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.

Installation Tool

Rapids.AI

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

GDF CheatSheet

PDF Download

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

10 Minutes to cuDF

cuDF Post

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

10 Minutes to Dask-XGBoost

Dask-XGBoost Post

A short introduction to XGBoost with a distributed CUDA DataFrame via Dask-cuDF.

Using RAPIDS + XGBoost4J-Spark

Github Repo

Getting started guide and source code for XGBoost4J-Spark, along with notebooks and examples.

Multi-GPU with Dask-cuDF

Dask-cuDF Post

Overview of using Dask for Multi-GPU cuDF solutions, on both a single machine or multiple GPUs across many machines in a cluster.

Our Collection of Example NoteBooks

Github Repo

A Github repository with our introductory examples of XGBoost, cuML demos, cuGraph demos, and more.

Our Extended Collection of Example NoteBooks

Github Repo

A Github repository with examples of XGBoost, cuML demos, cuGraph demos, and more.