This page is obsolete, and MXNet is not officially supported.

MXNet is a deep learning framework that has been ported to the HIP port of MXNet. It works both on HIP/ROCm and HIP/CUDA platforms. Mxnet makes use of rocBLAS,rocRAND,hcFFT and MIOpen APIs.

It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines.

MXNet is more than a deep learning project. It is a collection of blue prints and guidelines for building deep learning systems, and interesting insights of DL systems for hackers.

Installation Guide for MXNet library


GCC 4.8 or later to compile C++ 11. GNU Make

Install Dependencies to build mxnet for HIP/ROCm

sudo apt install -y rocm-device-libs rocm-libs rocblas hipblas rocrand rocfft
  • Install ROCm opencl

sudo apt install -y rocm-opencl rocm-opencl-dev
  • Install MIOpen for acceleration

sudo apt install -y miopengemm miopen-hip
  • Install rocthrust,rocprim, hipcub Libraries

sudo apt install -y rocthrust rocprim hipcub

Install Dependencies to build mxnet for HIP/CUDA

Install CUDA following the NVIDIA’s installation guide to setup MXNet with GPU support


  • Make sure to add CUDA install path to LD_LIBRARY_PATH

  • Example - export LD_LIBRARY_PATH=/usr/local/cuda/lib64/:$LD_LIBRARY_PATH

Install the dependencies hipblas, rocrand, hcfft from source.

Build the MXNet library

Step 1: Install build tools.

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

Step 2: Install OpenBLAS. MXNet uses BLAS and LAPACK libraries for accelerated numerical computations on CPU machine. There are several flavors of BLAS/LAPACK 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 liblapack-dev libomp-dev libatlas-dev libatlas-base-dev

Step 3: Install OpenCV. Install OpenCV <>`_ here. 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
$ cd mxnet
$ export PATH=/opt/rocm/bin:$PATH

Step 5:

To compile on HCC PLATFORM(HIP/ROCm):

$ export HIP_PLATFORM=hcc


$ export HIP_PLATFORM=nvcc

Step 6: To enable MIOpen for higher acceleration :


Step 7: If building on CPU:

make -jn(n=number of cores) USE_GPU=0 (For Ubuntu 16.04)
make -jn(n=number of cores)  CXX=g++-6 USE_GPU=0 (For Ubuntu 18.04)

If building on GPU:

make -jn(n=number of cores) USE_GPU=1 (For Ubuntu 16.04)
make -jn(n=number of cores)  CXX=g++-6 USE_GPU=1 (For Ubuntu 18.04)

On succesfull compilation a library called is created in mxnet/lib path.

  1. USE_CUDA(to build on GPU), USE_CUDNN(for acceleration) flags can be changed in make/

  2. To compile on HIP/CUDA make sure to set USE_CUDA_PATH to right CUDA installation path in make/ In most cases it is - /usr/local/cuda.

Install the MXNet Python binding

Step 1: Install prerequisites - python, setup-tools, python-pip and numpy.

$ sudo apt-get install -y python-dev python-setuptools python-numpy python-pip python-scipy
$ sudo apt-get install python-tk
$ sudo apt install -y fftw3 fftw3-dev pkg-config

Step 2: Install the MXNet Python binding.

$ cd python
$ sudo python install

Step 3: Execute sample example

$ cd example/
$ cd bayesian-methods/

To run on gpu change mx.cpu() to mx.gpu() in python script (Example-

$ python