diff --git a/docs/cugraph-docs/source/index.rst b/docs/cugraph-docs/source/index.rst index 642ec1e..bde4140 100644 --- a/docs/cugraph-docs/source/index.rst +++ b/docs/cugraph-docs/source/index.rst @@ -1,24 +1,4 @@ -*** NOTICE *** -============== - -**cuGraph-DGL has been removed from cuGraph GNN as of release 25.06.** 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. - -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 `_ - -* `pylibwholegraph `_ - the `Wholegraph `_ library for client memory management supporting cuGraph-PyG for even greater scalability -* `cugraph_pyg `_ provides native implementations of Pytorch Geometric's (PyG's) `GraphStore`, `FeatureStore`, and `Loader` interfaces, unlocking powerful GPU-accelerated graph analytics—including neighborhood sampling, centrality metrics, and community detection—directly within PyG workflows. - -`RAPIDS nx-cugraph `_ is now located in the `nx-cugraph repository `_ containing a backend to NetworkX for running supported algorithms with GPU acceleration. - -The `cugraph-docs repository `_ contains code to generate cuGraph documentation. - ---- - RAPIDS Graph documentation ========================== diff --git a/docs/cugraph-docs/source/tutorials/basic_cugraph.md b/docs/cugraph-docs/source/tutorials/basic_cugraph.md index b0d506b..9a69df8 100644 --- a/docs/cugraph-docs/source/tutorials/basic_cugraph.md +++ b/docs/cugraph-docs/source/tutorials/basic_cugraph.md @@ -2,27 +2,14 @@ ## Required hardware/software -cuGraph is part of [RAPIDS](https://docs.rapids.ai/user-guide/) and has the following system requirements: - * NVIDIA GPU, Volta architecture or later, with [compute capability](https://developer.nvidia.com/cuda-gpus) 7.0+ - * CUDA 12.2+ - * Python version 3.11, 3.12, 3.13, or 3.14 - * NetworkX >= version 3.3 or newer in order to use use [NetworkX Configs](https://networkx.org/documentation/stable/reference/backends.html#module-networkx.utils.configs) **This is required for use of nx-cuGraph, [see below](#cugraph-using-networkx-code).** +cuGraph is part of [RAPIDS](https://docs.rapids.ai/user-guide/) and the system requirements can be found [here]( https://docs.rapids.ai/platform-support/ ) + ## Installation -The latest RAPIDS System Requirements documentation is located [here](https://docs.rapids.ai/install#system-req). +The latest RAPIDS System Requirements documentation for UBUNTU is located [here](https://docs.rapids.ai/install/). -This includes several ways to set up cuGraph -* From Unix - * [Conda](https://docs.rapids.ai/install#wsl-conda) - * [Docker](https://docs.rapids.ai/install#wsl-docker) - * [pip](https://docs.rapids.ai/install#wsl-pip) +* In windows you must install [WSL2](https://learn.microsoft.com/en-us/windows/wsl/install) and then follow the directions [here](https://docs.rapids.ai/install/#wsl2) -* In windows you must install [WSL2](https://learn.microsoft.com/en-us/windows/wsl/install) and then choose one of the following: - * [Conda](https://docs.rapids.ai/install#wsl-conda) - * [Docker](https://docs.rapids.ai/install#wsl-docker) - * [pip](https://docs.rapids.ai/install#wsl-pip) - -* Build From Source To build from source, check each RAPIDS GitHub README for set up and build instructions. Further links are provided in the [selector tool](https://docs.rapids.ai/install#selector). If additional help is needed reach out on our [Slack Channel](https://rapids-goai.slack.com/archives/C5E06F4DC). @@ -31,8 +18,7 @@ While the steps above are required to use the full suite of cuGraph graph analyt Nx-cugraph offers those with existing NetworkX code, a **zero code change** option with a growing list of supported algorithms. -## Cugraph API Example -Coming soon ! +## Cugraph API Examples -Until then, [the cuGraph notebook repository](https://github.com/rapidsai/cugraph/blob/main/notebooks/README.md) has many examples of loading graph data and running algorithms in Jupyter notebooks. The [cuGraph test code](https://github.com/rapidsai/cugraph/tree/main/python/cugraph/cugraph/tests) gives examples of python scripts settng up and calling cuGraph algorithms. A simple example of [testing the degree centrality algorithm](https://github.com/rapidsai/cugraph/blob/main/python/cugraph/cugraph/tests/centrality/test_degree_centrality.py) is a good place to start. Some of these examples show [multi-GPU tests/examples with larger data sets](https://github.com/rapidsai/cugraph/blob/main/python/cugraph/cugraph/tests/centrality/test_degree_centrality_mg.py) as well. +[The cuGraph notebook repository](https://github.com/rapidsai/cugraph/blob/main/notebooks/README.md) has many examples of loading graph data and running algorithms in Jupyter notebooks. The [cuGraph test code](https://github.com/rapidsai/cugraph/tree/main/python/cugraph/cugraph/tests) gives examples of python scripts settng up and calling cuGraph algorithms. A simple example of [testing the degree centrality algorithm](https://github.com/rapidsai/cugraph/blob/main/python/cugraph/cugraph/tests/centrality/test_degree_centrality.py) is a good place to start. Some of these examples show [multi-GPU tests/examples with larger data sets](https://github.com/rapidsai/cugraph/blob/main/python/cugraph/cugraph/tests/centrality/test_degree_centrality_mg.py) as well.