RAPIDS Featured User Guides

The RAPIDS data science framework is a collection of libraries for running end-to-end data science pipelines completely on the GPU. The interaction is designed to have a familiar look and feel to working in Python, but utilizes optimized NVIDIA® CUDA® primitives and high-bandwidth GPU memory under the hood. Below are some links to help getting started with each of the individual RAPIDS libraries.

Community Notebooks: A collection of examples and tutorials used to introduce new users to the features and capabilities of RAPIDS.

Machine Learning Services Integration for RAPIDS Cloud: A repository with example notebooks and “getting started” code samples to help you integrate RAPIDS with the hyperparameter optimization services from Azure ML, AWS Sagemaker, Google Cloud, and Databricks.

Tools and Guides for RAPIDS Deployment: Deployment documentation to get you up and running with RAPIDS in AWS, GCP, Azure, IBM and more. Also includes guides for HPC, HPO, Kubernetes, Dask, and more.

ETL and Dataframe Processing with cuDF: Start with the 10 Minutes to cuDF and Dask-cuDF User Guide. Modeled after 10 Minutes to Pandas, this is a short introduction to cuDF that is geared mainly for new users. The cuDF User Guide is generally very extensive and helpful.

Accelerated Machine Learning with cuML: Start with the User Guide and the Estimator Intro, showcasing basic machine learning for training and evaluating machine learning models in cuML. The Intro and key concepts for cuML is helpful as well.

Graph Analytics with cuGraph: Start with the Easy Path to use NetworkX graph objects with accelerated algorithms. Or, use nx-cugraph to use the NetworkX API with a zero code change GPU accelerated backend. There is also a general cuGraph Introduction.

Spatial Analytics with cuSpatial: Start with the cuSpatial User Guide for an intro to GPU Accelerated Spatial Analytics.

Accelerated Cross Filtered Visualization with cuxfilter: Start with 10 Minutes to Cuxfilter to get an overview of how to quickly create a dashboard. There are also broader examples in the RAPIDS Visualization Guides.

Computer Vision and Analytics with cuCIM: Start with the Welcome Notebook for links to resources guides and a good overview of the project structure.

Algorithms and Primitives for Scientific Computing, Data Science and Machine Learning with RAFT: Start with the Quick Start guide for simple Python and C++ examples.

Accelerated Apache Spark with Spark RAPIDS: Start with the Examples Repository for Spark related utilities and examples using the RAPIDS Accelerator, including ETL, ML/DL, and more. A good overview is available on their docs introduction.