From 74be568fd402e0942930016a3ef2ed2a928b1584 Mon Sep 17 00:00:00 2001
From: xinyu-intel <xinyu1.chen@intel.com>
Date: Thu, 24 May 2018 19:27:09 +0800
Subject: [PATCH 01/16] add linux and macos doc

---
 MKL_README.md | 104 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 104 insertions(+)

diff --git a/MKL_README.md b/MKL_README.md
index a5c63b097c5e..1a3b82829601 100644
--- a/MKL_README.md
+++ b/MKL_README.md
@@ -12,6 +12,110 @@ Installing and enabling the full MKL installation enables MKL support for all op
 
   4. Run 'sudo python setup.py install'
 
+## Build/Install MXNet with MKLDNN on Linux:
+
+### Ubuntu pre-req
+
+```
+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
+```
+
+### Clone MXNet sources
+
+```
+git clone --recursive https://github.com/apache/incubator-mxnet.git
+cd incubator-mxnet
+git submodule update --recursive --init
+```
+
+### Build MXNet with MKLDNN
+
+```
+make -j $(nproc) USE_OPENCV=1 USE_MKLDNN=1 USE_BLAS=openblas USE_PROFILER=1
+```
+
+If you want to use Intel MKL blas, you can set `USE_BLAS=mkl USE_INTEL_PATH=/opt/intel`.
+
+### Verify MXNet with python
+
+```
+export PYTHONPATH=~/incubator-mxnet/python
+pip install --upgrade pip 
+pip install --upgrade jupyter graphviz cython pandas bokeh matplotlib opencv-python requests
+python -c "import mxnet as mx;print((mx.nd.ones((2, 3))*2).asnumpy());"
+
+Expected Output:
+
+[[ 2.  2.  2.]
+ [ 2.  2.  2.]]
+```
+
+## Build/Install MXNet with MKLDNN on MacOS:
+
+### MacOS pre-req
+
+Install the dependencies, required for MXNet, with the following commands:
+
+- Homebrew
+- gcc (clang in macOS does not support openMP)
+- OpenCV (for computer vision operations)
+
+```
+# Paste this command in Mac terminal to install Homebrew
+/usr/bin/ruby -e "$(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 gcc49
+brew link gcc49
+```
+
+### Modify Makefile to enable openMP for MacOS
+
+If you want to enable openMP for better performance, you should modify these two file:
+
+1. Makefile L138:
+
+```
+ifeq ($(USE_OPENMP), 1)
+	# ifneq ($(UNAME_S), Darwin)
+		CFLAGS += -fopenmp
+	# endif
+endif
+```
+
+2. prepare_mkldnn.sh L96:
+
+```
+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
+```
+
+### Build MXNet with MKLDNN
+
+```
+make -j $(sysctl -n hw.ncpu) USE_OPENCV=0 USE_OPENMP=1 USE_MKLDNN=1 USE_BLAS=apple USE_PROFILER=1
+```
+
+*Note: Temporarily disable OPENCV.*
+
+### Verify MXNet with python
+
+```
+export PYTHONPATH=~/incubator-mxnet/python
+pip install --upgrade pip 
+pip install --upgrade jupyter graphviz cython pandas bokeh matplotlib opencv-python requests
+python -c "import mxnet as mx;print((mx.nd.ones((2, 3))*2).asnumpy());"
+
+Expected Output:
+
+[[ 2.  2.  2.]
+ [ 2.  2.  2.]]
+```
 
 ## Build/Install MXNet with MKLDNN on Windows:
 

From 5966cb5ffaefc5fb406426da1a1f729188203186 Mon Sep 17 00:00:00 2001
From: xinyu-intel <xinyu1.chen@intel.com>
Date: Thu, 24 May 2018 22:44:54 +0800
Subject: [PATCH 02/16] update doc

---
 MKL_README.md | 106 +++++++++++++++++++-------------------------------
 1 file changed, 40 insertions(+), 66 deletions(-)

diff --git a/MKL_README.md b/MKL_README.md
index 1a3b82829601..6d81249ae29d 100644
--- a/MKL_README.md
+++ b/MKL_README.md
@@ -1,20 +1,12 @@
-## Build/Install MXNet with a full MKL installation:
+* [1. Build/Install MXNet with MKLDNN on Linux](#1)
+* [2. Build/Install MXNet with MKLDNN on MacOS](#2)
+* [3. Build/Install MXNet with MKLDNN on Windows](#3)
+* [4. Verify MXNet with python](#4)
+* [5. Build/Install MXNet with a full MKL installation](#5)
 
-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'
+<h2 id="1">Build/Install MXNet with MKLDNN on Linux</h2>
 
-  3. Navigate into the python directory
-
-  4. Run 'sudo python setup.py install'
-
-## Build/Install MXNet with MKLDNN on Linux:
-
-### Ubuntu pre-req
+### Prerequisites
 
 ```
 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
@@ -36,23 +28,9 @@ make -j $(nproc) USE_OPENCV=1 USE_MKLDNN=1 USE_BLAS=openblas USE_PROFILER=1
 
 If you want to use Intel MKL blas, you can set `USE_BLAS=mkl USE_INTEL_PATH=/opt/intel`.
 
-### Verify MXNet with python
-
-```
-export PYTHONPATH=~/incubator-mxnet/python
-pip install --upgrade pip 
-pip install --upgrade jupyter graphviz cython pandas bokeh matplotlib opencv-python requests
-python -c "import mxnet as mx;print((mx.nd.ones((2, 3))*2).asnumpy());"
-
-Expected Output:
-
-[[ 2.  2.  2.]
- [ 2.  2.  2.]]
-```
+<h2 id="2">Build/Install MXNet with MKLDNN on MacOS</h2>
 
-## Build/Install MXNet with MKLDNN on MacOS:
-
-### MacOS pre-req
+### Prerequisites
 
 Install the dependencies, required for MXNet, with the following commands:
 
@@ -75,9 +53,9 @@ brew install gcc49
 brew link gcc49
 ```
 
-### Modify Makefile to enable openMP for MacOS
+### Enable openMP for MacOS
 
-If you want to enable openMP for better performance, you should modify these two file:
+If you want to enable openMP for better performance, you should modify these two files:
 
 1. Makefile L138:
 
@@ -103,21 +81,7 @@ make -j $(sysctl -n hw.ncpu) USE_OPENCV=0 USE_OPENMP=1 USE_MKLDNN=1 USE_BLAS=app
 
 *Note: Temporarily disable OPENCV.*
 
-### Verify MXNet with python
-
-```
-export PYTHONPATH=~/incubator-mxnet/python
-pip install --upgrade pip 
-pip install --upgrade jupyter graphviz cython pandas bokeh matplotlib opencv-python requests
-python -c "import mxnet as mx;print((mx.nd.ones((2, 3))*2).asnumpy());"
-
-Expected Output:
-
-[[ 2.  2.  2.]
- [ 2.  2.  2.]]
-```
-
-## Build/Install MXNet with MKLDNN on Windows:
+<h2 id="3">Build/Install MXNet with MKLDNN on Windows</h2>
 
 To build and install MXNet yourself, you need the following dependencies. Install the required dependencies:
 
@@ -156,26 +120,36 @@ 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
+<h2 id="4">Verify MXNet with python</h2>
 
-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).
+```
+export PYTHONPATH=~/incubator-mxnet/python
+pip install --upgrade pip 
+pip install --upgrade jupyter graphviz cython pandas bokeh matplotlib opencv-python requests
+python -c "import mxnet as mx;print((mx.nd.ones((2, 3))*2).asnumpy());"
 
-```CMD
-    cd python
-    python setup.py install
+Expected Output:
+
+[[ 2.  2.  2.]
+ [ 2.  2.  2.]]
 ```
-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.]]
+### Verify whether MKLDNN works:
+
 ```
-We actually did a small tensor computation using MXNet! You are all set with MKLDNN MXNet on your Windows machine.
+WIP
+```
+
+<h2 id="5">Build/Install MXNet with a full MKL installation</h2>
+
+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'
\ No newline at end of file

From a87d357a2e12ffc95c168fad9f4949826f572186 Mon Sep 17 00:00:00 2001
From: Tao Lv <tao.a.lv@intel.com>
Date: Thu, 24 May 2018 23:00:22 +0800
Subject: [PATCH 03/16] Update MKL_README.md

---
 MKL_README.md | 40 +++++++++++++++++++++-------------------
 1 file changed, 21 insertions(+), 19 deletions(-)

diff --git a/MKL_README.md b/MKL_README.md
index 6d81249ae29d..89d5e7f6d041 100644
--- a/MKL_README.md
+++ b/MKL_README.md
@@ -1,10 +1,12 @@
-* [1. Build/Install MXNet with MKLDNN on Linux](#1)
-* [2. Build/Install MXNet with MKLDNN on MacOS](#2)
-* [3. Build/Install MXNet with MKLDNN on Windows](#3)
+<h2 id="0">Contents</h2>
+
+* [1. Build/Install MXNet with MKL-DNN on Linux](#1)
+* [2. Build/Install MXNet with MKL-DNN on MacOS](#2)
+* [3. Build/Install MXNet with MKL-DNN on Windows](#3)
 * [4. Verify MXNet with python](#4)
 * [5. Build/Install MXNet with a full MKL installation](#5)
 
-<h2 id="1">Build/Install MXNet with MKLDNN on Linux</h2>
+<h2 id="1">Build/Install MXNet with MKL-DNN on Linux</h2>
 
 ### Prerequisites
 
@@ -20,22 +22,22 @@ cd incubator-mxnet
 git submodule update --recursive --init
 ```
 
-### Build MXNet with MKLDNN
+### Build MXNet with MKL-DNN
 
 ```
-make -j $(nproc) USE_OPENCV=1 USE_MKLDNN=1 USE_BLAS=openblas USE_PROFILER=1
+make -j $(nproc) USE_OPENCV=1 USE_MKLDNN=1 USE_BLAS=mkl USE_INTEL_PATH=/opt/intel
 ```
 
-If you want to use Intel MKL blas, you can set `USE_BLAS=mkl USE_INTEL_PATH=/opt/intel`.
+If you don't have full MKL library installed, you can use OpenBLAS by setting `USE_BLAS=openblas`.
 
-<h2 id="2">Build/Install MXNet with MKLDNN on MacOS</h2>
+<h2 id="2">Build/Install MXNet with MKL-DNN on MacOS</h2>
 
 ### Prerequisites
 
 Install the dependencies, required for MXNet, with the following commands:
 
 - Homebrew
-- gcc (clang in macOS does not support openMP)
+- gcc (clang in macOS does not support OpenMP)
 - OpenCV (for computer vision operations)
 
 ```
@@ -53,9 +55,9 @@ brew install gcc49
 brew link gcc49
 ```
 
-### Enable openMP for MacOS
+### Enable OpenMP for MacOS
 
-If you want to enable openMP for better performance, you should modify these two files:
+If you want to enable OpenMP for better performance, you should modify these two files:
 
 1. Makefile L138:
 
@@ -73,7 +75,7 @@ endif
 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
 ```
 
-### Build MXNet with MKLDNN
+### Build MXNet with MKL-DNN
 
 ```
 make -j $(sysctl -n hw.ncpu) USE_OPENCV=0 USE_OPENMP=1 USE_MKLDNN=1 USE_BLAS=apple USE_PROFILER=1
@@ -81,7 +83,7 @@ make -j $(sysctl -n hw.ncpu) USE_OPENCV=0 USE_OPENMP=1 USE_MKLDNN=1 USE_BLAS=app
 
 *Note: Temporarily disable OPENCV.*
 
-<h2 id="3">Build/Install MXNet with MKLDNN on Windows</h2>
+<h2 id="3">Build/Install MXNet with MKL-DNN on Windows</h2>
 
 To build and install MXNet yourself, you need the following dependencies. Install the required dependencies:
 
@@ -99,7 +101,7 @@ After you have installed all of the required dependencies, build the MXNet sourc
 
 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
+git clone https://github.com/apache/incubator-mxnet.git --recursive
 ```
 
 2. Copy file `3rdparty/mkldnn/config_template.vcxproj` to incubator-mxnet root.
@@ -109,9 +111,9 @@ After you have installed all of the required dependencies, build the MXNet sourc
 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
+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.
@@ -134,7 +136,7 @@ Expected Output:
  [ 2.  2.  2.]]
 ```
 
-### Verify whether MKLDNN works:
+### Verify whether MKL-DNN works:
 
 ```
 WIP
@@ -152,4 +154,4 @@ Installing and enabling the full MKL installation enables MKL support for all op
 
   3. Navigate into the python directory
 
-  4. Run 'sudo python setup.py install'
\ No newline at end of file
+  4. Run 'sudo python setup.py install'

From 5a73eea990e7e6ffef961c05ac4eb4943aa10073 Mon Sep 17 00:00:00 2001
From: Tao Lv <tao.a.lv@intel.com>
Date: Thu, 24 May 2018 23:22:09 +0800
Subject: [PATCH 04/16] Update MKL_README.md

Add convolution code to verify mkldnn backend
---
 MKL_README.md | 38 ++++++++++++++++++++++++++++++++++++--
 1 file changed, 36 insertions(+), 2 deletions(-)

diff --git a/MKL_README.md b/MKL_README.md
index 89d5e7f6d041..41b4fca3f2d6 100644
--- a/MKL_README.md
+++ b/MKL_README.md
@@ -136,10 +136,44 @@ Expected Output:
  [ 2.  2.  2.]]
 ```
 
-### Verify whether MKL-DNN works:
+### 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
+from mxnet import Context
+
+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(Context.default_ctx, 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()
+```
+
+You can open the `MKLDNN_VERBOSE` flag by setting environment variable:
+```
+export MKLDNN_VERBOSE=1
+```
+Then by running above code snippet, you probably will get the following output message which means `convolution` and `reorder` primitive from MKL-DNN are called. Layout information and primitive execution performance are also demonstrated in the log message.
 ```
-WIP
+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">Build/Install MXNet with a full MKL installation</h2>

From 46e5cea4e49505e9cc167fa67ac14f7226a6e658 Mon Sep 17 00:00:00 2001
From: xinyu-intel <xinyu1.chen@intel.com>
Date: Fri, 25 May 2018 14:29:05 +0800
Subject: [PATCH 05/16] add homebrew link

---
 MKL_README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/MKL_README.md b/MKL_README.md
index 41b4fca3f2d6..15f0c8c0ee74 100644
--- a/MKL_README.md
+++ b/MKL_README.md
@@ -36,7 +36,7 @@ If you don't have full MKL library installed, you can use OpenBLAS by setting `U
 
 Install the dependencies, required for MXNet, with the following commands:
 
-- Homebrew
+- [Homebrew](https://brew.sh/)
 - gcc (clang in macOS does not support OpenMP)
 - OpenCV (for computer vision operations)
 

From 3f3259ffe7ad8f63a7778a7c079f7f461ac62e6a Mon Sep 17 00:00:00 2001
From: xinyu-intel <xinyu1.chen@intel.com>
Date: Sat, 26 May 2018 11:19:28 +0800
Subject: [PATCH 06/16] rename to MKLDNN_README

---
 MKL_README.md => MKLDNN_README.md | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename MKL_README.md => MKLDNN_README.md (100%)

diff --git a/MKL_README.md b/MKLDNN_README.md
similarity index 100%
rename from MKL_README.md
rename to MKLDNN_README.md

From 2c503c25b2e8df40cb9d800e8f2e9f18d7db1a11 Mon Sep 17 00:00:00 2001
From: xinyu-intel <xinyu1.chen@intel.com>
Date: Mon, 28 May 2018 12:52:29 +0800
Subject: [PATCH 07/16] add mkl verify

---
 MKLDNN_README.md | 62 ++++++++++++++++++++++++++++++++++++++----------
 1 file changed, 50 insertions(+), 12 deletions(-)

diff --git a/MKLDNN_README.md b/MKLDNN_README.md
index 15f0c8c0ee74..12d132de6199 100644
--- a/MKLDNN_README.md
+++ b/MKLDNN_README.md
@@ -1,12 +1,14 @@
+# Build/Install MXNet with MKL-DNN
+
 <h2 id="0">Contents</h2>
 
-* [1. Build/Install MXNet with MKL-DNN on Linux](#1)
-* [2. Build/Install MXNet with MKL-DNN on MacOS](#2)
-* [3. Build/Install MXNet with MKL-DNN on Windows](#3)
+* [1. Linux](#1)
+* [2. MacOS](#2)
+* [3. Windows](#3)
 * [4. Verify MXNet with python](#4)
-* [5. Build/Install MXNet with a full MKL installation](#5)
+* [5. Enable MKL BLAS](#5)
 
-<h2 id="1">Build/Install MXNet with MKL-DNN on Linux</h2>
+<h2 id="1">Linux</h2>
 
 ### Prerequisites
 
@@ -30,7 +32,7 @@ make -j $(nproc) USE_OPENCV=1 USE_MKLDNN=1 USE_BLAS=mkl USE_INTEL_PATH=/opt/inte
 
 If you don't have full MKL library installed, you can use OpenBLAS by setting `USE_BLAS=openblas`.
 
-<h2 id="2">Build/Install MXNet with MKL-DNN on MacOS</h2>
+<h2 id="2">MacOS</h2>
 
 ### Prerequisites
 
@@ -83,7 +85,7 @@ make -j $(sysctl -n hw.ncpu) USE_OPENCV=0 USE_OPENMP=1 USE_MKLDNN=1 USE_BLAS=app
 
 *Note: Temporarily disable OPENCV.*
 
-<h2 id="3">Build/Install MXNet with MKL-DNN on Windows</h2>
+<h2 id="3">Windows</h2>
 
 To build and install MXNet yourself, you need the following dependencies. Install the required dependencies:
 
@@ -143,7 +145,6 @@ After MXNet is installed, you can verify if MKL-DNN backend works well with a si
 ```
 import mxnet as mx
 import numpy as np
-from mxnet import Context
 
 num_filter = 32
 kernel = (3, 3)
@@ -153,7 +154,7 @@ 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(Context.default_ctx, x=shape)
+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)
@@ -176,7 +177,7 @@ mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_oihw out:f32_OIhw16i16o,num:1,3
 mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_nChw16c out:f32_nchw,num:1,32x32x256x256,20.4819
 ```
 
-<h2 id="5">Build/Install MXNet with a full MKL installation</h2>
+<h2 id="5">Enable MKL BLAS</h2>
 
 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.
 
@@ -184,8 +185,45 @@ Installing and enabling the full MKL installation enables MKL support for all op
 
   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'
+  2. Run `make -j ${nproc} USE_BLAS=mkl`
 
   3. Navigate into the python directory
 
-  4. Run 'sudo python setup.py install'
+  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
+```
\ No newline at end of file

From a8b5e9b41f7ddd04bcd2f901a586a27396e7c608 Mon Sep 17 00:00:00 2001
From: xinyu-intel <xinyu1.chen@intel.com>
Date: Tue, 29 May 2018 00:25:55 +0800
Subject: [PATCH 08/16] trigger

---
 MKLDNN_README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/MKLDNN_README.md b/MKLDNN_README.md
index 12d132de6199..f70d0fc85fb3 100644
--- a/MKLDNN_README.md
+++ b/MKLDNN_README.md
@@ -103,7 +103,7 @@ After you have installed all of the required dependencies, build the MXNet sourc
 
 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
+git clone --recursive https://github.com/apache/incubator-mxnet.git
 ```
 
 2. Copy file `3rdparty/mkldnn/config_template.vcxproj` to incubator-mxnet root.

From cb2080b18bebc9765609bb03f19f6108f8cb6d2f Mon Sep 17 00:00:00 2001
From: xinyu-intel <xinyu1.chen@intel.com>
Date: Tue, 29 May 2018 11:07:18 +0800
Subject: [PATCH 09/16] trigger

---
 MKLDNN_README.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/MKLDNN_README.md b/MKLDNN_README.md
index f70d0fc85fb3..9db627f1bdfd 100644
--- a/MKLDNN_README.md
+++ b/MKLDNN_README.md
@@ -65,9 +65,9 @@ If you want to enable OpenMP for better performance, you should modify these two
 
 ```
 ifeq ($(USE_OPENMP), 1)
-	# ifneq ($(UNAME_S), Darwin)
-		CFLAGS += -fopenmp
-	# endif
+# ifneq ($(UNAME_S), Darwin)
+    CFLAGS += -fopenmp
+# endif
 endif
 ```
 
@@ -226,4 +226,4 @@ 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
-```
\ No newline at end of file
+```

From 4f23cbb591845b8c3ec37061a0f3834d857c48c9 Mon Sep 17 00:00:00 2001
From: xinyu-intel <xinyu1.chen@intel.com>
Date: Tue, 19 Jun 2018 08:41:52 +0800
Subject: [PATCH 10/16] set mac complier to gcc47

---
 MKLDNN_README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/MKLDNN_README.md b/MKLDNN_README.md
index 9db627f1bdfd..e673c2d31f30 100644
--- a/MKLDNN_README.md
+++ b/MKLDNN_README.md
@@ -80,7 +80,7 @@ CC=gcc-4.9 CXX=g++-4.9 cmake $MKLDNN_ROOTDIR -DCMAKE_INSTALL_PREFIX=$MKLDNN_INST
 ### Build MXNet with MKL-DNN
 
 ```
-make -j $(sysctl -n hw.ncpu) USE_OPENCV=0 USE_OPENMP=1 USE_MKLDNN=1 USE_BLAS=apple USE_PROFILER=1
+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
 ```
 
 *Note: Temporarily disable OPENCV.*

From f477fa9dea23c12bacd15922abf44575814a7974 Mon Sep 17 00:00:00 2001
From: xinyu-intel <xinyu1.chen@intel.com>
Date: Tue, 19 Jun 2018 16:21:57 +0800
Subject: [PATCH 11/16] add VS2017 support experimentally

---
 MKLDNN_README.md | 58 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 58 insertions(+)

diff --git a/MKLDNN_README.md b/MKLDNN_README.md
index e673c2d31f30..7e82ec7bd4e5 100644
--- a/MKLDNN_README.md
+++ b/MKLDNN_README.md
@@ -87,6 +87,10 @@ make -j $(sysctl -n hw.ncpu) CC=gcc-4.9 CXX=g++-4.9 USE_OPENCV=0 USE_OPENMP=1 US
 
 <h2 id="3">Windows</h2>
 
+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/).
+
+**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.
@@ -124,6 +128,60 @@ 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](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 https://github.com/apache/incubator-mxnet.git --recursive
+```
+
+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>
 
 ```

From 3f74b8658916e196508f0fc5d2bbaeb7cfc44f84 Mon Sep 17 00:00:00 2001
From: xinyu-intel <xinyu1.chen@intel.com>
Date: Wed, 20 Jun 2018 10:33:49 +0800
Subject: [PATCH 12/16] improve quality

---
 MKLDNN_README.md | 23 ++++++++++++++++-------
 1 file changed, 16 insertions(+), 7 deletions(-)

diff --git a/MKLDNN_README.md b/MKLDNN_README.md
index 7e82ec7bd4e5..9f8e2b2edcaa 100644
--- a/MKLDNN_README.md
+++ b/MKLDNN_README.md
@@ -13,7 +13,11 @@
 ### Prerequisites
 
 ```
-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
+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
@@ -21,7 +25,6 @@ apt-get update && apt-get install -y build-essential git libopencv-dev curl gcc
 ```
 git clone --recursive https://github.com/apache/incubator-mxnet.git
 cd incubator-mxnet
-git submodule update --recursive --init
 ```
 
 ### Build MXNet with MKL-DNN
@@ -30,7 +33,7 @@ git submodule update --recursive --init
 make -j $(nproc) USE_OPENCV=1 USE_MKLDNN=1 USE_BLAS=mkl USE_INTEL_PATH=/opt/intel
 ```
 
-If you don't have full MKL library installed, you can use OpenBLAS by setting `USE_BLAS=openblas`.
+If you don't have full [MKL](https://software.intel.com/en-us/intel-mkl) library installed, you can use OpenBLAS by setting `USE_BLAS=openblas`.
 
 <h2 id="2">MacOS</h2>
 
@@ -57,6 +60,13 @@ brew install gcc49
 brew link gcc49
 ```
 
+### Clone MXNet sources
+
+```
+git clone --recursive https://github.com/apache/incubator-mxnet.git
+cd incubator-mxnet
+```
+
 ### Enable OpenMP for MacOS
 
 If you want to enable OpenMP for better performance, you should modify these two files:
@@ -148,7 +158,7 @@ After you have installed all of the required dependencies, build the MXNet sourc
 
 ```r
 cd C:\
-git clone https://github.com/apache/incubator-mxnet.git --recursive
+git clone --recursive https://github.com/apache/incubator-mxnet.git
 ```
 
 3. Copy file `3rdparty/mkldnn/config_template.vcxproj` to incubator-mxnet root.
@@ -185,9 +195,8 @@ msbuild mxnet.sln /p:Configuration=Release;Platform=x64 /maxcpucount
 <h2 id="4">Verify MXNet with python</h2>
 
 ```
-export PYTHONPATH=~/incubator-mxnet/python
-pip install --upgrade pip 
-pip install --upgrade jupyter graphviz cython pandas bokeh matplotlib opencv-python requests
+cd python
+sudo python setup.py install
 python -c "import mxnet as mx;print((mx.nd.ones((2, 3))*2).asnumpy());"
 
 Expected Output:

From b7dc9a5ca960595ed5c3f3b89b7c397a816df5fb Mon Sep 17 00:00:00 2001
From: xinyu-intel <xinyu1.chen@intel.com>
Date: Wed, 20 Jun 2018 14:14:16 +0800
Subject: [PATCH 13/16] improve quality

---
 MKLDNN_README.md | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/MKLDNN_README.md b/MKLDNN_README.md
index 9f8e2b2edcaa..e79012aee266 100644
--- a/MKLDNN_README.md
+++ b/MKLDNN_README.md
@@ -1,5 +1,7 @@
 # Build/Install MXNet with MKL-DNN
 
+Building MXNet with [Intel MKL-DNN](https://github.com/intel/mkl-dnn) will gain better performance when using Intel Xeon CPUs for training and inference. The improvement of performance can be seen in this [page](https://mxnet.incubator.apache.org/faq/perf.html#intel-cpu). Below are instructions for linux, MacOS and Windows platform.
+
 <h2 id="0">Contents</h2>
 
 * [1. Linux](#1)
@@ -69,9 +71,11 @@ cd incubator-mxnet
 
 ### Enable OpenMP for MacOS
 
-If you want to enable OpenMP for better performance, you should modify these two files:
+If you want to enable OpenMP for better performance, you should modify these two files in MXNet root dictionary:
+
+1. Makefile:
 
-1. Makefile L138:
+Add CFLAGS '-fopenmp' for Darwin.
 
 ```
 ifeq ($(USE_OPENMP), 1)
@@ -81,7 +85,9 @@ ifeq ($(USE_OPENMP), 1)
 endif
 ```
 
-2. prepare_mkldnn.sh L96:
+2. prepare_mkldnn.sh:
+
+Set cmake complier to gcc-4.9 to support OpenMP.
 
 ```
 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

From 33def026bb34a3cad86b58b22c4f2c704149bc8a Mon Sep 17 00:00:00 2001
From: xinyu-intel <xinyu1.chen@intel.com>
Date: Wed, 11 Jul 2018 14:07:16 +0800
Subject: [PATCH 14/16] modify mac build instruction since prepare_mkldnn.sh
 has been rm

---
 MKLDNN_README.md | 12 +-----------
 1 file changed, 1 insertion(+), 11 deletions(-)

diff --git a/MKLDNN_README.md b/MKLDNN_README.md
index e79012aee266..e495a8b2b094 100644
--- a/MKLDNN_README.md
+++ b/MKLDNN_README.md
@@ -71,9 +71,7 @@ cd incubator-mxnet
 
 ### Enable OpenMP for MacOS
 
-If you want to enable OpenMP for better performance, you should modify these two files in MXNet root dictionary:
-
-1. Makefile:
+If you want to enable OpenMP for better performance, you should modify the Makefile in MXNet root dictionary:
 
 Add CFLAGS '-fopenmp' for Darwin.
 
@@ -85,14 +83,6 @@ ifeq ($(USE_OPENMP), 1)
 endif
 ```
 
-2. prepare_mkldnn.sh:
-
-Set cmake complier to gcc-4.9 to support OpenMP.
-
-```
-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
-```
-
 ### Build MXNet with MKL-DNN
 
 ```

From 6e6472f71aeb74f475af94b5f3c5248f93839c12 Mon Sep 17 00:00:00 2001
From: xinyu-intel <xinyu1.chen@intel.com>
Date: Wed, 11 Jul 2018 19:55:39 +0800
Subject: [PATCH 15/16] trigger

---
 MKLDNN_README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/MKLDNN_README.md b/MKLDNN_README.md
index e495a8b2b094..0c67f35713f9 100644
--- a/MKLDNN_README.md
+++ b/MKLDNN_README.md
@@ -59,7 +59,7 @@ brew tap homebrew/core
 brew install opencv
 brew tap homebrew/versions
 brew install gcc49
-brew link gcc49
+brew link gcc49 #gcc-5 and gcc-7 also work
 ```
 
 ### Clone MXNet sources

From 846d3bcb938b357a1ada0d2a5eb6170d75723fe6 Mon Sep 17 00:00:00 2001
From: xinyu-intel <xinyu1.chen@intel.com>
Date: Thu, 12 Jul 2018 09:32:40 +0800
Subject: [PATCH 16/16] add some improvement

---
 MKLDNN_README.md | 19 ++++++++++++++-----
 1 file changed, 14 insertions(+), 5 deletions(-)

diff --git a/MKLDNN_README.md b/MKLDNN_README.md
index 0c67f35713f9..43cced49ed0d 100644
--- a/MKLDNN_README.md
+++ b/MKLDNN_README.md
@@ -9,6 +9,7 @@ Building MXNet with [Intel MKL-DNN](https://github.com/intel/mkl-dnn) will gain
 * [3. Windows](#3)
 * [4. Verify MXNet with python](#4)
 * [5. Enable MKL BLAS](#5)
+* [6. Support](#6)
 
 <h2 id="1">Linux</h2>
 
@@ -100,8 +101,8 @@ We recommend to build and install MXNet yourself using [Microsoft Visual Studio
 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).
+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
@@ -120,8 +121,8 @@ git clone --recursive https://github.com/apache/incubator-mxnet.git
 
 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:
+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
@@ -139,7 +140,7 @@ Also ```libmkldnn.dll``` with be in the ```./build/3rdparty/mkldnn/src/Release/`
 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.
+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```).
@@ -290,3 +291,11 @@ 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">Next Steps and Support</h2>
+
+- For questions or support specific to MKL, visit the [Intel MKL](https://software.intel.com/en-us/mkl)
+
+- For questions or support specific to MKL, visit the [Intel MKLDNN](https://github.com/intel/mkl-dnn)
+
+- 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)