RAPIDS is a collection of open source software libraries and APIs that gives you the ability to execute end-to-end data science and analytics pipelines entirely on NVIDIA GPUs using familiar PyData APIs. Jump To Section
Learn more about RAPIDS success stories, use cases, applications, research and other topics related to GPU accelerated data science. Jump To Section
Get in contact with RAPIDS, find enhanced support with NVIDIA enterprise services, and reach out to the broader developer community. Jump To Section
Utilizing NVIDIA CUDA primitives for low-level compute optimization, RAPIDS exposes GPU parallelism and high-bandwidth memory speed through user-friendly interfaces:
RAPIDS delivers impressive acceleration from on a single GPU desktop system all the way to MNMG (multi-node multi-GPU) configurations on an expansive range of NVIDIA hardware.
RAPIDS had its start from the Apache Arrow and GoAi projects based on a columnar, in-memory data structure that delivers efficient and fast data interchange with flexibility to support complex data models.
With NVIDIA backing, it has maintained a focus on open source development (typically Apache 2.0 licensed) and collaboration with a broad community of cutting edge data science projects.
When you are a global-scale company, precision is everything. Learn how Walmart implemented XGBoost to increase forecast accuracy and potentially save billions of dollars. Read More
Buzzwords is Bumble's open-source GPU-powered topic modelling tool, developed in house and building upon the work found in BERTopic and Top2Vec. Buzzwords uses cuML's UMAP and HDBSCAN algorithms. Read More
AT&T was able to do more, faster by moving data science workloads to GPU. Hear about their analysis and use of NVIDIA capabilities across different domains and share specific use case examples, their efficiency gains, and corresponding impacts. Hear More
See how RAPIDS is enabling work on the very cutting edge by enabling Graph Neural Networks (GNN) for applications including drug discovery, recommender systems, fraud detection, and cybersecurity. Hear More
Learn about Kaggle Grandmasters of NVIDIA (KGMoN), and see how they use RAPIDS to build winning recommender systems, predict degradation rates in RNA molecules, identify melanoma in medical imaging, and more. Read More on the KGMON Page
Using RAPIDS in your data science research and development? Let us know for opportunities to promote it. See the RAPIDS Citation Guide
Use RAPIDS directly or through NVIDIA AI Enterprise, which provides extensive optimization, certified hardware profiles, and direct IT support. Read more about NVIDIA AI Enterprise
Reach out and engage with the RAPIDS community on the following channels:Slack Channel
Promote and foster more use of RAPIDS with these Deep Learning Institute (DLI) and Launchpad courses:Accelerating End-to-End Data Science Workflows