GPU Accelerated Data Science

Data Science at the speed of thought

What is RAPIDS

RAPIDS provides unmatched speed with familiar APIs that match the most popular PyData libraries. Built on state-of-the-art foundations like NVIDIA CUDA and Apache Arrow, it unlocks the speed of GPUs with code you already know.

Learn more on the About Section


RAPIDS allows fluid, creative interaction with data for everyone from BI users to AI researchers on the cutting edge. GPU acceleration means less time and less cost moving data and training models.

Find out more from RAPIDS Use Cases

Open Source Ecosystem

RAPIDS is Open Source and available on GitHub. Our mission is to empower and advance the open-source GPU data science data engineering ecosystem.

Jump to RAPIDS on GitHub

Faster Pandas
with cuDF

cuDF is a near drop in replacement to pandas for most use cases and has greatly improved performance.

Run this benchmark yourself

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

Faster networkX
with cuGraph

cuGraph makes migration from networkX easy, accelerates graph analytics, and allows scaling far beyond existing tools.

Run this benchmark yourself

* Benchmark on AMD EPYC 7642 (using 1x 2.3GHz CPU core) w/ 512GB and NVIDIA A100 80GB (1x GPU) w/ networkX v3.0 and cuGraph v23.02

Quick Start

Quick Local Install

RAPIDS offers several installation methods, the quickest is shown below.
For more detailed guides go to our advanced install page


A. NVIDIA Pascal™ or better GPU with a compute capability 6.0 and above

B. Ubuntu 20.04 or 22.04, CentOS 7, Rocky Linux 8, or WSL2 on Windows 11

C. Recent CUDA version and NVIDIA driver pairs. Check yours with: nvidia-smi

Install with Conda

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


2. Then quick install RAPIDS with:

conda create --solver=libmamba -n rapids-23.08 -c rapidsai -c conda-forge -c nvidia rapids=23.08 python=3.10 cudatoolkit=11.8

Install with pip

1. Install via pip channels:

pip install cudf-cu11 dask-cudf-cu11 --extra-index-url=
pip install cuml-cu11 --extra-index-url=
pip install cugraph-cu11 --extra-index-url=

Install with Docker

Check that you have the required environment and then use the install selector

Install on Windows

Use the Windows WSL2 installation instructions

Do I need an Advanced Install?

If you want to use Docker, WSL2, have an uncommon OS, hardware configuration, environment, or need specific versions such as CUDA 11.5 or Python 3.8 then you should use the installation selector in our Advanced Installations Page

Try RAPIDS Online

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

Google CoLab

LAUNCH Google CoLab (pip)

Jump right into a GPU enabled notebook environment with required account. Also available with Conda.

Amazon Sagemaker Studio Lab

LAUNCH Studio Lab

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


LAUNCH 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

Go To 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 and guides on the User Guides Page



NVIDIA's industry leading hardware provides the platform for RAPIDS high performance. Get details on the newest GPUs, server architectures, and cloud offerings in our Ecosystem Hardware Section


Find out details on featured RAPIDS projects like cuDF, cuML, cuGraph, and more. Also learn about those using our integrated with RAPIDS in our Ecosystem Software Section


Get involved with RAPIDS projects, reach out to its developers, find maintainer and contribution guides in our Ecosystem Developers Section

Open Source

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

Apache Arrow Dask nuclio Numba scikitlearn XGboost

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 booz-allen-hamilton databricks graphistry h20ai ibm iguzaio inria kinetica paperspace
plotly dash preferred-networks pytorch ray saturn-cloud uber ursa
anaconda capitalOne chainer cuPY deepwave-digital gunrock nvidia quantsight walmart

Learn More


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

Use Cases

Hear about success stories, resources for integrating RAPIDs workflows in your business, and deployment strategies in our Use Cases Section

Get Involved

Use RAPIDS directly or through NVIDIA AI Enterprise, which provides extensive optimization, certified hardware profiles, and direct IT support. Find additional business resources, community resources, and guides for RAPIDS evangelism in our Get Involved Section

Latest News

RAPIDS Twitter

Follow the latest from the RAPIDS Twitter community with @RAPIDSai

RAPIDS Support Notices

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


Find our highlighted content, including videos, talks, posts, guides and more in the Featured Content Page

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