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Open GPU Data Science
A suite of software libraries for executing end-to-end data science completely on GPUs
Learn More About RAPIDS
LEARN MORE
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About RAPIDS

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The RAPIDS suite of open source software libraries and APIs gives you the ability to execute end-to-end data science and analytics pipelines entirely on GPUs. Licensed under Apache 2.0, RAPIDS is incubated by NVIDIA®{: target="_blank"} based on extensive hardware and data science experience. RAPIDS utilizes NVIDIA CUDA®{: target="_blank"} primitives for low-level compute optimization, and exposes GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.

RAPIDS also focuses on common data preparation tasks for analytics and data science. This includes a familiar dataframe API that integrates with a variety of machine learning algorithms for end-to-end pipeline accelerations without paying typical serialization costs. RAPIDS also includes support for multi-node, multi-GPU deployments, enabling vastly accelerated processing and training on much larger dataset sizes.

For RAPIDS logos, themes, branding, and other guides, take a look at our Branding and Guides page.

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Apache Arrow

{: .section-subtitle-top-1} RAPIDS had its start from the Apache Arrow{: target="_blank"} and GoAi{: target="_blank"} projects based on a columnar, in-memory data structure that delivers efficient and fast data interchange with flexibility to support complex data models.

Libraries and APIs Overview

Some RAPIDS projects include cuDF{: target="_blank"}, a pandas-like dataframe manipulation library; cuML{: target="_blank"}, a collection of machine learning libraries that will provide GPU versions of algorithms available in scikit-learn; cuGraph{: target="_blank"}, a NetworkX-like accelerated graph analytics library. Development follows a 6 week release schedule, so new features and libraries are always on the way.

Integration With Deep Learning Libraries

RAPIDS provides native array_interface support. This means data stored in Apache Arrow can be seamlessly pushed to deep learning frameworks that accept array_interface or work with DLPack{: target="_blank"}, such as Chainer{: target="_blank"}, MXNet{: target="_blank"}, and PyTorch{: target="_blank"}.

Visualization

Our focus on Python allows RAPIDS to play well with most data science visualization libraries. For even greater performance, we are working towards deeper integration with these libraries since a native GPU in-memory data format provides high-performance, high-FPS data visualization capabilities, even with very large datasets. {% endcapture %}

{% capture about_bottom_left %} ![RAPIDS ease]({{ site.baseurl }}{% link /assets/images/EaseVsPerformance.svg %}){: .half-image-center} {% endcapture %}

{% capture about_bottom_right %} ![RAPIDS performance]({{ site.baseurl }}{% link /assets/images/rapids-end-to-end-performance-chart-oss-page-r4.svg %}){: .full-image-center} {% endcapture %}

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