Minimum Requirements¶

You must have the following:

• A C++ compiler that supports C++ 11. The C++ compiler compiles and builds MXNet source code. Supported compilers include the following:

• G++ (4.8 or later)

• Clang

• A BLAS (Basic Linear Algebra Subprograms) library. BLAS libraries contain routines that provide the standard building blocks for performing basic vector and matrix operations. You need a BLAS library to perform basic linear algebraic operations. Supported BLAS libraries include the following:

  • libblas
  • openblas
  • Intel MKL

• Graphviz for visualizing the network graphs.

• Jupyter Notebook for running examples and tutorials of MXNet.

Requirements for Using GPUs¶

• A GPU with support for Compute Capability 2.0 or higher. Compute capability describes which features are supported by CUDA hardware. For a list of features supported by each compute capability, see CUDA Version features and specifications. For a list of features supported by NVIDIA GPUs, see CUDA GPUs.
• The CUDA Toolkit 7.0 or higher. The CUDA Toolkit is an environment that allows MXNet to run on NVIDIA GPUs. It includes a compiler, math libraries, and debugging tools. To download the latest version, see CUDA Toolkit download page.
• The CuDNN (CUDA Deep Neural Network) library. The CuDNN library accelerates GPU computation by providing low-level GPU performance tuning. To download the latest version, see CUDA Deep Neural Network.

Requirements for Computer Vision and Image Augmentation Support¶

If you need to support computer vision and image augmentation, you need OpenCV. The Open Source Computer Vision (OpenCV) library contains programming functions for computer vision and image augmentation. For more information, see OpenCV.

Building GCC from Source Code¶

To build the GNU Compiler Collection (GCC) from source code, you need the 32-bit libc library.

1. Install libc with one of the following system-specific commands:

       sudo apt-get install libc6-dev-i386 # In Ubuntu
       sudo yum install glibc-devel.i686   # In RHEL (Red Hat Linux)
       sudo yum install glibc-devel.i386   # In CentOS 5.8
       sudo yum install glibc-devel.i686   # In CentOS 6/7
   

2. To download and extract the GCC source code, run the following commands:

       wget http://mirrors.concertpass.com/gcc/releases/gcc-4.8.5/gcc-4.8.5.tar.gz
       tar -zxf gcc-4.8.5.tar.gz
       cd gcc-4.8.5
       ./contrib/download_prerequisites
   

3. To build GCC, run the following commands:

       mkdir release && cd release
       ../configure --prefix=/usr/local --enable-languages=c,c++
       make -j10
       sudo make install
   

4. Add the lib path to your ~/.bashrc file:

       export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/lib64
   

Build OpenCV from Source Code¶

To build OpenCV from source code, you need the cmake library .

• If you don’t have cmake or if your version of cmake is earlier than 3.6.1 (such as the default version of cmake on RHEL), run the following commands to install a newer version of cmake:

      wget https://cmake.org/files/v3.6/cmake-3.6.1-Linux-x86_64.tar.gz
      tar -zxvf cmake-3.6.1-Linux-x86_64.tar.gz
      alias cmake="cmake-3.6.1-Linux-x86_64/bin/cmake"
  

• To download and extract the OpenCV source code, run the following commands:

      wget https://codeload.github.com/opencv/opencv/zip/2.4.13
      unzip 2.4.13
      cd opencv-2.4.13
      mkdir release
      cd release/
  

• Build OpenCV. The following commands build OpenCV without GPU support, which might significantly slow down an MXNet program running on a GPU processor. It also disables 1394 which might generate a warning:

      cmake -D BUILD_opencv_gpu=OFF -D WITH_CUDA=OFF -D WITH_1394=OFF -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local ..
      make -j8
      sudo make install
  

• Add the lib path to your ~/.bashrc file:

      export PKG_CONFIG_PATH=$PKG_CONFIG_PATH:/usr/local/lib/pkgconfig/
  

General¶

Message: ImportError: No module named _graphviz

Cause: Graphviz is not installed.

Solution: On Mac, You can install Graphviz with below command

  brew install graphviz

Or, using pip

  brew install python
  pip install graphviz

Message: RuntimeError: failed to execute [‘dot’, ‘-Tsvg’], make sure the Graphviz executables are on your systems’ path

Cause: Graphviz executable (lib) path is currently not in the system path and program is unable to use Graphviz for plotting the graph

Solution: Add Graphviz executable (lib) path to your system path. On Mac/Linux machines, Graphviz is generally installed in - /usr/local/lib/graphviz/ or /usr/lib/graphviz/ or /usr/lib64/graphviz/ and on Windows - C:\Program Files (x86)\Graphviz2.38\bin.

Note If you are using Jupyter notebook, you may need to restart the kernel to refresh the system path and find Graphviz executable.

Mac OS X Error Message¶

Message: link error ld: library not found for -lgomp

Cause: The GNU implementation of OpenMP is not in the operating system’s library path.

Solution: Add the location of OpenMP to the library path:

• Create the locate database by running the following command:

      sudo launchctl load -w /System/Library/LaunchDaemons/com.apple.locate.plist
  

• Locate the OpenMP library by running the following command:

      locate libgomp.dylib
  

• To add OpenMP to your library path, replace path1 in the following command with the output from the last command:

      ln -s path1 /usr/local/lib/libgomp.dylib
  

• To build your changes, run the following command:

      make -j$(sysctl -n hw.ncpu)
  

R Error Message¶

Message Unable to load mxnet after enabling CUDA

Solution: If you enabled CUDA when you installed MXNet, but can’t load mxnet, add the following lines to your $RHOME/etc/ldpaths environment variable:

export CUDA_HOME=/usr/local/cuda
export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${LD_LIBRARY_PATH}

Note: To find your $RHOME environment variable, use the R.home() command in R.