RAPIDS Graph documentation

Making graph analytics fast and easy regardless of scale

RAPIDS Graph covers a range of graph libraries and packages, that includes:

Core	GNN	Extension
cugraph	cugraph-ops	cugraph-service
pylibcugraph	cugraph-dgl
libcugraph	cugraph-pyg
libcugraph_etl	wholegraph

Introduction

cuGraph is a library of graph algorithms that seamlessly integrates into the RAPIDS data science ecosystem and allows the data scientist to easily call graph algorithms using data stored in GPU DataFrames, NetworkX Graphs, or even CuPy or SciPy sparse Matrices.

Note: We are redoing all of our documents, please be patient as we update the docs and links

Contents:

• Basics
  • cuGraph Introduction
  • NetworkX Compatibility and Transition
  • Method Cascading and cuGraph
• Installation
  • Getting cuGraph Packages
  • Building from Source
• Tutorials
  • How To Guides
  • cuGraph Blogs and Presentations
  • Commmunity Resources
  • cuGraph Notebooks
• Graph Support
  • Algorithms
  • Compatibility
  • Graph Neural Network Support
  • Data Stores
  • CuGraph Service
• WholeGraph
  • Basics
  • Installation
• References
  • References
  • Data Sets
  • License
• API Reference
  • Core Graph API Documentation
  • Graph Nerual Networks API Documentation
  • Additional Graph Packages API Documentation

Indices and tables

• Index

• Search Page