Get Started with RAPIDS
Learn how to use our libraries, accelerate common data science use cases, and access hands-on labs to develop business-critical applications.
Explore Resources to Get Started With RAPIDS Libraries
cuDF is a GPU DataFrame library that accelerates pandas workflows on GPUs with zero code changes. cuDF is available by default on Colab.
Polars provides a GPU engine powered by cuDF that delivers up to 13X speedup over CPU, allowing users to process hundreds of millions of records in seconds.
- Demo Video
- Tutorial Notebook on GitHub and Colab
- Polars Documentation
- Release Blog
The RAPIDS Accelerator for Apache Spark provides a set of plug-ins for Apache Spark that leverage GPUs to accelerate processing via the RAPIDS libraries.
With the NetworkX GPU backend, you can accelerate popular graph algorithms with zero code changes with cuGraph, a GPU-accelerated graph analytics library.
cuML is a unified CPU/GPU library for executing machine learning (ML) algorithms with an API that closely follows the scikit-learn API.
- Blog: Zero Code Change Acceleration for Machine Learning
- Getting Started With cuML
- Machine Learning With GPU-Accelerated Pandas and Scikit-Learn
- Webinar:Deep Dive Into GPU-Accelerated Technologies
- Online Course: Fundamentals of Accelerated Data Science
- Notebook: Zero Code Change Acceleration for scikit-learn
Use Case Tutorials
Learn how to accelerate data science in a few short hours in simple technical walk-throughs that pair step-by-step tutorials with notebooks, sample datasets, and benchmarks in example exercises that demonstrate how RAPIDS helps data scientists save time and better leverage their data in ubiquitous data science and machine learning use cases.
Accelerated Data Analysis
Learn how to use RAPIDS cuDF to accelerate data analytics workflows.
This starter kit covers how to use RAPIDS for:
- Exploratory Data Analysis
- Time Series Analysis
- Visualization
- DataFrame to ML
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Data Processing at Scale
Explore how to accelerate Apache Spark 3.0 workloads with the RAPIDS Accelerator for Apache Spark.
This starter kit walks through an example use case in retail:
- Data Processing, including ETL and manipulation functions
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Time Series Forecasting
Build time series forecasting models with RAPIDS cuDF and cuML, which mimic pandas and sci-kit learn.
This starter kit includes two examples of time series workflows:
- Time Series Analysis
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Learn How Top-Performing Kaggle Grandmasters Win Data Science Competitions
The Kaggle Grandmasters of NVIDIA place in the top 10% of Kaggle Grandmasters worldwide. Learn their tricks of the trade with our Kaggle Grandmaster series that explain how our teams use accelerated data science to create winning recommender systems and predictive models.

Mastering Multilingual Recommender Systems
The KGMoN leveraged transfer learning, model embedding, and other innovative techniques to construct a two-stage pipeline solution for e-commerce product suggestions. This solution won the 2023 KDD Cup.
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Mastering Recommender Systems
KGMoN tackled an e-commerce use case by training high-functioning recommendation systems with a dataset that included millions of anonymized user interactions. These strategies placed top 3 in Kaggle’s Otto competition.
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Predicting Credit Defaults on a Massive Dataset
Jiwei Liu, KGMON, walks through GPU-acceleration in a default prediction use case (top 10 in Kaggle’s American Express competition) that resulted in significantly faster inference times.
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RAPIDS libraries are open source, written in Python, and built on Apache Arrow. The software is being developed in partnership with enterprises globally.
Download RAPIDS to dramatically accelerate machine learning and data science.
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