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Update mkldnn window build instructions in MKLDNN_README.md #14952

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Update mkldnn window build instructions in MKLDNN_README.md #14952

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update mklndd.md
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Merge branch 'master' of https://github.com/apache/incubator-mxnet in…
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157 changes: 53 additions & 104 deletions docs/tutorials/mkldnn/MKLDNN_README.md
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<!--- 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, -->
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<!--- "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY -->
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<!--- specific language governing permissions and limitations -->
<!--- under the License. -->

<!--- Licensed to the Apache Software Foundation (ASF) under one -->
<!--- or more contributor license agreements. See the NOTICE file -->
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<!--- 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 -->
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What is the change in the License part?

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seems the original EOL have LF and CRLF as file end flag. I change all of them to LF. thanks

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I see there're several hardcode for the software versions. The advantage is the fix id can make the build more easily. On the other hand, the document should be a little flexible and maybe don't need to pin on specific version id.

How about giving a list of SW version in the document and mentioned which are fully tested version?

The numpy etc, are common support library, version-insensitive, I removed the version information. thanks

# 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.
Expand Down Expand Up @@ -108,111 +108,60 @@ On Windows, you can use [Micrsoft Visual Studio 2015](https://www.visualstudio.c
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\```.
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. Download the MXNet source code from [GitHub](https://github.com/apache/incubator-mxnet). Don't forget to pull the submodules:
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. 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:
2. 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
>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. Enable Intel MKL-DNN and Intel MKL as BLAS library by the command:
```

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
>"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"
```

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:

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
```

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.
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.

<h2 id="4">Verify MXNet with python</h2>

Preinstall python and some dependent modules:
```
pip install numpy==1.15.2 graphviz==0.8.4 requests
set PYTHONPATH=[workdir]\incubator-mxnet\python
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```
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
Expand Down Expand Up @@ -295,7 +244,7 @@ Then by running above code snippet, you probably will get the following output m
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 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
```

Expand All @@ -306,14 +255,14 @@ Graph optimization by subgraph feature are available in master branch. You can b
```
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
```

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:

Expand Down