This repo contains the code and data of two simple examples you can play with.
For Windows/OSX users, we provide a CPU-only pre-built binary package. You can install a weekly updated package directly from the R console:
install.packages("drat", repos="https://cran.rstudio.com")
drat::addRepo("dmlc")
install.packages("mxnet")
To install the mxnet R package on Linux or enable the GPU backend, please follow the instruction below:
http://mxnet.io/get_started/install.html
Besides, we highly recommend the blogs on MXNet from Azure team. It includes detailed tutorials on installation and various examples on distributed training using MXNet.
The CNN example is modified from one wonderful blog from Azure team.
However, they used 4 K80 Tesla GPUs for training the netwrok on over 2 million samples. The model and data have been tailored to get satisfactory performance using CPU on a laptop. This can be used as a starting point for people interested in MXNet.
The RNN example is done as a GSOC project in last year (I am the mentor 😆 ). Just as the CNN example, you should be able to get good performance on your laptop.