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.
Handy PDF reference guide for handling GPU Data Frames (GDF) with cuDF.
10 Minutes to cuDF and Dask-cuDF
Modeled after 10 Minutes to Pandas, this is a short introduction to cuDF that is geared mainly for new users.
RAPIDS Spark Examples
A repo for Spark related utilities and examples using the Rapids Accelerator, including ETL, ML/DL, etc.
Our Collection of Example NoteBooks
A Github repository with our introductory examples of XGBoost, cuML demos, cuGraph demos, and more.
Our Extended Collection of Example NoteBooks
A Github repository with examples of XGBoost, cuML demos, cuGraph demos, and more.