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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:
Thanks for checking out our new package!
— Navdeep Gill , Erin LeDell , and Yuan Tang
At H2O.ai, democratizing AI isn’t just an idea. It’s a movement. And that means that it requires action. We started out as a group of like minded individuals in the open source community, collectively driven by the idea that there should be freedom around the creation and use of AI.
Today we have evolved into a global company built by people from a variety of different backgrounds and skill sets, all driven to be part of something greater than ourselves. Our partnerships now extend beyond the open-source community to include business customers, academia, and non-profit organizations.
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