GPU Accelerated Data Science
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 SectionRAPIDS 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 CasesRAPIDS 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 GitHubcuDF is a near drop in replacement to pandas for most use cases and has greatly improved performance.
* 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
cuML brings huge speedups to ML modeling with an API that matches scikit-learn.
* 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
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
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
1. If not installed, download and run the install script.
This will install the latest miniconda:
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh2. 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=11.81. Install via pip channels:
pip install cudf-cu11 dask-cudf-cu11 --extra-index-url=https://pypi.nvidia.com
pip install cuml-cu11 --extra-index-url=https://pypi.nvidia.com
pip install cugraph-cu11 --extra-index-url=https://pypi.nvidia.comCheck that you have the required environment and then use the install selector
Use the Windows WSL2 installation instructions
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
Don't have access to a GPU system right now? Try out RAPIDS with cloud based hardware from one of these featured channels:
Jump right into a GPU enabled notebook environment with required account. Also available with Conda.
Enables notebook based environments in a free trial with required account.
Microsoft Azure Cloud infrastructure and services are available with RAPIDS.
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
RAPIDS would not be possible without the collaboration of these important open source projects. Click on a logo to learn more:
RAPIDS has a strong ecosystem of adopters and contributors in a variety of industries and communities. Click on a logo to 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
Hear about success stories, resources for integrating RAPIDs workflows in your business, and deployment strategies in our Use Cases Section
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
Follow the latest from the RAPIDS Twitter community with @RAPIDSai
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
