nstall baidu warpctc

  
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

  
  
 
 


cd ~/  git clone https://github.com/baidu-research/warp-ctc  cd warp-ctc  mkdir build  cd build  cmake ..  make  sudo make install

Enable warpctc in mxnet

  comment out following lines in make/config.mk
  WARPCTC_PATH = $(HOME)/warp-ctc
  MXNET_PLUGINS += plugin/warpctc/warpctc.mk
  
  rebuild mxnet by
  make clean && 
make -j $(nproc) USE_OPENCV=1 USE_BLAS=openblas USE_CUDA=1 USE_CUDA_PATH=/usr/local/cuda USE_CUDNN=1