The RAPIDS data science framework is a collection of libraries for running end-to-end data science pipelines completely on the GPU. The interaction is designed to have a familiar look and feel to working in Python, but utilizes optimized NVIDIA® CUDA® primitives and high-bandwidth GPU memory under the hood. Below are some links to help getting started with the RAPIDS libraries.
Contains a configurator tool to help you choose between the various methods for installing RAPIDS.
10 Minutes to cuDF
Modeled after 10 Minutes to Pandas, this is a short introduction to cuDF that is geared mainly for new users.
Handy PDF reference guide for handling GPU Data Frames (GDF) with cuDF.
Linear Models with cuDF and cuML XGBoost
A robust blog post with notebook example using RAPIDS libraries for linear models.
Our Collection of Example NoteBooks
Github repository with examples of cuML using knn, dbscan, pca and tsvd, the End-to-End Mortgage demo, cuGraph demos, and more.