GPU Accelerated Data Science

What is RAPIDS

RAPIDS provides unmatched speed with familiar APIs that match the most popular PyData libraries.Built on state-of-the-art foundations likeNVIDIA CUDA andApache Arrow, it unlocks the speed of GPUs with code you already know.Jump to About Section

Why Use RAPIDS

RAPIDS allows fluid, creative interaction with data for everyone from BI users to AI researcherson thecutting edge. GPU acceleration means less time and less cost moving data and training models.Jump to RAPIDS Use Cases

Open Source Ecosystem

RAPIDS is Open Source and available onGitHub.Our mission is to empower and advance the open-source GPU data science data engineeringecosystem.Jump to RAPIDS GitHub


Pandas Accelerator Mode for cuDF

Use cuDF pandas Accelerator Mode to speed up pandas workflows with zero code change.Learn More on the Accelerator Mode Page

Polars GPU Engine powered by cuDF

Accelerate Polars by enabling the GPU engine with zero code change.Learn More on the Launch Page

Accelerated scikit-learn with cuML

Run machine learning models faster with zero code change.Learn More on the Accelerated ML Page

NetworkX Supercharged by cuGraph

Speed up your large-scale graph workflows with zero code change.Learn More on the nx-cugraph Page


Faster Pandas
with cuDF

cuDF accelerates pandas with zero code changes and brings greatly improved performance.


Run this benchmark yourself

* Benchmark on AMD EPYC 7642 (using 1x 2.3GHz CPU core) w/ 512GB andNVIDIA A100 80GB (1x GPU) w/ pandas v1.5 and cuDF v23.02

Faster scikit-learn
with cuML

cuML brings huge speedups to ML modeling with an API that matches scikit-learn.


Run this benchmark yourself

* Benchmark on AMD EPYC 7642 (using 1x 2.3GHz CPU core) w/ 512GB andNVIDIA A100 80GB (1x GPU) w/ scikit-learn v1.2 and cuML v23.02

Faster NetworkX
with cuGraph

cuGraph accelerates NetworkX with zero code changes for much greater performance at scale.


Run this benchmark yourself

* Benchmark on Intel(R) Xeon(R) w9-3495X w/ 250 GB andNVIDIA A100 80GB (1x GPU) w/ NetworkX v3.4.1 and cuGraph/nx-cugraph v24.10;WCC = Weakly Connected Components; Betweenness = Betweenness Centrality with k=100

Quick Start

Quick Local Install

RAPIDS offers several installation methods, the quickest is shown below.


For more information, refer to theRAPIDS Installation Guide

Requirements

A. NVIDIA Volta™ or higher GPU withcompute capability 7.0+

B.Compatible Linux distribution orWSL2 on Windows 11

C. RecentCUDA versionand NVIDIA driver pairs. Check yours with:nvidia-smi

SeeSystem Requirements for details.

Install with Conda

1. If not installed, download and run the install script.
This will install the latest miniforge:

wget "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"bash Miniforge3-$(uname)-$(uname -m).sh

2. Then quick install RAPIDS with:

conda create -n rapids-25.04 -c rapidsai -c conda-forge -c nvidia rapids=25.04 python=3.12 cuda-version=12.8

Install with pip

Install via the NVIDIA PyPI index:

pip install \  --extra-index-url=https://pypi.nvidia.com \  cudf-cu12==25.4.* \  dask-cudf-cu12==25.4.* \  cuml-cu12==25.4.* \  cugraph-cu12==25.4.*

Install with Docker

Check that you have therequiredenvironment and then use theinstall selector

Install on Windows

Use the WindowsWSL2 installationinstructions

RAPIDS Release Selector

Please see theRAPIDS Installation Guidefor the interactiverelease selector with more options,detailed installation steps, and information about supported platforms.

Test Drive cuDF

Try out cuDF pandas Accelerator Mode, with a free required account, right now bylaunching Google Colab



Try RAPIDS Online

Don't have access to a GPU system right now? Try out all of the RAPIDS libraries with cloud based hardware from one ofthese featured channels:


Google CoLab

Jump right into a GPU enabled RAPIDS notebook environment with a free required account.

Studio Lab

Enables Amazon Sagemaker notebook based environments in a free trial with required account.

Paperspace

Use Quick Start Instances through a limited free account.

NVIDIA Launchpad

Free short term use to try and learn with hands-on lab environment.

Microsoft Azure

Microsoft Azure Cloud infrastructure and services are available with RAPIDS.

Oracle Cloud

Oracle Cloud infrastructure and services are available with RAPIDS.

IBM Cloud

IBM Cloud infrastructure and services are available with RAPIDS.

User Guides and Tutorials

After installing, the best place to start is by looking through our more detailed tutorials andguides on theUser Guides Page

Ecosystem

Hardware

NVIDIA's industry leading hardware provides the platform for RAPIDS high performance. Getdetails on thenewest GPUs, server architectures, and cloud offerings in ourEcosystemHardware Section

Software

Find out details on featured RAPIDS projects like cuDF, cuML, cuGraph, and more. Also learn aboutthose using our integrated with RAPIDS in ourEcosystemSoftware Section

Developers

Get involved with RAPIDS projects, reach out to its developers, find maintainer and contributionguidesin ourEcosystem Developers Section

Open Source

RAPIDS would not be possible without the collaboration of these important open source projects. Click on a logo tolearn more:

Apache Arrow LogoDask LogoNetworkX LogoNuclio LogoNumba Logoscikit-learn LogoXGBoost Logo

Adopters and Contributors

RAPIDS has a strong ecosystem of adopters and contributors in a variety of industries and communities. Click on a logo to learn more:

Anyscale LogoBooz Allen Hamilton LogoDatabricks LogoGraphistry LogoH2O.ai LogoIBM Cloud LogoInria LogoKinetica LogoPaperspace Logo
Plotly Dash LogoPreferred Networks LogoPyTorch LogoRay LogoSaturn Cloud LogoUber LogoUrsa Labs Logo
Anaconda LogoCapital One LogoChainer LogoCuPy LogoDeepwave Digital LogoGunrock LogoNVIDIA LogoQuansight LogoWalmart Logo

Learn More

About RAPIDS

Learn more about RAPIDS' start with Apache Arrow and GoAi. Also find an overview of thecapabilities of RAPIDS, as well as featured projects in ourAbout Section

Use Cases

Hear about success stories, resources for integrating RAPIDS workflows in your business, anddeployment strategies in ourUse Cases Section

Get Involved

Use RAPIDS directly or throughNVIDIA AI Enterprise, which provides extensive optimization, certifiedhardware profiles, and direct IT support. Find additional business resources, communityresources, and guides for RAPIDS evangelism in ourGet Involved Section

Latest News

RAPIDS X/Twitter

Follow the latest from the RAPIDS X/Twitter community with@RAPIDSai

RAPIDS Support Notices

Get the full list of developer updates and notices (RSN) that may affect your projects on theRSN Docs Page

RAPIDS News

Find our highlighted content, including talks, posts, guides and more on theNVIDIA Dev Blog andRAPIDS Blog

Latest Posts