Ecosystem
RAPIDS runs on hardware powered by CUDA, the fundamental set of tools for GPU computing built by NVIDIA. RAPIDS works on PCs, workstations, data centers, and the cloud. Learn more about the different types of hardware and find the best fit for your use case on NVIDA's product page for Data Science Hardware
Set on a foundation of powerful hardware and parallelized by CUDA, the RAPIDS ecosystem is based on core projects, those extending its capabilities, and those built on top of RAPIDS.
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We hope you will consider integrating RAPIDS into your solution or workflow. RAPIDS is built for Python and C++ developers alike. Build something great with RAPIDS.
Jump to SectionFeatured components of the RAPIDS ecosystem.
cuDF is a library for data engineering and data analysis closely modeled on the game-changing Pandas API. Speed up your ETL and analysis pipeline with minimal code changes.
GitHublibcudf provides the CUDA C++ building blocks powering cuDF.
GitHubcuML is a library for executing machine learning algorithms on GPUs with an API that closely follows the Scikit-Learn API. Reduce the time to fit your model from hours or minutes to seconds.
GitHublibcuml provides the CUDA C++ building blocks powering cuML.
GitHubcuGraph is a library to make graph analytics available to everyone. With a Python API that follows NetworkX, you can analyze networks of millions of nodes without specialized software faster than ever.
GitHublibcugraph provides the CUDA C++ building blocks powering cuGraph.
GitHubcuSpatial is a library for lightning-fast spatial analytics. Fully integrated with GeoPandas and growing fast, this toolkit will feel familiar to Python users of GIS tools.
GitHublibcuspatial provides the CUDA C++ building blocks powering cuSpatial.
GitHubRAFT contains CUDA accelerated primitives for rapidly composing analytics, and is used by other RAPIDS libraries. Its building blocks include dense algebra, sparse algebra, spatial computations, clustering, solvers, and statistics.
GitHubQuickly visualize and filter through large datasets, with a few lines of code, using best in class charting libraries.
GitHubTake full advantage of GPU Direct Storage (GDS) through powerful bindings to cuFile.
GitHublibkvikio provides the CUDA C++ building blocks powering kvikIO.
GitHubcuSignal accelerates manipulation and analysis of signal data in a Scipy Signal-like API.
GitHubGreatly accelerate your computer vision and image processing tasks, especially for biomedical imaging.
GitHubVisit our GitHub organization page or our documentation for a more extensive list of projects.
Projects and services that have RAPIDS acceleration as an added feature.
NVIDIA is bringing RAPIDS to Apache Spark to accelerate ETL workflows with no code change, improving performance, and reducing total costs.
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Scale out your workload onto multiple GPUs and multiple machines with Dask. RAPIDS also develops the Dask-CUDA library for GPU-friendly cluster deployment
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XGBoost is the gold standard in single model performance for classification and regression. RAPIDS was built from the start to integrate with this amazing algorithm.
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Scale out workloads with Dask via tried-and-true SQL queries on your data.
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RAPIDS integrates with popular and powerful visualization projects, such as Plotly Dash, HoloViz, Bokeh, and more.
RAPIDS Visualization Guide
RAPIDS is built to be fast where ever it runs. Learn how to run RAPIDS on all the major cloud providers, with open-source Hyperparameter Optimization tools, and High Performance Computing like Slurm.
Deployment DocumentationCutting edge software built with RAPIDS.
Triton Inference Server, part of the NVIDIA AI platform, streamlines and standardizes AI inference by enabling teams to deploy, run, and scale trained AI models from any framework on any GPU or CPU-based infrastructure.
GitHubNVIDIA Merlin is an open source library providing end-to-end GPU-accelerated recommender systems. Merlin leverages RAPIDS cuDF and Dask cuDF for dataframe transformation during ETL and inference, as well as for the optimized dataloaders in TensorFlow, PyTorch or HugeCTR to accelerate deep learning training.
GitHubNVIDIA cuOpt is an operations research optimization API using AI to help developers create complex, real-time fleet routing. These APIs can be used to solve complex routing problems with multiple constraints and deliver new capabilities, like dynamic rerouting, job scheduling, and robotic simulations, with subsecond solver response time.
Learn MoreNVIDIA Morpheus is an open application framework that enables cybersecurity developers to create optimized AI pipelines for filtering, processing, and classifying large volumes of real-time data.
GitHubNVIDIA Clara is a platform of AI applications and accelerated frameworks for healthcare developers, researchers, and medical device makers creating AI solutions to improve healthcare delivery and accelerate drug discovery.
Learn MoreRAPIDS cuCIM has been integrated into the MONAI Transforms component to accelerate the data pathology training pipeline on GPU.
GitHubComplex pipelines that utilize RAPIDS components.
Integrating and combining RAPIDS libraries forms powerful workflows that optimize data science pipelines. Learn more about real world workflows and featured success stories in our Use Cases Section
Deploy and maintain RAPIDS in production environments with these machine learning operations, hyper parameter optimization, and integration guides on our Cloud ML Examples Repository and Deployments Page
Guides and resources for RAPIDS developers.
RAPIDS projects stay cohesive thanks to detailed maintainer guides and ops procedures. Look up release schedules, get CI help, and stay up to date on our Maintainer Documentation Page
Open source projects stay healthy with active contributions. Overall contribution guides can be found on our Documentation. Each RAPIDS repository also has helpful and detailed contributing guides, such as cuDF's example. Check out each project's README and documentation for their specific requirements in the RAPIDS Repositories
