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new c56c146 [Doc] Start the tutorials for MKL-DNN backend (#14202)
c56c146 is described below
commit c56c14658daa650090377501d01e2606f4702e1e
Author: Tao Lv <tao.a...@intel.com>
AuthorDate: Tue Mar 19 13:36:06 2019 +0800
[Doc] Start the tutorials for MKL-DNN backend (#14202)
* start tutorials for mkldnn backend
* fix links
* add to tutorial index
* fix sanity test
* fix sanity test complain
* add to webpage
* Hint message and avoid 404
---
MKLDNN_README.md | 314 +--------------------
NEWS.md | 2 +-
README.md | 2 +-
docs/faq/perf.md | 2 +-
docs/install/ubuntu_setup.md | 2 +-
docs/install/windows_setup.md | 4 +-
docs/tutorials/index.md | 2 +
.../tutorials/mkldnn/MKLDNN_README.md | 6 +-
docs/tutorials/mkldnn/index.md | 25 ++
tests/tutorials/test_sanity_tutorials.py | 2 +
10 files changed, 39 insertions(+), 322 deletions(-)
diff --git a/MKLDNN_README.md b/MKLDNN_README.md
index 214fc83..34790c9 100644
--- a/MKLDNN_README.md
+++ b/MKLDNN_README.md
@@ -15,316 +15,4 @@
<!--- 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.
-
-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
-```
-
-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).
+File is moved to
[docs/tutorials/mkldnn/MKLDNN_README.md](docs/tutorials/mkldnn/MKLDNN_README.md).
diff --git a/NEWS.md b/NEWS.md
index 20804da..ad842ac 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -164,7 +164,7 @@ MKLDNN backend takes advantage of MXNet subgraph to
implement the most of possib
##### Quantization
Performance of reduced-precision (INT8) computation is also dramatically
improved after the graph optimization feature is applied on CPU Platforms.
Various models are supported and can benefit from reduced-precision
computation, including symbolic models, Gluon models and even custom models.
Users can run most of the pre-trained models with only a few lines of commands
and a new quantization script imagenet_gen_qsym_mkldnn.py. The observed
accuracy loss is less than 0.5% for popular CNN [...]
-Please find detailed information and performance/accuracy numbers here:
[MKLDNN
README](https://github.com/apache/incubator-mxnet/blob/master/MKLDNN_README.md),
[quantization
README](https://github.com/apache/incubator-mxnet/tree/master/example/quantization#1)
and [design
proposal](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Graph+Optimization+and+Quantization+based+on+subgraph+and+MKL-DNN)
+Please find detailed information and performance/accuracy numbers here:
[MKLDNN
README](https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/mkldnn/MKLDNN_README.md),
[quantization
README](https://github.com/apache/incubator-mxnet/tree/master/example/quantization#1)
and [design
proposal](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Graph+Optimization+and+Quantization+based+on+subgraph+and+MKL-DNN)
### New Operators
diff --git a/README.md b/README.md
index 307bbd4..dbc606d 100644
--- a/README.md
+++ b/README.md
@@ -66,7 +66,7 @@ What's New
* [Version 0.8.0 Release](https://github.com/dmlc/mxnet/releases/tag/v0.8.0)
* [Updated Image Classification with new Pre-trained
Models](./example/image-classification)
* [Notebooks How to Use
MXNet](https://github.com/zackchase/mxnet-the-straight-dope)
-* [MKLDNN for Faster CPU Performance](./MKLDNN_README.md)
+* [MKLDNN for Faster CPU Performance](./docs/tutorials/mkldnn/MKLDNN_README.md)
* [MXNet Memory Monger, Training Deeper Nets with Sublinear Memory
Cost](https://github.com/dmlc/mxnet-memonger)
* [Tutorial for NVidia GTC 2016](https://github.com/dmlc/mxnet-gtc-tutorial)
* [Embedding Torch layers and functions in
MXNet](https://mxnet.incubator.apache.org/faq/torch.html)
diff --git a/docs/faq/perf.md b/docs/faq/perf.md
index 00310df..e1318b8 100644
--- a/docs/faq/perf.md
+++ b/docs/faq/perf.md
@@ -43,7 +43,7 @@ We also find that setting the following environment variables
can help:
| :-------- | :---------- |
| `OMP_NUM_THREADS` | Suggested value: `vCPUs / 2` in which `vCPUs`
is the number of virtual CPUs. For more information, please see the guide for
[setting the number of threads using an OpenMP environment
variable](https://software.intel.com/en-us/mkl-windows-developer-guide-setting-the-number-of-threads-using-an-openmp-environment-variable)
|
| `KMP_AFFINITY` | Suggested value:
`granularity=fine,compact,1,0`. For more information, please see the guide for
[Thread Affinity Interface (Linux* and
Windows*)](https://software.intel.com/en-us/node/522691). |
-| `MXNET_SUBGRAPH_BACKEND` | Set to MKLDNN to enable the [subgraph
feature](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Graph+Optimization+and+Quantization+based+on+subgraph+and+MKL-DNN)
for better performance. For more information please see [Build/Install MXNet
with
MKL-DNN](https://github.com/apache/incubator-mxnet/blob/master/MKLDNN_README.md)|
+| `MXNET_SUBGRAPH_BACKEND` | Set to MKLDNN to enable the [subgraph
feature](https://cwiki.apache.org/confluence/display/MXNET/MXNet+Graph+Optimization+and+Quantization+based+on+subgraph+and+MKL-DNN)
for better performance. For more information please see [Build/Install MXNet
with
MKL-DNN](https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/mkldnn/MKLDNN_README.md)|
Note that _MXNet_ treats all CPUs on a single machine as a single device.
So whether you specify `cpu(0)` or `cpu()`, _MXNet_ will use all CPU cores on
the machine.
diff --git a/docs/install/ubuntu_setup.md b/docs/install/ubuntu_setup.md
index 24f9ab9..f225023 100644
--- a/docs/install/ubuntu_setup.md
+++ b/docs/install/ubuntu_setup.md
@@ -175,7 +175,7 @@ If building on CPU and using OpenBLAS:
make -j $(nproc)
```
-If building on CPU and using MKL and MKL-DNN (make sure MKL is installed
according to [Math Library
Selection](build_from_source.html#math-library-selection) and [MKL-DNN
README](https://github.com/apache/incubator-mxnet/blob/master/MKLDNN_README.md)):
+If building on CPU and using MKL and MKL-DNN (make sure MKL is installed
according to [Math Library
Selection](build_from_source.html#math-library-selection) and [MKL-DNN
README](https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/mkldnn/MKLDNN_README.md)):
```bash
git clone --recursive https://github.com/apache/incubator-mxnet.git
diff --git a/docs/install/windows_setup.md b/docs/install/windows_setup.md
index 3c3da53..9692969 100644
--- a/docs/install/windows_setup.md
+++ b/docs/install/windows_setup.md
@@ -136,7 +136,7 @@ We provide two primary options to build and install MXNet
yourself using [Micros
**NOTE:** Visual Studio 2017's compiler is `vc15`. This is not to be confused
with Visual Studio 2015's compiler, `vc14`.
-You also have the option to install MXNet with MKL or MKL-DNN. In this case it
is recommended that you refer to the
[MKLDNN_README](https://github.com/apache/incubator-mxnet/blob/master/MKLDNN_README.md).
+You also have the option to install MXNet with MKL or MKL-DNN. In this case it
is recommended that you refer to the
[MKLDNN_README](https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/mkldnn/MKLDNN_README.md).
**Option 1: Build with Microsoft Visual Studio 2017 (VS2017)**
@@ -156,7 +156,7 @@ To build and install MXNet yourself using
[VS2017](https://www.visualstudio.com/
1. Download and run the
[OpenCV](https://sourceforge.net/projects/opencvlibrary/files/opencv-win/3.4.1/opencv-3.4.1-vc14_vc15.exe/download)
package. There are more recent versions of OpenCV, so please create an
issue/PR to update this info if you validate one of these later versions.
1. This will unzip several files. You can place them in another directory if
you wish. We will use `C:\utils`(```mkdir C:\utils```) as our default path.
1. Set the environment variable `OpenCV_DIR` to point to the OpenCV build
directory that you just unzipped. Start ```cmd``` and type `set
OpenCV_DIR=C:\utils\opencv\build`.
-1. If you don’t have the Intel Math Kernel Library (MKL) installed, you can
install it and follow the
[MKLDNN_README](https://github.com/apache/incubator-mxnet/blob/master/MKLDNN_README.md)
from here, or you can use OpenBLAS. These instructions will assume you're
using OpenBLAS.
+1. If you don’t have the Intel Math Kernel Library (MKL) installed, you can
install it and follow the
[MKLDNN_README](https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/mkldnn/MKLDNN_README.md)
from here, or you can use OpenBLAS. These instructions will assume you're
using OpenBLAS.
1. Download the
[OpenBlas](https://sourceforge.net/projects/openblas/files/v0.2.19/OpenBLAS-v0.2.19-Win64-int32.zip/download)
package. Later versions of OpenBLAS are available, but you would need to build
from source. v0.2.19 is the most recent version that ships with binaries.
Contributions of more recent binaries would be appreciated.
1. Unzip the file, rename it to ```OpenBLAS``` and put it under `C:\utils`.
You can place the unzipped files and folders in another directory if you wish.
1. Set the environment variable `OpenBLAS_HOME` to point to the OpenBLAS
directory that contains the `include` and `lib` directories and type `set
OpenBLAS_HOME=C:\utils\OpenBLAS` on the command prompt(```cmd```).
diff --git a/docs/tutorials/index.md b/docs/tutorials/index.md
index 7e0ffaa..d429036 100644
--- a/docs/tutorials/index.md
+++ b/docs/tutorials/index.md
@@ -27,6 +27,7 @@
embedded/index.md
gluon/index.md
java/index.md
+ mkldnn/index.md
nlp/index.md
onnx/index.md
python/index.md
@@ -142,6 +143,7 @@ Select API:
* [Large-Scale Multi-Host Multi-GPU Image
Classification](/tutorials/vision/large_scale_classification.html)
* [Importing an ONNX model into
MXNet](/tutorials/onnx/super_resolution.html)
* [Optimizing Deep Learning Computation Graphs with
TensorRT](/tutorials/tensorrt/inference_with_trt.html)
+ * [How to build and install MXNet with MKL-DNN
backend](/tutorials/mkldnn/MKLDNN_README.html)
* API Guides
* Core APIs
* NDArray
diff --git a/MKLDNN_README.md b/docs/tutorials/mkldnn/MKLDNN_README.md
similarity index 94%
copy from MKLDNN_README.md
copy to docs/tutorials/mkldnn/MKLDNN_README.md
index 214fc83..189de3f 100644
--- a/MKLDNN_README.md
+++ b/docs/tutorials/mkldnn/MKLDNN_README.md
@@ -151,7 +151,7 @@ To build and install MXNet yourself using [Microsoft Visual
Studio 2017](https:/
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).
+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:
@@ -251,7 +251,7 @@ mkldnn_verbose,exec,reorder,jit:uni,undef,in:f32_nChw16c
out:f32_nchw,num:1,32x3
<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).
+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)
@@ -317,7 +317,7 @@ This limitations of this experimental feature are:
<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.
+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).
diff --git a/docs/tutorials/mkldnn/index.md b/docs/tutorials/mkldnn/index.md
new file mode 100644
index 0000000..faf6526
--- /dev/null
+++ b/docs/tutorials/mkldnn/index.md
@@ -0,0 +1,25 @@
+<!--- 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. -->
+
+# Tutorials
+
+```eval_rst
+.. toctree::
+ :glob:
+
+ *
+```
diff --git a/tests/tutorials/test_sanity_tutorials.py
b/tests/tutorials/test_sanity_tutorials.py
index 429527d..7865000 100644
--- a/tests/tutorials/test_sanity_tutorials.py
+++ b/tests/tutorials/test_sanity_tutorials.py
@@ -33,6 +33,8 @@ whitelist = ['basic/index.md',
'embedded/index.md',
'embedded/wine_detector.md',
'gluon/index.md',
+ 'mkldnn/index.md',
+ 'mkldnn/MKLDNN_README.md',
'nlp/index.md',
'onnx/index.md',
'python/index.md',
