2>
+
+With MKL BLAS, the performace is expected to furtherly improved with variable range depending on the computation load of the models.
+You can redistribute not only dynamic libraries but also headers, examples and static libraries on accepting the license [Intel Simplified license](https://software.intel.com/en-us/license/intel-simplified-software-license).
+Installing 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`
+
+### Verify whether MKL works
+
+After MXNet is installed, you can verify if MKL BLAS works well with a single dot layer.
+
+```
+import mxnet as mx
+import numpy as np
+
+shape_x = (1, 10, 8)
+shape_w = (1, 12, 8)
+
+x_npy = np.random.normal(0, 1, shape_x)
+w_npy = np.random.normal(0, 1, shape_w)
+
+x = mx.sym.Variable('x')
+w = mx.sym.Variable('w')
+y = mx.sym.batch_dot(x, w, transpose_b=True)
+exe = y.simple_bind(mx.cpu(), x=x_npy.shape, w=w_npy.shape)
+
+exe.forward(is_train=False)
+o = exe.outputs[0]
+t = o.asnumpy()
+```
+
+You can open the `MKL_VERBOSE` flag by setting environment variable:
+```
+export MKL_VERBOSE=1
+```
+Then by running above code snippet, you probably will get the following output message which means `SGEMM` primitive from MKL are called. Layout information and primitive execution performance are also demonstrated in the log message.
+```
+Numpy + Intel(R) MKL: THREADING LAYER: (null)
+Numpy + Intel(R) MKL: setting Intel(R) MKL to use INTEL OpenMP runtime
+Numpy + Intel(R) MKL: preloading libiomp5.so runtime
+MKL_VERBOSE Intel(R) MKL 2019.0 Update 3 Product build 20190125 for Intel(R) 64 architecture Intel(R) Advanced Vector Extensions 512 (Intel(R) AVX-512) enabled processors, Lnx 2.40GHz lp64 intel_thread NMICDev:0
+MKL_VERBOSE SGEMM(T,N,12,10,8,0x7f7f927b1378,0x1bc2140,8,0x1ba8040,8,0x7f7f927b1380,0x7f7f7400a280,12) 8.93ms CNR:OFF Dyn:1 FastMM:1 TID:0  NThr:40 WDiv:HOST:+0.000
+```
+
+<h2 id="6">Enable graph optimization</h2>
+
+Graph optimization by subgraph feature are available in master branch. You can build from source and then use below command to enable this *experimental* feature for better performance:
+
+```
+export MXNET_SUBGRAPH_BACKEND=MKLDNN
+```
+
+When `MKLDNN` backend is enabled, advanced control options are avaliable:
+
+```
+export MXNET_DISABLE_MKLDNN_CONV_OPT=1 # disable MKLDNN convolution optimization pass
+export MXNET_DISABLE_MKLDNN_FC_OPT=1 # disable MKLDNN FullyConnected optimization pass
+```
+
+
+This limitations of this experimental feature are:
+
+- Use this feature only for inference. When training, be sure to turn the feature off by unsetting the `MXNET_SUBGRAPH_BACKEND` environment variable.
+
+- This feature will only run on the CPU, even if you're using a GPU-enabled build of MXNet. 
+
+- [MXNet Graph Optimization and Quantization Technical Information and Performance Details](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Graph+Optimization+and+Quantization+based+on+subgraph+and+MKL-DNN).
+
+<h2 id="7">Quantization and Inference with INT8</h2>
+
+Benefiting from Intel MKL-DNN, MXNet built with Intel MKL-DNN brings outstanding performance improvement on quantization and inference with INT8 Intel CPU Platform on Intel Xeon Scalable Platform.
+
+- [CNN Quantization Examples](https://github.com/apache/incubator-mxnet/tree/master/example/quantization).
+
+<h2 id="8">Next Steps and Support</h2>
+
+- For questions or support specific to MKL, visit the [Intel MKL](https://software.intel.com/en-us/mkl) website.
+
+- For questions or support specific to MKL, visit the [Intel MKLDNN](https://github.com/intel/mkl-dnn) website.
+
+- If you find bugs, please open an issue on GitHub for [MXNet with MKL](https://github.com/apache/incubator-mxnet/labels/MKL) or [MXNet with MKLDNN](https://github.com/apache/incubator-mxnet/labels/MKLDNN).
+=======
+<!--- Licensed to the Apache Software Foundation (ASF) under one -->
+<!--- or more contributor license agreements.  See the NOTICE file -->
+<!--- distributed with this work for additional information -->
+<!--- regarding copyright ownership.  The ASF licenses this file -->
+<!--- to you under the Apache License, Version 2.0 (the -->
+<!--- "License"); you may not use this file except in compliance -->
+<!--- with the License.  You may obtain a copy of the License at -->
+
+<!---   http://www.apache.org/licenses/LICENSE-2.0 -->
+
+<!--- Unless required by applicable law or agreed to in writing, -->
+<!--- software distributed under the License is distributed on an -->
+<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
+<!--- KIND, either express or implied.  See the License for the -->
+<!--- specific language governing permissions and limitations -->
+<!--- under the License. -->
+
+# Build/Install MXNet with MKL-DNN
+
+A better training and inference performance is expected to be achieved on Intel-Architecture CPUs with MXNet built with [Intel MKL-DNN](https://github.com/intel/mkl-dnn) on multiple operating system, including Linux, Windows and MacOS.
+In the following sections, you will find build instructions for MXNet with Intel MKL-DNN on Linux, MacOS and Windows.
+
+Please find MKL-DNN optimized operators and other features in the [MKL-DNN operator list](../mkldnn/operator_list.md).
+
+The detailed performance data collected on Intel Xeon CPU with MXNet built with Intel MKL-DNN can be found [here](https://mxnet.incubator.apache.org/faq/perf.html#intel-cpu).
+
+
+<h2 id="0">Contents</h2>
+
+* [1. Linux](#1)
+* [2. MacOS](#2)
+* [3. Windows](#3)
+* [4. Verify MXNet with python](#4)
+* [5. Enable MKL BLAS](#5)
+* [6. Enable graph optimization](#6)
+* [7. Quantization](#7)
+* [8. Support](#8)
+
+<h2 id="1">Linux</h2>
+
+### Prerequisites
+
+```
+sudo apt-get update
+sudo apt-get install -y build-essential git
+sudo apt-get install -y libopenblas-dev liblapack-dev
+sudo apt-get install -y libopencv-dev
+sudo apt-get install -y graphviz
+```
+
+### Clone MXNet sources
+
+```
+git clone --recursive https://github.com/apache/incubator-mxnet.git
+cd incubator-mxnet
+```
+
+### Build MXNet with MKL-DNN
+
+```
+make -j $(nproc) USE_OPENCV=1 USE_MKLDNN=1 USE_BLAS=mkl USE_INTEL_PATH=/opt/intel
+```
+
+If you don't have the full [MKL](https://software.intel.com/en-us/intel-mkl) library installation, you might use OpenBLAS as the blas library, by setting USE_BLAS=openblas.
+
+<h2 id="2">MacOS</h2>
+
+### Prerequisites
+
+Install the dependencies, required for MXNet, with the following commands:
+
+- [Homebrew](https://brew.sh/)
+- llvm (clang in macOS does not support OpenMP)
+- OpenCV (for computer vision operations)
+
+```
+# Paste this command in Mac terminal to install Homebrew
+/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
+
+# install dependency
+brew update
+brew install pkg-config
+brew install graphviz
+brew tap homebrew/core
+brew install opencv
+brew tap homebrew/versions
+brew install llvm
+```
+
+### Clone MXNet sources
+
+```
+git clone --recursive https://github.com/apache/incubator-mxnet.git
+cd incubator-mxnet
+```
+
+### Build MXNet with MKL-DNN
+
+```
+LIBRARY_PATH=$(brew --prefix llvm)/lib/ make -j $(sysctl -n hw.ncpu) CC=$(brew --prefix llvm)/bin/clang CXX=$(brew --prefix llvm)/bin/clang++ USE_OPENCV=1 USE_OPENMP=1 USE_MKLDNN=1 USE_BLAS=apple USE_PROFILER=1
+```
+
+<h2 id="3">Windows</h2>
+
+On Windows, you can use [Micrsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/) and [Microsoft Visual Studio 2017](https://www.visualstudio.com/downloads/) to compile MXNet with Intel MKL-DNN.
+[Micrsoft Visual Studio 2015](https://www.visualstudio.com/vs/older-downloads/) is recommended.
+
+**Visual Studio 2015**
+
+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 3](https://cmake.org/) if it is not already installed.
+3. Download and install [OpenCV 3](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 --recursive https://github.com/apache/incubator-mxnet.git
+```
+
+2. Copy file `3rdparty/mkldnn/config_template.vcxproj` to incubator-mxnet root.
+
+3. Start a Visual Studio command prompt.
+
+4. Use [CMake 3](https://cmake.org/) to create a Visual Studio solution in ```./build``` or some other directory. Make sure to specify the architecture in the 
+[CMake 3](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.
+
+**Visual Studio 2017**
+
+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 3](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```).
+
+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 --recursive https://github.com/apache/incubator-mxnet.git
+```
+
+3. Copy file `3rdparty/mkldnn/config_template.vcxproj` to incubator-mxnet root.
+
+4. 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```.
+
+5. 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
+```
+
+6. Create a build dir using the following command and go to the directory, for example:
+
+```r
+mkdir C:\build
+cd C:\build
+```
+
+7. CMake the MXNet source code by using following command:
+
+```r
+cmake -G "Visual Studio 15 2017 Win64" .. -T host=x64 -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
+```
+
+8. 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
+```
+
+9. 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.
+
+<h2 id="4">Verify MXNet with python</h2>
+
+```
+cd python
+sudo python setup.py install
+python -c "import mxnet as mx;print((mx.nd.ones((2, 3))*2).asnumpy());"
+
+Expected Output:
+
+[[ 2.  2.  2.]
+ [ 2.  2.  2.]]
+```
+
+### Verify whether MKL-DNN works
+
+After MXNet is installed, you can verify if MKL-DNN backend works well with a single Convolution layer.
+
+```
+import mxnet as mx
+import numpy as np
+
+num_filter = 32
+kernel = (3, 3)
+pad = (1, 1)
+shape = (32, 32, 256, 256)
+
+x = mx.sym.Variable('x')
+w = mx.sym.Variable('w')
+y = mx.sym.Convolution(data=x, weight=w, num_filter=num_filter, kernel=kernel, no_bias=True, pad=pad)
+exe = y.simple_bind(mx.cpu(), x=shape)
+
+exe.arg_arrays[0][:] = np.random.normal(size=exe.arg_arrays[0].shape)
+exe.arg_arrays[1][:] = np.random.normal(size=exe.arg_arrays[1].shape)
+
+exe.forward(is_train=False)
+o = exe.outputs[0]
+t = o.asnumpy()
+```
+
+More detailed debugging and profiling information can be logged by setting the environment variable 'MKLDNN_VERBOSE':
+```
+export MKLDNN_VERBOSE=1
+```
+For example, by running above code snippet, the following debugging logs providing more insights on MKL-DNN primitives `convolution` and `reorder`. That includes: Memory layout, infer shape and the time cost of primitive execution.
+```
+mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_nchw out:f32_nChw16c,num:1,32x32x256x256,6.47681
+mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_oihw out:f32_OIhw16i16o,num:1,32x32x3x3,0.0429688
+mkldnn_verbose,exec,convolution,jit:avx512_common,forward_inference,fsrc:nChw16c fwei:OIhw16i16o fbia:undef fdst:nChw16c,alg:convolution_direct,mb32_g1ic32oc32_ih256oh256kh3sh1dh0ph1_iw256ow256kw3sw1dw0pw1,9.98193
+mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_oihw out:f32_OIhw16i16o,num:1,32x32x3x3,0.0510254
+mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_nChw16c out:f32_nchw,num:1,32x32x256x256,20.4819
+```
+
+<h2 id="5">Enable MKL BLAS</h2>
+
+With MKL BLAS, the performace is expected to furtherly improved with variable range depending on the computation load of the models.
+You can redistribute not only dynamic libraries but also headers, examples and static libraries on accepting the license [Intel Simplified license](https://software.intel.com/en-us/license/intel-simplified-software-license).
+Installing 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`
+
+### Verify whether MKL works
+
+After MXNet is installed, you can verify if MKL BLAS works well with a single dot layer.
+
+```
+import mxnet as mx
+import numpy as np
+
+shape_x = (1, 10, 8)
+shape_w = (1, 12, 8)
+
+x_npy = np.random.normal(0, 1, shape_x)
+w_npy = np.random.normal(0, 1, shape_w)
+
+x = mx.sym.Variable('x')
+w = mx.sym.Variable('w')
+y = mx.sym.batch_dot(x, w, transpose_b=True)
+exe = y.simple_bind(mx.cpu(), x=x_npy.shape, w=w_npy.shape)
+
+exe.forward(is_train=False)
+o = exe.outputs[0]
+t = o.asnumpy()
+```
+
+You can open the `MKL_VERBOSE` flag by setting environment variable:
+```
+export MKL_VERBOSE=1
+```
+Then by running above code snippet, you probably will get the following output message which means `SGEMM` primitive from MKL are called. Layout information and primitive execution performance are also demonstrated in the log message.
+```
+Numpy + Intel(R) MKL: THREADING LAYER: (null)
+Numpy + Intel(R) MKL: setting Intel(R) MKL to use INTEL OpenMP runtime
+Numpy + Intel(R) MKL: preloading libiomp5.so runtime
+MKL_VERBOSE Intel(R) MKL 2018.0 Update 1 Product build 20171007 for Intel(R) 64 architecture Intel(R) Advanced Vector Extensions 512 (Intel(R) AVX-512) enabled processors, Lnx 2.40GHz lp64 intel_thread NMICDev:0
+MKL_VERBOSE SGEMM(T,N,12,10,8,0x7f7f927b1378,0x1bc2140,8,0x1ba8040,8,0x7f7f927b1380,0x7f7f7400a280,12) 8.93ms CNR:OFF Dyn:1 FastMM:1 TID:0  NThr:40 WDiv:HOST:+0.000
+```
+
+<h2 id="6">Enable graph optimization</h2>
+
+Graph optimization by subgraph feature are available in master branch. You can build from source and then use below command to enable this *experimental* feature for better performance:
+
+```
+export MXNET_SUBGRAPH_BACKEND=MKLDNN
+```
+
+When `MKLDNN` backend is enabled, advanced control options are avaliable:
+
+```
+export MXNET_DISABLE_MKLDNN_CONV_OPT=1 # disable MKLDNN convolution optimization pass
+export MXNET_DISABLE_MKLDNN_FC_OPT=1 # disable MKLDNN FullyConnected optimization pass
+```
+
+
+This limitations of this experimental feature are:
+
+- Use this feature only for inference. When training, be sure to turn the feature off by unsetting the `MXNET_SUBGRAPH_BACKEND` environment variable.
+
+- This feature will only run on the CPU, even if you're using a GPU-enabled build of MXNet. 
+
+- [MXNet Graph Optimization and Quantization Technical Information and Performance Details](https://medium.com/apache-mxnet/model-quantization-for-production-level-neural-network-inference-f54462ebba05).
+
+<h2 id="7">Quantization and Inference with INT8</h2>
+
+Benefiting from Intel MKL-DNN, MXNet built with Intel MKL-DNN brings outstanding performance improvement on quantization and inference with INT8 Intel CPU Platform on Intel Xeon Scalable Platform.
+
+- [CNN Quantization Examples](https://github.com/apache/incubator-mxnet/tree/master/example/quantization).
+
+<h2 id="8">Next Steps and Support</h2>
+
+- For questions or support specific to MKL, visit the [Intel MKL](https://software.intel.com/en-us/mkl) website.
+
+- For questions or support specific to MKL, visit the [Intel MKLDNN](https://github.com/intel/mkl-dnn) website.
+
+- If you find bugs, please open an issue on GitHub for [MXNet with MKL](https://github.com/apache/incubator-mxnet/labels/MKL) or [MXNet with MKLDNN](https://github.com/apache/incubator-mxnet/labels/MKLDNN).
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