MXNet

MXNet is a deep learning library developed by Amazon. It can run on CPUs and GPUs. For this chapter, running on CPUs will suffice.

Apache MXNet is a flexible and scalable deep learning framework that supports convolutional neural networks (CNNs) and long short-term memory networks (LSTMs). It can be distributed across multiple processors/machines and achieves almost linear scale on multiple GPUs/CPUs. It is easy to install on R and it supports a good range of deep learning functionality for R. It is an excellent choice for writing our first deep learning model for image-classification.

MXNet originated at Carnegie Mellon University and is heavily supported by Amazon; they chose it as their default deep learning library in 2016. In 2017, MXNet was accepted as the Apache Incubator project, ensuring that it would remain open source software. It has a higher-level programming model similar to Keras, but the reported performance is better. MXNet is very scalable as additional GPUs are added.

To install the MXNet package for Windows, run the following code from an R session:

cran <- getOption("repos")
cran["dmlc"] <- "https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN"
options(repos = cran)
install.packages("mxnet")

This installs the CPU version; for the GPU version, you need to change the second line to:

cran["dmlc"] <- "https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/R/CRAN/GPU/cu92"

You have to change cu92 to cu80, cu90 or cu91 based on the version of CUDA installed on your machine. For other operating systems (and in case the this does not work, as things change very fast in deep learning), you can get further instructions at https://mxnet.incubator.apache.org/install/index.html.