https://github.com/rapidsai/cugraph-gnn

https://github.com/rapidsai/cugraph-gnn

Last synced: 5 months ago
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• Host: GitHub
• URL: https://github.com/rapidsai/cugraph-gnn
• Owner: rapidsai
• License: apache-2.0
• Created: 2022-08-24T14:49:59.000Z (over 3 years ago)
• Default Branch: branch-25.10
• Last Pushed: 2025-08-13T18:29:50.000Z (5 months ago)
• Last Synced: 2025-08-13T18:37:36.879Z (5 months ago)
• Language: Cuda
• Size: 14.8 MB
• Stars: 48
• Watchers: 7
• Forks: 29
• Open Issues: 28
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE
  • Codeowners: .github/CODEOWNERS

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README

          


  


  







    





    














[RAPIDS](https://rapids.ai) 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](./docs/cugraph/source/installation/getting_cugraph.md) *

[Graph Algorithms](./docs/cugraph/source/graph_support/algorithms.md) *

[GNN Support](./readme_pages/gnn_support.md)



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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.

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## Table of contents

- Installation

  - [Getting cuGraph Packages](./docs/cugraph/source/installation/getting_cugraph.md)

  - [Building from Source](./docs/cugraph/source/installation/source_build.md)

  - [Contributing to cuGraph](./readme_pages/CONTRIBUTING.md)

- General

  - [Latest News](./readme_pages/news.md)

  - [Current list of algorithms](./docs/cugraph/source/graph_support/algorithms.md)

  - [Blogs and Presentation](./docs/cugraph/source/tutorials/cugraph_blogs.rst)

  - [Performance](./readme_pages/performance/performance.md)

- Packages

  - [cugraph-pyg](./readme_pages/cugraph_pyg.md)

- API Docs

  - Python

    - [Python Nightly](https://docs.rapids.ai/api/cugraph/nightly/)

    - [Python Stable](https://docs.rapids.ai/api/cugraph/stable/)

- References

  - [RAPIDS](https://rapids.ai/)

  - [PyG](https://pyg.org)





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[RAPIDS](https://rapids.ai) cuGraph-GNN is a collection of GPU-accelerated plugins that support [PyG](https://pyg.org), [PyTorch](https://pytorch.org), and a variety

of other graph and GNN frameworks.  cuGraph-GNN is built on top of RAPIDS [cuGraph](https://github.com/rapidai/cugraph), leveraging its low-level [pylibcugraph](https://github.com/rapidsai/cugraph/python/pylibcugraph) API

and C++ primitives for sampling and other GNN operations ([libcugraph](https://github.com/rapidai/cugraph/python/libcugraph))

cuGraph-GNN is comprised of two subprojects: [cugraph-PyG](https://github.com/rapidsai/cugraph-gnn/python/cugraph-pyg) and

[WholeGraph](https://github.com/rapidsai/cugraph-gnn/python/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](./python/nx-cugraph/README.md) 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](https://rapids.ai/).





### Apache Arrow on GPU  

The GPU version of [Apache Arrow](https://arrow.apache.org/) 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.