Implementing some of the VDR feedback (#4674)

Adding some content and navigation options per VDR,
resolves  rapidsai/graph_dl#594
resolves rapidsai/graph_dl#593

---------

Co-authored-by: rlratzel <[email protected]>

Author: acostadon

docs/cugraph/source/index.rst

@@ -1,58 +1,82 @@
 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.
+graph algorithms using data stored in GPU DataFrames, NetworkX Graphs, or even
+CuPy or SciPy sparse Matrices. Our major integration effort with NetworkX
+allows for **zero code change** GPU acceleration through the use of the
+nx-cugraph backend. NetworkX and the nx-cugraph backend offer a seamless
+transition to GPU accelerated graph analytics for 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. NetworkX version 3.4 or newer is recommended. (`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
 
-Note: We are redoing all of our documents, please be patient as we update
-the docs and links
+  * `Conda <https://docs.rapids.ai/install#wsl-conda>`_
+  * `Docker <https://docs.rapids.ai/install#wsl-docker>`_
+  * `pip <https://docs.rapids.ai/install#wsl-pip>`_
 
-|
+
+cuGraph Using NetworkX Code
+
+cuGraph is now available as a NetworkX backend using `nx-cugraph <https://rapids.ai/nx-cugraph/>`_.
+nx-cugraph offers NetworkX users a **zero code change** option to accelerate
+their existing NetworkX code using an NVIDIA GPU and cuGraph.
+
+
+ Cugraph API Example
+
+ .. code-block:: python
+
+  import cugraph
+  import cudf
+
+  # Create an instance of the popular Zachary Karate Club 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