Replaced links with RAPIDS standard doc links

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 <https://github.com/rapidsai/cugraph-gnn>`_
-
-* `pylibwholegraph <https://github.com/rapidsai/cugraph-gnn/tree/main/python/>`_ - the `Wholegraph <https://docs.rapids.ai/api/cugraph/nightly/wholegraph/>`_ library for client memory management supporting cuGraph-PyG for even greater scalability
-* `cugraph_pyg <https://github.com/rapidsai/cugraph-gnn/blob/main/readme_pages/cugraph_pyg.md>`_ 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 <https://rapids.ai/nx-cugraph/>`_ is now located in the `nx-cugraph repository <https://github.com/rapidsai/nx-cugraph>`_ containing a backend to NetworkX for running supported algorithms with GPU acceleration.
-
-The `cugraph-docs repository <https://github.com/rapidsai/cugraph-docs>`_ contains code to generate cuGraph documentation.
-
----
-
 RAPIDS Graph documentation
 ==========================
 

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.