From eaf6de1bcb1ed2e88dbab8a945adfd617736240a Mon Sep 17 00:00:00 2001 From: acostadon Date: Mon, 13 Jan 2025 11:06:05 -0500 Subject: [PATCH] reorganized page per review comments --- README.md | 29 ++++++++++++++--------------- 1 file changed, 14 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index dac6ad6955..d3be41d9e0 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,17 @@ +# ** NOTICE ** the cuGraph repository has been refactored to make it more efficient to build, maintain and use. + +Libraries supporting GNNs are now located in the [cugraph-gnn repository](https://github.com/rapidsai/cugraph-gnn) + +* [pylibwholegraph](https://github.com/rapidsai/cugraph-gnn/tree/branch-25.02/python/) - the [Wholegraph](https://docs.rapids.ai/api/cugraph/nightly/wholegraph/) library for client memory management supporting both cuGraph-DGL and cuGraph-PyG for even greater scalability +* [cugraph_dgl](https://github.com/rapidsai/cugraph-gnn/blob/main/readme_pages/cugraph_dgl.md) enables the ability to use cugraph Property Graphs with Deep Graph Library (DGL) +* [cugraph_pyg](https://github.com/rapidsai/cugraph-gnn/blob/main/readme_pages/cugraph_pyg.md) enables the ability to use cugraph Property Graphs with PyTorch Geometric (PyG). + +[RAPIDS nx-cugraph](https://rapids.ai/nx-cugraph/) is now located in the nx-cugraph repository. +* [nx-cugraph library](https://github.com/rapidsai/nx-cugraph) is a backend to NetworkX to run supported algorithms with GPU acceleration. + +The cugraph-docs repository contains code to generate cuGraph documentation. It is now located in the [cugraph-docs repository](https://docsgithub.com/rapidsai/cugraph-docs) + +


cuGraph @@ -24,21 +38,6 @@ [RAPIDS](https://rapids.ai) cuGraph is a repo that represents a collection of packages focused on GPU-accelerated graph analytics including support for property graphs and remote (graph as a service) operations. cuGraph supports the creation and manipulation of graphs followed by the execution of scalable fast graph algorithms. -# NOTICE -- the cuGraph repository has been refactored to make it more efficient to build, maintain and use. - -cuGraph code supporting GNNs is now located in the [cugraph-gnn repository](https://github.com/rapidsai/cugraph-gnn) - -This repository, cugraph-gnn, contains: - -* [pylibwholegraph](https://github.com/rapidsai/cugraph-gnn/tree/branch-25.02/python/) - the [Wholegraph](https://docs.rapids.ai/api/cugraph/nightly/wholegraph/) library for client memory management supporting both cuGraph-DGL and cuGraph-PyG for even greater scalability -* [cugraph_dgl](https://github.com/rapidsai/cugraph-gnn/blob/main/readme_pages/cugraph_dgl.md) enables the ability to use cugraph Property Graphs with Deep Graph Library (DGL) -* [cugraph_pyg](https://github.com/rapidsai/cugraph-gnn/blob/main/readme_pages/cugraph_pyg.md) enables the ability to use cugraph Property Graphs with PyTorch Geometric (PyG). - -Code for cuGraph as a NetworkX backend is now located in the [RAPIDS nx-cugraph repository](https://github.com/rapidsai/nx-cugraph) -* [nx-cugraph library](https://github.com/rapidsai/nx-cugraph) is a backend to NetworkX to run supported algorithms with GPU acceleration. - -The cugraph-docs repository contains code to generate cuGraph documentation. It is now located in the [cugraph-docs repository](https://docsgithub.com/rapidsai/cugraph-docs) -
[Getting cuGraph](https://docs.rapids.ai/api/cugraph/nightly/) *