RAPIDS cuGraph GNN is a monorepo containing packages for GPU-accelerated graph neural networks (GNNs). cuGraph-GNN supports the creation and manipulation of graphs followed by the execution of scalable fast graph algorithms.

Getting cuGraph * Graph Algorithms * GNN Support

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News

WARNING! cuGraph-DGL has been removed in release 25.08. We recommend migrating to cuGraph-PyG, which offers the same functionality along with additional features like support for heterogeneous sampling and a unified API. The cuGraph team is not planning any further work in the DGL ecosystem going forward.

NEW! nx-cugraph, a NetworkX backend that provides GPU acceleration to NetworkX with zero code change.

> pip install nx-cugraph-cu11 --extra-index-url https://pypi.nvidia.com
> export NETWORKX_AUTOMATIC_BACKENDS=cugraph

That's it. NetworkX now leverages cuGraph for accelerated graph algorithms.

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Table of contents

• Installation
  • Getting cuGraph Packages
  • Building from Source
  • Contributing to cuGraph
• General
  • Latest News
  • Current list of algorithms
  • Blogs and Presentation
  • Performance
• Packages
  • cugraph-pyg
• API Docs
  • Python
    • Python Nightly
    • Python Stable
• References
  • RAPIDS
  • PyG

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RAPIDS cuGraph-GNN is a collection of GPU-accelerated plugins that support PyG, PyTorch, and a variety of other graph and GNN frameworks. cuGraph-GNN is built on top of RAPIDS cuGraph, leveraging its low-level pylibcugraph API and C++ primitives for sampling and other GNN operations (libcugraph)

cuGraph-GNN is comprised of two subprojects: cugraph-PyG and WholeGraph.

• cuGraph-PyG supports PyTorch Geometric (PyG) and implements PyG's GraphStore, FeatureStore, Loader, and Sampler interfaces.
• WholeGraph supports PyTorch and provides a distributed graph and kv store. cuGraph-PyG can leverage WholeGraph for even greater scalability.

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Projects that use cuGraph

(alphabetical order)

• ArangoDB - a free and open-source native multi-model database system - https://www.arangodb.com/
• CuPy - "NumPy/SciPy-compatible Array Library for GPU-accelerated Computing with Python" - https://cupy.dev/
• Memgraph - In-memory Graph database - https://memgraph.com/
• NetworkX (via nx-cugraph backend) - an extremely popular, free and open-source package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks - https://networkx.org/
• PyGraphistry - free and open-source GPU graph ETL, AI, and visualization, including native RAPIDS & cuGraph support - http://github.com/graphistry/pygraphistry
• ScanPy - a scalable toolkit for analyzing single-cell gene expression data - https://scanpy.readthedocs.io/en/stable/

(please post an issue if you have a project to add to this list)

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Open GPU Data Science

The RAPIDS suite of open source software libraries aims to enable execution of end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization but exposing that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.

For more project details, see rapids.ai.

Apache Arrow on GPU

The GPU version of Apache Arrow is a common API that enables efficient interchange of tabular data between processes running on the GPU. End-to-end computation on the GPU avoids unnecessary copying and converting of data off the GPU, reducing compute time and cost for high-performance analytics common in artificial intelligence workloads. As the name implies, cuDF uses the Apache Arrow columnar data format on the GPU. Currently, a subset of the features in Apache Arrow are supported.