Install MXNet with oneDNN

A better training and inference performance is expected to be achieved on Intel-Architecture CPUs with MXNet built with oneDNN on multiple operating system, including Linux, Windows and MacOS. In the following sections, you will find build instructions for MXNet with oneDNN on Linux, MacOS and Windows.

The detailed performance data collected on Intel Xeon CPU with MXNet built with oneDNN can be found here.




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
cd mxnet

Build MXNet with oneDNN

To achieve better performance, the Intel OpenMP and llvm OpenMP are recommended as below instruction. Otherwise, default GNU OpenMP will be used and you may get the sub-optimal performance. If you don’t have the full MKL library installation, you might use OpenBLAS as the blas library, by setting USE_BLAS=Open.

# build with llvm OpenMP and Intel MKL/OpenBlas
mkdir build && cd build
make -j $(nproc)
# build with Intel MKL and Intel OpenMP
mkdir build && cd build
make -j $(nproc)
# build with openblas and GNU OpenMP (sub-optimal performance)
mkdir build && cd build
make -j $(nproc)



Install the dependencies, required for MXNet, with the following commands:

  • Homebrew

  • 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"

# 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
cd mxnet

Build MXNet with oneDNN

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_ONEDNN=1 USE_BLAS=apple


On Windows, you can use Micrsoft Visual Studio 2015 and Microsoft Visual Studio 2017 to compile MXNet with oneDNN. Micrsoft Visual Studio 2015 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 is not already installed, download and install it. You can download and install the free community edition.

  2. Download and Install CMake 3 if it is not already installed.

  3. Download OpenCV 3, and unzip the OpenCV package, set the environment variable OpenCV_DIR to point to the OpenCV build directory (e.g.,``OpenCV_DIR = C:opencvbuild``). 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 MKLROOT environment variable 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, or build the latest version of OpenBLAS from source. Note that you should also download along with openBLAS and add them to PATH.

  6. Set the environment variable OpenBLAS_HOME to point to the OpenBLAS directory that contains the include and lib directories. Typically, you can find the directory in C:\Downloads\OpenBLAS\.

After you have installed all of the required dependencies, build the MXNet source code:

  1. Start a Visual Studio command prompt by click windows Start menu>>Visual Studio 2015>>VS2015 X64 Native Tools Command Prompt, and download the MXNet source code from GitHub by the command:

git clone --recursive
cd C:\mxent
  1. Enable oneDNN by -DUSE_ONEDNN=1. Use CMake 3 to create a Visual Studio solution in ./build. Make sure to specify the architecture in the command:

>mkdir build
>cd build
  1. Enable oneDNN and Intel MKL as BLAS library by the command:

>"C:\Program Files (x86)\IntelSWTools\compilers_and_libraries\windows\mkl\bin\mklvars.bat" intel64
  1. After the CMake successfully completed, in Visual Studio, open the solution file .sln and compile it, or compile the MXNet source code by using following command:

msbuild mxnet.sln /p:Configuration=Release;Platform=x64 /maxcpucount

These commands produce mxnet library called libmxnet.dll in the ./build/Release/ or ./build/Debug folder. Also libmkldnn.dll with be in the ./build/3rdparty/onednn/src/Release/

  1. Make sure that all the dll files used above(such as libmkldnn.dll, libmklml*.dll, libiomp5.dll, libopenblas*.dll, etc) are added to the system PATH. For convinence, you can put all of them to \windows\system32. Or you will come across Not Found Dependencies when loading MXNet.

Visual Studio 2017

User can follow the same steps of Visual Studio 2015 to build MXNET with oneDNN, but change the version related command, for example,C:\opencv\build\x64\vc15\bin and build command is as below:


Verify MXNet with python

Preinstall python and some dependent modules:

pip install numpy graphviz
set PYTHONPATH=[workdir]\mxnet\python

or install mxnet

cd python
sudo python 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 oneDNN works

After MXNet is installed, you can verify if oneDNN backend works well with a single Convolution layer.

from mxnet import np
from mxnet.gluon import nn

num_filter = 32
kernel = (3, 3)
pad = (1, 1)
shape = (32, 32, 256, 256)

conv_layer = nn.Conv2D(channels=num_filter, kernel_size=kernel, padding=pad)

data = np.random.normal(size=shape)
o = conv_layer(data)

More detailed debugging and profiling information can be logged by setting the environment variable ‘DNNL_VERBOSE’:


For example, by running above code snippet, the following debugging logs providing more insights on oneDNN primitives convolution and reorder. That includes: Memory layout, infer shape and the time cost of primitive execution.

dnnl_verbose,info,oneDNN v2.3.2 (commit e2d45252ae9c3e91671339579e3c0f0061f81d49)
dnnl_verbose,info,cpu,isa:Intel AVX-512 with Intel DL Boost
dnnl_verbose,exec,cpu,reorder,jit:uni,undef,src_f32::blocked:abcd:f0 dst_f32::blocked:acdb:f0,,,32x32x256x256,8.34912
dnnl_verbose,exec,cpu,reorder,jit:uni,undef,src_f32::blocked:abcd:f0 dst_f32::blocked:Acdb32a:f0,,,32x32x3x3,0.0229492
dnnl_verbose,exec,cpu,convolution,brgconv:avx512_core,forward_inference,src_f32::blocked:acdb:f0 wei_f32::blocked:Acdb32a:f0 bia_f32::blocked:a:f0 dst_f32::blocked:acdb:f0,,alg:convolution_direct,mb32_ic32oc32_ih256oh256kh3sh1dh0ph1_iw256ow256kw3sw1dw0pw1,10.5898

You can find step-by-step guidance to do profiling for oneDNN primitives in Profiling oneDNN Operators.


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. 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. You can also install MKL through YUM or APT Repository.

  2. Create and navigate to build directory mkdir build && cd build

  3. Run cmake -DUSE_CUDA=OFF -DUSE_BLAS=mkl ..

  4. Run make -j

  5. Navigate into the python directory

  6. Run sudo python install

Verify whether MKL works

After MXNet is installed, you can verify if MKL BLAS works well with a linear matrix solver.

from mxnet import np
coeff = np.array([[7, 0], [5, 2]])
y = np.array([14, 18])
x = np.linalg.solve(coeff, y)

You can get the verbose log output from mkl library by setting environment variable:

export MKL_VERBOSE=1

Then by running above code snippet, you should get the similar output to message below (SGESV primitive from MKL was executed). Layout information and primitive execution performance are also demonstrated in the log message.

mkl-service + Intel(R) MKL: THREADING LAYER: (null)
mkl-service + Intel(R) MKL: setting Intel(R) MKL to use INTEL OpenMP runtime
mkl-service + Intel(R) MKL: preloading runtime
Intel(R) MKL 2020.0 Update 1 Product build 20200208 for Intel(R) 64 architecture Intel(R) Advanced Vector Extensions 512 (Intel(R) AVX-512) with support of Vector Neural Network Instructions enabled processors, Lnx 2.70GHz lp64 intel_thread
MKL_VERBOSE SGESV(2,1,0x7f74d4002780,2,0x7f74d4002798,0x7f74d4002790,2,0) 77.58us CNR:OFF Dyn:1 FastMM:1 TID:0  NThr:56

Graph optimization

To better utilise oneDNN potential, using graph optimizations is recommended. There are few limitations of this feature:

  • It works only for inference.

  • Only subclasses of HybridBlock and Symbol can call optimize_for API.

  • This feature will only run on the CPU, even if you’re using a GPU-enabled build of MXNet.

If your use case met above conditions, graph optimizations can be enabled by just simple call optimize_for API. Example below:

from mxnet import np
from mxnet.gluon import nn

data = np.random.normal(size=(32,3,224,224))

net = nn.HybridSequential()
net.add(nn.Conv2D(channels=64, kernel_size=(3,3)))
print("=" * 5, " Not optimized ", "=" * 5)
o = net(data)

net.optimize_for(data, backend='ONEDNN')
print("=" * 5, " Optimized ", "=" * 5)
o = net(data)

Above code snippet should produce similar output to the following one (printed tensors are omitted) :

===== Not optimized =====
[15:05:43] ../src/storage/ Using Pooled (Naive) StorageManager for CPU
dnnl_verbose,info,oneDNN v2.3.2 (commit e2d45252ae9c3e91671339579e3c0f0061f81d49)
dnnl_verbose,info,cpu,isa:Intel AVX-512 with AVX512BW, AVX512VL, and AVX512DQ extensions
dnnl_verbose,exec,cpu,reorder,jit:uni,undef,src_f32::blocked:abcd:f0 dst_f32::blocked:acdb:f0,,,32x3x224x224,8.87793
dnnl_verbose,exec,cpu,reorder,jit:uni,undef,src_f32::blocked:abcd:f0 dst_f32::blocked:Acdb64a:f0,,,64x3x3x3,0.00708008
dnnl_verbose,exec,cpu,convolution,brgconv:avx512_core,forward_inference,src_f32::blocked:acdb:f0 wei_f32::blocked:Acdb64a:f0 bia_f32::blocked:a:f0 dst_f32::blocked:acdb:f0,,alg:convolution_direct,mb32_ic3oc64_ih224oh222kh3sh1dh0ph0_iw224ow222kw3sw1dw0pw0,91.511
dnnl_verbose,exec,cpu,reorder,jit:uni,undef,src_f32::blocked:abcd:f0 dst_f32::blocked:Acdb64a:f0,,,64x3x3x3,0.00610352
dnnl_verbose,exec,cpu,eltwise,jit:avx512_common,forward_inference,data_f32::blocked:acdb:f0 diff_undef::undef::f0,,alg:eltwise_relu alpha:0 beta:0,32x64x222x222,85.4392
===== Optimized =====
dnnl_verbose,exec,cpu,reorder,jit:uni,undef,src_f32::blocked:Acdb64a:f0 dst_f32::blocked:abcd:f0,,,64x3x3x3,0.00610352
dnnl_verbose,exec,cpu,reorder,jit:uni,undef,src_f32::blocked:abcd:f0 dst_f32::blocked:Acdb64a:f0,,,64x3x3x3,0.00585938
dnnl_verbose,exec,cpu,reorder,jit:uni,undef,src_f32::blocked:abcd:f0 dst_f32::blocked:acdb:f0,,,32x3x224x224,3.98999
dnnl_verbose,exec,cpu,convolution,brgconv:avx512_core,forward_inference,src_f32::blocked:acdb:f0 wei_f32::blocked:Acdb64a:f0 bia_f32::blocked:a:f0 dst_f32::blocked:acdb:f0,attr-post-ops:eltwise_relu:0:1 ,alg:convolution_direct,mb32_ic3oc64_ih224oh222kh3sh1dh0ph0_iw224ow222kw3sw1dw0pw0,20.46

After optimization of Convolution + ReLU oneDNN executes both operations within single convolution primitive.

Quantization and Inference with INT8

MXNet built with oneDNN brings outstanding performance improvement on quantization and inference with INT8 Intel CPU Platform on Intel Xeon Scalable Platform.

Next Steps and Support

  • For questions or support specific to MKL, visit the Intel MKL website.

  • For questions or support specific to oneDNN, visit the oneDNN website.

  • If you find bugs, please open an issue on GitHub for MXNet with MKL or MXNet with oneDNN.