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[MXNET-711] Website build and version dropdown update (apache#11892)
* adding param for list of tags to display on website * using new website display argument for artifact placement in version folder * adding display logic * remove restricted setting for testing * update usage instructions * reverted Jenkinsfile to use restricted nodes [MXAPPS-581] Fixes for broken Straight Dope tests. (apache#11923) * Update relative paths pointing to the data directory to point to the correct place in the testing temporary folder. * Enable the notebooks that were previously broken because of relative file paths not pointing to the correct place. * Move some notebooks we do not plan to test to the whitelist. These notebooks are not published in the Straight Dope book. * Clean-up: Convert print statements to info/warn/error logging statements. Add some logging statements for better status. Disable flaky test: test_spatial_transformer_with_type (apache#11930) apache#11839 Add linux and macos MKLDNN Building Instruction (apache#11049) * add linux and macos doc * update doc * Update MKL_README.md * Update MKL_README.md Add convolution code to verify mkldnn backend * add homebrew link * rename to MKLDNN_README * add mkl verify * trigger * trigger * set mac complier to gcc47 * add VS2017 support experimentally * improve quality * improve quality * modify mac build instruction since prepare_mkldnn.sh has been rm * trigger * add some improvement [MXNET-531] Add download util (apache#11866) * add changes to example * place the file to the util * add retry scheme * fix the retry logic * change the DownloadUtil to Util * Trigger the CI [MXNET-11241] Avoid use of troublesome cudnnFind() results when grad_req='add' (apache#11338) * Add tests that fail due to issue 11241 * Fix apache#11241 Conv1D throws CUDNN_STATUS_EXECUTION_FAILED * Force algo 1 when grad_req==add with large c. Expand tests. * Shorten test runtimes. Improving documentation and error messages for Async distributed training with Gluon (apache#11910) * Add description about update on kvstore * add async check for gluon * only raise error if user set update_on_kvstore * fix condition * add async nightly test * fix case when no kvstore * add example for trainer creation in doc [MXNET-641] fix R windows install docs (apache#11805) * fix R windows install docs * addressed PR comments * PR comments * PR comments * fixed line wrappings * fixed line wrappings a hot fix for mkldnn link (apache#11939) re-enabling randomized test_l2_normalization (apache#11900) [MXNET-651] MXNet Model Backwards Compatibility Checker (apache#11626) * Added MNIST-MLP-Module-API models to check model save and load_checkpoint methods * Added LENET with Conv2D operator training file * Added LENET with Conv2d operator inference file * Added LanguageModelling with RNN training file * Added LamguageModelling with RNN inference file * Added hybridized LENET Gluon Model training file * Added hybridized LENET gluon model inference file * Added license headers * Refactored the model and inference files and extracted out duplicate code in a common file * Added runtime function for executing the MBCC files * Added JenkinsFile for MBCC to be run as a nightly job * Added boto3 install for s3 uploads * Added README for MBCC * Added license header * Added more common functions from lm_rnn_gluon_train and inference files into common.py to clean up code * Added scripts for training models on older versions of MXNet * Added check for preventing inference script from crashing in case no trained models are found * Fixed indentation issue * Replaced Penn Tree Bank Dataset with Sherlock Holmes Dataset * Fixed indentation issue * Removed training in models and added smaller models. Now we are simply checking a forward pass in the model with dummy data. * Updated README * Fixed indentation error * Fixed indentation error * Removed code duplication in the training file * Added comments for runtime_functions script for training files * Merged S3 Buckets for storing data and models into one * Automated the process to fetch MXNet versions from git tags * Added defensive checks for the case where the data might not be found * Fixed issue where we were performing inference on state model files * Replaced print statements with logging ones * Removed boto install statements and move them into ubuntu_python docker * Separated training and uploading of models into separate files so that training runs in Docker and upload runs outside Docker * Fixed pylint warnings * Updated comments and README * Removed the venv for training process * Fixed indentation in the MBCC Jenkins file and also separated out training and inference into two separate stages * Fixed indendation * Fixed erroneous single quote * Added --user flag to check for Jenkins error * Removed unused methods * Added force flag in the pip command to install mxnet * Removed the force-re-install flag * Changed exit 1 to exit 0 * Added quotes around the shell command * added packlibs and unpack libs for MXNet builds * Changed PythonPath from relative to absolute * Created dedicated bucket with correct permission * Fix for python path in training * Changed bucket name to CI bucket * Added set -ex to the upload shell script * Now raising an exception if no models are found in the S3 bucket * Added regex to train models script * Added check for performing inference only on models trained on same major versions * Added set -ex flags to shell scripts * Added multi-version regex checks in training * Fixed typo in regex * Now we will train models for all the minor versions for a given major version by traversing the tags * Added check for validating current_version [MXNET-531] NeuralStyle Example for Scala (apache#11621) * add initial neuralstyle and test coverage * Add two more test and README * kill comments * patch on memory leaks fix * fix formatting issues * remove redundant files * disable the Gan example for now * add ignore method * add new download scheme to match the changes
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‎MKLDNN_README.md‎

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# Build/Install MXNet with MKL-DNN
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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.
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<h2 id="0">Contents</h2>
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* [1. Linux](#1)
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* [2. MacOS](#2)
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* [3. Windows](#3)
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* [4. Verify MXNet with python](#4)
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* [5. Enable MKL BLAS](#5)
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* [6. Support](#6)
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<h2 id="1">Linux</h2>
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### Prerequisites
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```
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sudo apt-get update
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sudo apt-get install -y build-essential git
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sudo apt-get install -y libopenblas-dev liblapack-dev
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sudo apt-get install -y libopencv-dev
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sudo apt-get install -y graphviz
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```
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### Clone MXNet sources
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```
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git clone --recursive https://github.com/apache/incubator-mxnet.git
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cd incubator-mxnet
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```
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### Build MXNet with MKL-DNN
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```
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make -j $(nproc) USE_OPENCV=1 USE_MKLDNN=1 USE_BLAS=mkl USE_INTEL_PATH=/opt/intel
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```
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If you don't have full [MKL](https://software.intel.com/en-us/intel-mkl) library installed, you can use OpenBLAS by setting `USE_BLAS=openblas`.
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<h2 id="2">MacOS</h2>
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### Prerequisites
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Install the dependencies, required for MXNet, with the following commands:
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- [Homebrew](https://brew.sh/)
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- gcc (clang in macOS does not support OpenMP)
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- OpenCV (for computer vision operations)
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```
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# Paste this command in Mac terminal to install Homebrew
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/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
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# install dependency
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brew update
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brew install pkg-config
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brew install graphviz
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brew tap homebrew/core
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brew install opencv
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brew tap homebrew/versions
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brew install gcc49
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brew link gcc49 #gcc-5 and gcc-7 also work
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```
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### Clone MXNet sources
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```
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git clone --recursive https://github.com/apache/incubator-mxnet.git
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cd incubator-mxnet
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```
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### Enable OpenMP for MacOS
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If you want to enable OpenMP for better performance, you should modify the Makefile in MXNet root dictionary:
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Add CFLAGS '-fopenmp' for Darwin.
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```
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ifeq ($(USE_OPENMP), 1)
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# ifneq ($(UNAME_S), Darwin)
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CFLAGS += -fopenmp
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# endif
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endif
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```
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### Build MXNet with MKL-DNN
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```
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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
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```
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*Note: Temporarily disable OPENCV.*
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<h2 id="3">Windows</h2>
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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/).
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**Visual Studio 2015**
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To build and install MXNet yourself, you need the following dependencies. Install the required dependencies:
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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.
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2. Download and Install [CMake 3](https://cmake.org/) if it is not already installed.
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3. Download and install [OpenCV 3](http://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.0.0/opencv-3.0.0.exe/download).
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4. Unzip the OpenCV package.
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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.
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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
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```C:\Program Files (x86)\IntelSWTools\compilers_and_libraries_2018\windows\mkl```.
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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.
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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\```.
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After you have installed all of the required dependencies, build the MXNet source code:
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1. Download the MXNet source code from [GitHub](https://github.com/apache/incubator-mxnet). Don't forget to pull the submodules:
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```
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git clone --recursive https://github.com/apache/incubator-mxnet.git
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```
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2. Copy file `3rdparty/mkldnn/config_template.vcxproj` to incubator-mxnet root.
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3. Start a Visual Studio command prompt.
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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
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[CMake 3](https://cmake.org/) command:
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```
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mkdir build
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cd build
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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
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```
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5. In Visual Studio, open the solution file,```.sln```, and compile it.
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These commands produce a library called ```libmxnet.dll``` in the ```./build/Release/``` or ```./build/Debug``` folder.
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Also ```libmkldnn.dll``` with be in the ```./build/3rdparty/mkldnn/src/Release/```
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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.
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**Visual Studio 2017**
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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:
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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.
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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.
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3. Download and install [OpenCV](https://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.4.1/opencv-3.4.1-vc14_vc15.exe/download).
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4. Unzip the OpenCV package.
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5. Set the environment variable ```OpenCV_DIR``` to point to the ```OpenCV build directory``` (e.g., ```OpenCV_DIR = C:\utils\opencv\build```).
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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).
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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```).
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After you have installed all of the required dependencies, build the MXNet source code:
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1. Start ```cmd``` in windows.
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2. Download the MXNet source code from GitHub by using following command:
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```r
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cd C:\
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git clone --recursive https://github.com/apache/incubator-mxnet.git
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```
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3. Copy file `3rdparty/mkldnn/config_template.vcxproj` to incubator-mxnet root.
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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```.
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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):
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```r
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"C:\Program Files (x86)\Microsoft Visual Studio\2017\Community\VC\Auxiliary\Build\vcvars64.bat" -vcvars_ver=14.11
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```
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6. Create a build dir using the following command and go to the directory, for example:
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```r
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mkdir C:\build
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cd C:\build
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```
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7. CMake the MXNet source code by using following command:
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```r
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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
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```
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8. After the CMake successfully completed, compile the the MXNet source code by using following command:
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```r
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msbuild mxnet.sln /p:Configuration=Release;Platform=x64 /maxcpucount
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```
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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.
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<h2 id="4">Verify MXNet with python</h2>
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```
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cd python
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sudo python setup.py install
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python -c "import mxnet as mx;print((mx.nd.ones((2, 3))*2).asnumpy());"
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Expected Output:
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[[ 2. 2. 2.]
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[ 2. 2. 2.]]
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```
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### Verify whether MKL-DNN works
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After MXNet is installed, you can verify if MKL-DNN backend works well with a single Convolution layer.
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```
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import mxnet as mx
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import numpy as np
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num_filter = 32
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kernel = (3, 3)
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pad = (1, 1)
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shape = (32, 32, 256, 256)
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x = mx.sym.Variable('x')
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w = mx.sym.Variable('w')
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y = mx.sym.Convolution(data=x, weight=w, num_filter=num_filter, kernel=kernel, no_bias=True, pad=pad)
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exe = y.simple_bind(mx.cpu(), x=shape)
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exe.arg_arrays[0][:] = np.random.normal(size=exe.arg_arrays[0].shape)
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exe.arg_arrays[1][:] = np.random.normal(size=exe.arg_arrays[1].shape)
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exe.forward(is_train=False)
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o = exe.outputs[0]
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t = o.asnumpy()
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```
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You can open the `MKLDNN_VERBOSE` flag by setting environment variable:
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```
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export MKLDNN_VERBOSE=1
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```
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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.
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```
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mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_nchw out:f32_nChw16c,num:1,32x32x256x256,6.47681
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mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_oihw out:f32_OIhw16i16o,num:1,32x32x3x3,0.0429688
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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
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mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_oihw out:f32_OIhw16i16o,num:1,32x32x3x3,0.0510254
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mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_nChw16c out:f32_nchw,num:1,32x32x256x256,20.4819
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```
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<h2 id="5">Enable MKL BLAS</h2>
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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.
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Installing and enabling the full MKL installation enables MKL support for all operators under the linalg namespace.
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1. Download and install the latest full MKL version following instructions on the [intel website.](https://software.intel.com/en-us/mkl)
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2. Run `make -j ${nproc} USE_BLAS=mkl`
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3. Navigate into the python directory
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4. Run `sudo python setup.py install`
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### Verify whether MKL works
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After MXNet is installed, you can verify if MKL BLAS works well with a single dot layer.
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```
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import mxnet as mx
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import numpy as np
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shape_x = (1, 10, 8)
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shape_w = (1, 12, 8)
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x_npy = np.random.normal(0, 1, shape_x)
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w_npy = np.random.normal(0, 1, shape_w)
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x = mx.sym.Variable('x')
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w = mx.sym.Variable('w')
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y = mx.sym.batch_dot(x, w, transpose_b=True)
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exe = y.simple_bind(mx.cpu(), x=x_npy.shape, w=w_npy.shape)
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exe.forward(is_train=False)
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o = exe.outputs[0]
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t = o.asnumpy()
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```
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You can open the `MKL_VERBOSE` flag by setting environment variable:
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```
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export MKL_VERBOSE=1
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```
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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.
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```
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Numpy + Intel(R) MKL: THREADING LAYER: (null)
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Numpy + Intel(R) MKL: setting Intel(R) MKL to use INTEL OpenMP runtime
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Numpy + Intel(R) MKL: preloading libiomp5.so runtime
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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
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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
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```
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<h2 id="6">Next Steps and Support</h2>
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- For questions or support specific to MKL, visit the [Intel MKL](https://software.intel.com/en-us/mkl)
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- For questions or support specific to MKL, visit the [Intel MKLDNN](https://github.com/intel/mkl-dnn)
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- 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)

‎MKL_README.md‎

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‎README.md‎

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* [Version 0.8.0 Release](https://github.com/dmlc/mxnet/releases/tag/v0.8.0)
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* [Updated Image Classification with new Pre-trained Models](./example/image-classification)
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* [Python Notebooks for How to Use MXNet](https://github.com/dmlc/mxnet-notebooks)
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* [MKLDNN for Faster CPU Performance](./MKL_README.md)
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* [MKLDNN for Faster CPU Performance](./MKLDNN_README.md)
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* [MXNet Memory Monger, Training Deeper Nets with Sublinear Memory Cost](https://github.com/dmlc/mxnet-memonger)
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* [Tutorial for NVidia GTC 2016](https://github.com/dmlc/mxnet-gtc-tutorial)
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* [Embedding Torch layers and functions in MXNet](https://mxnet.incubator.apache.org/faq/torch.html)

‎ci/docker/install/ubuntu_python.sh‎

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python2 get-pip.py
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pip2 install nose cpplint==1.3.0 pylint==1.8.3 'numpy<1.15.0,>=1.8.2' nose-timer 'requests<2.19.0,>=2.18.4' h5py==2.8.0rc1 scipy==1.0.1
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pip3 install nose cpplint==1.3.0 pylint==1.8.3 'numpy<1.15.0,>=1.8.2' nose-timer 'requests<2.19.0,>=2.18.4' h5py==2.8.0rc1 scipy==1.0.1
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pip2 install nose cpplint==1.3.0 pylint==1.8.3 'numpy<1.15.0,>=1.8.2' nose-timer 'requests<2.19.0,>=2.18.4' h5py==2.8.0rc1 scipy==1.0.1 boto3
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pip3 install nose cpplint==1.3.0 pylint==1.8.3 'numpy<1.15.0,>=1.8.2' nose-timer 'requests<2.19.0,>=2.18.4' h5py==2.8.0rc1 scipy==1.0.1 boto3

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