RAPIDS™ is an open-source suite of GPU-accelerated data science and AI libraries with APIs that match the most popular open-source data tools. It accelerates performance by orders of magnitude at scale across data pipelines.
Faster pipelines enable more experimentation, improving outcomes.
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Easy to Adopt
Familiar Python APIs and plug-ins quickly accelerate existing workloads.
Explore Modular Libraries
Flexible Open-Source Platform
With 100+ software integrations, RAPIDS promotes collaboration.
Explore the Ecosystem
RAPIDS runs on all major clouds, on your local machine, or on premises.
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Accelerating Data Science
With libraries that speed up widely adopted operations and algorithms, RAPIDS helps reduce time to insight as questions evolve.
Faster Pandas with cuDF
* Benchmark on Groupy advanced operation (5GB) DuckDB Data Benchmark HW: Intel Xeon Platinum 8480CL CPU and NVIDIA Grace Hopper GPU SW: pandas v1.5 and cudf.pandas v23.10
Faster Scikit-Learn with cuML
* Benchmark on UMAP-Unsupervised on 100,000 samples and 256 features HW: Intel Xeon Platinum 8480CL CPU and NVIDIA H100 80GB (1x GPU) SW: scikit-learn v1.3 and cuML v23.10
Faster NetworkX with cuGraph
* Benchmark on PageRank with synthetic dataset having ~16,384 vertices and ~524,288 edges HW: Intel Xeon Platinum 8480CL CPU and NVIDIA H100 80GB (1x GPU) SW: NetworkX v3.2 and cuGraph v23.10
See benchmarks at rapids.ai
Flexible Across Data Workloads
With a distinctive, modular, interoperable selection of libraries that smoothly plug and play into pipelines and applications, RAPIDS simplifies the development process.
RAPIDS keeps data science pipelines running smoothly at any scale.
Seamlessly accelerate data analytics for tabular datasets, graph databases, or the Spark framework with your existing tools.
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Accelerated Data Analytics
Boost model training speed with an API that closely follows scikit-learn.
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Support efficient graph neural networks training with DGL and PyG.
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Deploy high-performance machine learning inference with cuML and NVIDIA Triton™.
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Inference and Deployment
Inspired by the most popular open-source data tools, RAPIDS libraries adapt to your workflow.
Data Preprocessing: cuDF
Get started in accelerated data science with GPU-accelerated DataFrames using the pandas API.
Big Data Processing: RAPIDS Accelerator for Apache Spark
Accelerate your existing Apache Spark applications with minimal code changes.
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GPU-Accelerated Spark Go to GitHub
Machine Learning: cuML
Execute machine learning algorithms on CPUs and GPUs with an API that closely follows the scikit-learn API.
Graph Analytics: cuGraph
Quickly navigate graph analytics libraries with a python API that follows NetworkX.
Vector Search: RAFT
Apply RAFT’s primitives to accelerate popular algorithms in machine learning, including vector search.
Scale RAPIDS: Dask-RAPIDS
Expand data science pipelines to multiple nodes with RAPIDS on Dask.
Go to GitHub
Create interactive data visuals with multidimensional filtering of over 100-million-row tabular datasets.
Accelerate input/output (IO), computer vision, and image processing of n-dimensional, especially biomedical, images.
Leverage purpose-built NVIDIA frameworks and guides to build accelerated applications for common and high-impact use cases.
Revolutionize data management and preprocessing with the RAPIDS Accelerator for Spark.
Learn More About Scaled Data Processing
Accelerate time-series modeling from feature engineering to forecasting.
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Build high-performing recommender systems at scale with NVIDIA Merlin™.
Learn More About Recommenders
Filter, process, and classify real-time data in optimized AI pipelines to quickly detect cyberthreats.
Learn More About AI Cybersecurity
cuOpt’s world-record-holding accelerated solver optimizes routes for last-mile delivery, technician dispatch, or intra-factory logistics.
Learn More About Route Optimization
RAPIDS excels at accelerating business-critical applications, reducing years of planning and development across industries.
With more than 100 open-source and commercial software integrations, RAPIDS provides a foundation for a collaborative high-performance data science ecosystem.
We're committed to simplifying, unifying, and accelerating data science for the open-source community.
RAPIDS partners with the most popular data science and machine learning platforms to democratize access to accelerated data science.
Enterprise Data Science
Accelerated data science with NVIDIA AI Enterprise, an end-to-end, secure, cloud-native AI software platform optimized to take enterprises to the leading edge of AI. NVIDIA AI Enterprise delivers validation and integration for NVIDIA AI open-source software, including RAPIDS, access to AI solution workflows to speed time to production, certifications to deploy AI everywhere, and enterprise-grade support, security, manageability, and API stability to mitigate the potential risks of open-source software.
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Our customers use RAPIDS’ fully functional stack to scale their enterprise use cases.
CapitalOne accelerated their financial and credit analysis pipelines, improving model training by 100X.Watch On-Demand Session
Walmart solved scalability issues with their product-substitution algorithm.Watch On-Demand Session
AT&T applied the RAPIDS Accelerator for Apache Spark on GPU clusters in their data-to-AI pipeline.Read Blog
TCS Optumera accelerated their demand forecasting pipeline with the RAPIDS Accelerator for Apache Spark.Watch On-Demand Session
Check out more RAPIDS resources, including developer kits, NVIDIA LaunchPad labs, and guidance on deployment options.