Implementing some of the VDR feedback

docs/cugraph/source/index.rst

@@ -1,58 +1,84 @@
 RAPIDS Graph documentation
 ==========================
+
 .. image:: images/cugraph_logo_2.png
    :width: 600
 
-*Making graph analytics fast and easy regardless of scale*
-
-
-.. list-table:: RAPIDS Graph covers a range of graph libraries and packages, that includes:
-   :widths: 25 25 25
-   :header-rows: 1
-
-   * - Core
-     - GNN
-     - Extension
-   * - :abbr:`cugraph (Python wrapper with lots of convenience functions)`
-     - :abbr:`cugraph-ops (GNN aggregators and operators)`
-     - :abbr:`cugraph-service (Graph-as-a-service provides both Client and Server packages)`
-   * - :abbr:`pylibcugraph (light-weight Python wrapper with no guard rails)`
-     - :abbr:`cugraph-dgl (Accelerated extensions for use with the DGL framework)`
-     -
-   * - :abbr:`libcugraph (C++ API)`
-     - :abbr:`cugraph-pyg (Accelerated extensions for use with the PyG framework)`
-     -
-   * - :abbr:`libcugraph_etl (C++ renumbering function for strings)`
-     - :abbr:`wholegraph (Shared memory-based GPU-accelerated GNN training)`
-     -
-..
-|
 
 ~~~~~~~~~~~~
 Introduction
 ~~~~~~~~~~~~
 cuGraph is a library of graph algorithms that seamlessly integrates into the
 RAPIDS data science ecosystem and allows the data scientist to easily call
 graph algorithms using data stored in GPU DataFrames, NetworkX Graphs, or
-even CuPy or SciPy sparse Matrices.
+even CuPy or SciPy sparse Matrices. Our major integration effort with NetworkX allows
+**zero code change** use of nx-cuGraph as a backend for NetworkX calls. This offers a near seamless
+transition to GPU accelerated graph analytics to NetworkX users with access to a supported GPU.
+
+Getting started with cuGraph
+
+Required hardware/software for cuGraph and `RAPIDS <https://docs.rapids.ai/user-guide>`_
+ * NVIDIA GPU, Volta architecture or later, with `compute capability <https://developer.nvidia.com/cuda-gpus> 7.0+`_
+ * CUDA 11.2-11.8, 12.0-12.5
+ * Python version 3.10, 3.11, or 3.12
+ * NetworkX version 3.0 or newer in order to use use the nx-cuGraph backend. Version 3.3 is required to use `NetworkX Configs <https://networkx.org/documentation/stable/reference/backends.html#module-networkx.utils.configs>`_ `see below <#cugraph-using-networkx-code>`_.
+
+Installation
+The latest RAPIDS System Requirements documentation is located `here <https://docs.rapids.ai/install#system-req>`_.
+
+This includes several ways to set up cuGraph
+
+* From Unix
+
+  * `Conda <https://docs.rapids.ai/install/#conda>`_
+  * `Docker <https://docs.rapids.ai/install/#docker>`_
+  * `pip <https://docs.rapids.ai/install/#pip>`_
+
+
+**Note: Windows use of RAPIDS depends on prior installation of** `WSL2 <https://learn.microsoft.com/en-us/windows/wsl/install>`_.
+
+* From Windows
+
+  * `Conda <https://docs.rapids.ai/install#wsl-conda>`_
+  * `Docker <https://docs.rapids.ai/install#wsl-docker>`_
+  * `pip <https://docs.rapids.ai/install#wsl-pip>`_
 
-Note: We are redoing all of our documents, please be patient as we update
-the docs and links
 
-|
+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>`_.
+
+cuGraph Using NetworkX Code
+While the steps above are required to use the full suite of cuGraph graph analytics, cuGraph is now supported as a NetworkX backend using `nx-cugraph <https://docs.rapids.ai/api/cugraph/nightly/nx_cugraph/nx_cugraph/>_.
+Nx-cugraph offers those with existing NetworkX code, a **zero code change** option with a growing list of supported algorithms.
+
+
+ Cugraph API Example
+
+ .. code-block:: python
+
+  # Import needed libraries
+  import cugraph
+  import cudf
+
+  # Use cuGraph datasets API to load karate data set into a graph
+  from cugraph.datasets import karate
+  G = karate.get_graph()
+
+  # Call cugraph.degree_centrality
+  vertex_bc = cugraph.degree_centrality(G)
+
+There are several resources containing cuGraph examples, `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>_` contain python scripts setting 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 show `multi-GPU tests/examples <https://github.com/rapidsai/cugraph/blob/main/python/cugraph/cugraph/tests/centrality/test_degree_centrality_mg.py>`_ with larger data sets as well.
 
 .. toctree::
    :maxdepth: 2
-   :caption: Contents:
-
-   basics/index
-   nx_cugraph/index
-   installation/index
-   tutorials/index
-   graph_support/index
-   wholegraph/index
-   references/index
-   api_docs/index
+
+   top_toc
 
 Indices and tables
 ==================

docs/cugraph/source/top_toc.rst

@@ -0,0 +1,13 @@
+.. toctree::
+  :maxdepth: 2
+  :caption: cuGraph documentation Contents:
+  :name: top_toc
+
+  basics/index
+  nx_cugraph/index
+  installation/index
+  tutorials/index
+  graph_support/index
+  wholegraph/index
+  references/index
+  api_docs/index