In September, H2O.ai released a new open source software project for GPU machine learning called H2O4GPU . The initial release (blog post here ) included a Python module with a scikit-learn compatible API, which allows it to be used as a drop-in replacement for scikit-learn with support for GPUs on selected (and ever-growing) algorithms. We are proud to announce that the same collection of GPU algorithms is now available in R, and the h2o4gpu  R package is available on CRAN .
 
The R package makes use of RStudio’s reticulate  R package for facilitating access to Python libraries through R. Reticulate embeds a Python session within your R session, enabling seamless, high-performance interoperability and was originally created by RStudio in an effort to bring the TensorFlow Python library into R.
 
This is exciting news for the R community, as h2o4gpu  is the first machine learning package that brings together a diverse collection of supervised and unsupervised GPU-powered algorithms in a unified interface. The initial collection of algorithms includes:

• Random Forest, Gradient Boosting Machine (GBM), Generalized Linear Models (GLM) with Elastic Net regularization
• K-Means, Principal Component Analysis (PCA), Truncated SVD

• Here is the main GitHub repo. If you like the package, please ? the repo on GitHub!
• If you’re looking to contribute, check out the CONTRIBUTING.md file.
• All open issues that are specific to the R package are here.
• All open issues are here.

Thanks for checking out our new package!
— Navdeep Gill , Erin LeDell , and Yuan Tang