linux下安装mxnet和python接口配置

Build the MXNet core shared library

Step 1 Install build tools and git.

$ sudo apt-get update$ sudo apt-get install -y build-essential git

Step 2 Install OpenBLAS.

MXNet uses BLAS library for accelerated numerical computations. There are several flavors of BLAS libraries -OpenBLAS,ATLAS and MKL. In this step we install OpenBLAS. You can choose to install ATLAS or MKL.

$ sudo apt-get install -y libopenblas-dev

Step 3 Install OpenCV.

MXNet uses OpenCV for efficient image loading and augmentation operations.

$ sudo apt-get install -y libopencv-dev

Step 4 Download MXNet sources and build MXNet core shared library.

$ git clone --recursive https://github.com/dmlc/mxnet$ cd mxnet$ make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas USE_CUDA=1 USE_CUDA_PATH=/usr/local/cuda USE_CUDNN=1

Note - USE_OPENCV, USE_BLAS, USE_CUDA, USE_CUDA_PATH AND USE_CUDNN are make file flags to set compilation options to use OpenCV, OpenBLAS, CUDA and cuDNN libraries. You can explore and use more compilation options inmake/config.mk. Make sure to set USE_CUDA_PATH to right CUDA installation path. In most cases it is -/usr/local/cuda.

Build the MXNet Python binding

Step 1 Install prerequisites - python setup tools and numpy.

$ sudo apt-get install -y python-dev python-setuptools python-numpy

Step 2 Build the MXNet Python binding.

$ cd python$ sudo python setup.py install

Step 3 Install Graphviz. (Optional, needed for graph visualization using mxnet.viz package).

sudo apt-get install graphvizpip install graphviz

Step 4 Validate the installation by running simple MXNet code describedhere.

参考网址： http://mxnet.io/get_started/install.html