Get Started

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.

Intended audience

Community

Developers

Installation Tool

Rapids.AI

Contains a configurator tool to help you choose between the various methods for installing RAPIDS.

10 Minutes to cuDF

cuDF Docs

Modeled after 10 Minutes to Pandas, this is a short introduction to cuDF that is geared mainly for new users.

GDF CheatSheet

PDF Download

Handy PDF reference guide for handling GPU Data Frames (GDF) with cuDF.

Linear Models with cuDF and cuML XGBoost

Blog Post

A robust blog post with notebook example using RAPIDS libraries for linear models.

Our Collection of Example NoteBooks

Github Readme

Github repository with examples of cuML using knn, dbscan, pca and tsvd, the End-to-End Mortgage demo, cuGraph demos, and more.