Math Library Selection¶

It is useful to consider your math library selection prior to your other prerequisites. MXNet relies on the BLAS (Basic Linear Algebra Subprograms) library for numerical computations. Those can be extended with LAPACK (Linear Algebra Package), an additional set of mathematical functions.

MXNet supports multiple mathematical backends for computations on the CPU:

• Apple Accelerate
• ATLAS
• MKL (MKL, MKLML)
• MKL-DNN
• OpenBLAS

The default order of choice for the libraries if found follows the path from the most (recommended) to less performant backends. The following lists show this order by library and cmake switch.

For desktop platforms (x86_64):

1. MKL-DNN (submodule) | USE_MKLDNN
2. MKL | USE_MKL_IF_AVAILABLE
3. MKLML (downloaded) | USE_MKLML
4. Apple Accelerate | USE_APPLE_ACCELERATE_IF_AVAILABLE | Mac only
5. OpenBLAS | BLAS | Options: Atlas, Open, MKL, Apple

Note: If USE_MKL_IF_AVAILABLE is set to False then MKLML and MKL-DNN will be disabled as well for configuration backwards compatibility.

For embedded platforms (all other and if cross compiled):

1. OpenBLAS | BLAS | Options: Atlas, Open, MKL, Apple

You can set the BLAS library explicitly by setting the BLAS variable to:

• Atlas
• Open
• MKL
• Apple

See the cmake/ChooseBLAS.cmake file for the options.

Intel’s MKL (Math Kernel Library) is one of the most powerful math libraries https://software.intel.com/en-us/mkl

It has following flavors:

• MKL is a complete math library, containing all the functionality found in ATLAS, OpenBlas and LAPACK. It is free under community support licensing (https://software.intel.com/en-us/articles/free-mkl), but needs to be downloaded and installed manually.
• MKLML is a subset of MKL. It contains a smaller number of functions to reduce the size of the download and reduce the number of dynamic libraries user needs.

• MKL-DNN is a separate open-source library, it can be used separately from MKL or MKLML. It is shipped as a subrepo with MXNet source code (see 3rdparty/mkldnn or the MKL-DNN project)

Since the full MKL library is almost always faster than any other BLAS library it’s turned on by default, however it needs to be downloaded and installed manually before doing cmake configuration. Register and download on the Intel performance libraries website.

Note: MKL is supported only for desktop builds and the framework itself supports the following hardware:

• Intel® Xeon Phi™ processor
• Intel® Xeon® processor
• Intel® Core™ processor family
• Intel Atom® processor

If you have a different processor you can still try to use MKL, but performance results are unpredictable.

Install GPU Software¶

If you want to run MXNet with GPUs, you must install NVDIA CUDA and cuDNN.

Install Optional Software¶

These might be optional, but they’re typically desirable as the extend or enhance MXNet’s functionality.

• OpenCV - Image Loading and Augmentation. Each operating system has different packages and build from source options for OpenCV. Refer to your OS’s link in the Build Instructions by Operating System section for further instructions.
• NCCL - NVIDIA’s Collective Communications Library. Instructions for installing NCCL are found in the following Build MXNet with NCCL section.

More information on turning these features on or off are found in the following build configurations section.

Build MXNet with NCCL¶

• Download and install the latest NCCL library from NVIDIA.
• Note the directory path in which NCCL libraries and header files are installed.
• Ensure that the installation directory contains lib and include folders.
• Ensure that the prerequisites for using NCCL such as Cuda libraries are met.
• Append the config.mk file with following, in addition to the CUDA related options.
• USE_NCCL=1
• USE_NCCL_PATH=path-to-nccl-installation-folder

echo "USE_NCCL=1" >> make/config.mk
echo "USE_NCCP_PATH=path-to-nccl-installation-folder" >> make/config.mk
cp make/config.mk .

• Run make command

make -j"$(nproc)"

@unittest.skip("Test requires NCCL library installed and enabled during build")

nosetests --verbose tests/python/gpu/test_nccl.py

Build MXNet with C++¶

• To enable C++ package, just add USE_CPP_PACKAGE=1 when you run make or cmake (see examples).

Usage Examples¶

For example, you can specify using all cores on Linux as follows:

mkdir build && cd build
cmake -GNinja ..
ninja -v

mkdir build && cd build
cmake -DUSE_CUDA=1 -DUSE_CUDA_PATH=/usr/local/cuda -DUSE_CUDNN=1 -DUSE_MKLDNN=1 -GNinja ..
ninja -v

mkdir build && cd build
cmake -DBLAS=open -DUSE_CUDA=1 -DUSE_CUDA_PATH=/usr/local/cuda -DUSE_CUDNN=1 -GNinja ..
ninja -v

mkdir build && cd build
cmake -DUSE_CUDA=0 -DUSE_MKLDNN=1 -GNinja ..
ninja -v

mkdir build && cd build
cmake -DUSE_CUDA=0 -DBLAS=open -GNinja ..
ninja -v

mkdir build && cd build
cmake -DUSE_OPENCV=0 -GNinja ..
ninja -v

mkdir build && cd build
cmake -DBLAS=apple -DUSE_OPENCV=0 -DUSE_OPENMP=0 -GNinja ..
ninja -v

brew install llvm
mkdir build && cd build
cmake -DBLAS=apple -DUSE_OPENMP=1 -GNinja ..
ninja -v