Getting ready

The MXNet package is a lightweight deep learning architecture supporting multiple programming languages such as R, Python, and Julia. From a programming perspective, it is a combination of symbolic and imperative programming with support for CPU and GPU.

The CPU-based MXNet in R can be installed using the prebuilt binary package or the source code where the libraries need to be built. In Windows/mac, prebuilt binary packages can be download and installed directly from the R console. MXNet requires the R version to be 3.2.0 and higher. The installation requires the drat package from CRAN. The drat package helps maintain R repositories and can be installed using the install.packages() command.

To install MXNet on Linux (13.10 or later), the following are some dependencies:

• Git (to get the code from GitHub)
• libatlas-base-dev (to perform linear algebraic operations)
• libopencv-dev (to perform computer vision operations)

To install MXNet with a GPU processor, the following are some dependencies:

• Microsoft Visual Studio 2013
• The NVIDIA CUDA Toolkit
• The MXNet package
• cuDNN (to provide a deep neural network library)

Another quick way to install mxnet with all the dependencies is to use the prebuilt Docker image from the chstone repository. The chstone/mxnet-gpu Docker image will be installed using the following tools:

• MXNet for R and Python
• Ubuntu 16.04
• CUDA (Optional for GPU)
• cuDNN (Optional for GPU)