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Build and Installation

Minimal system requirement:

  • recent c++ compiler supporting C++ 11 such as g++ >= 4.8
  • git
  • BLAS library.
  • opencv

On Ubuntu >= 13.10, one can install them by

sudo apt-get update
sudo apt-get install -y build-essential git libblas-dev libopencv-dev

Then build mxnet

git clone --recursive https://github.com/dmlc/mxnet
cd mxnet; make -j4

To install the python package, first make sure python >= 2.7 and numpy >= ? are installed, then

cd python; python setup.py install

If anything goes well, now we can train a multilayer perceptron on the hand digit recognition dataset.

cd ..; python example/mnist/mlp.py

Advanced Build

  • update the repo:
git pull
git submodule update
  • install python package in developing model,
cd python; python setup.py develop --user

  • modify the compiling options such as compilers, CUDA, CUDNN, Intel MKL, various distributed filesystem such as HDFS/Amazon S3/...

    First copy make/config.mk to the project root, then modify the according flags.

Build in Visual Studio 2013

Firstly, we should make your Visual Studio 2013 support more C++11 features.

  • Download and install Visual C++ Compiler Nov 2013 CTP.
  • Copy all files in C:\Program Files (x86)\Microsoft Visual C++ Compiler Nov 2013 CTP (or the folder where you extracted the zip archive) to C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC and overwrite all existed files. Don't forget to backup the original files before copying.

Secondly, fetch the third-party libraries, including OpenCV, CuDNN and OpenBlas(ignore this if you have MKL).

  • NOTICE: You need to register as a NVIDIA community user to get the download link of CuDNN.

Finally, use CMake to create a Visual Studio solution in ./build/. During configuration, you may need to set the path of each third-party library, until no error is reported. Open the solution and compile, you will get a mxnet.dll in ./build/Release or ./build/Debug.

The following steps are the same with Linux.