Updates docs to describe nx-cugraph based on latest updates for 24.10 (…

…#4694) ## Proposed Changes This PR contains critical fixes to the cugraph docs pages. --------- Co-authored-by: acostadon <[email protected]> Co-authored-by: rlratzel <[email protected]> Co-authored-by: James Lamb <[email protected]>
rapidsai · Oct 7, 2024 · 7733017 · 7733017
1 parent 3789b70
commit 7733017
Show file tree

Hide file tree

Showing 13 changed files with 149 additions and 382 deletions.
diff --git a/docs/cugraph/source/basics/cugraph_cascading.md b/docs/cugraph/source/basics/cugraph_cascading.md
diff --git a/docs/cugraph/source/basics/cugraph_intro.md → docs/cugraph/source/basics/index.md b/docs/cugraph/source/basics/cugraph_intro.md → docs/cugraph/source/basics/index.md
@@ -1,5 +1,5 @@
-
 # cuGraph Introduction
+
 The Data Scientist has a collection of techniques within their
 proverbial toolbox. Data engineering, statistical analysis, and
 machine learning are among the most commonly known. However, there
@@ -20,8 +20,8 @@ into the RAPIDS data science ecosystem and allows the data scientist to easily
 call graph algorithms using data stored in a GPU DataFrame, NetworkX Graphs, or even
 CuPy or SciPy sparse Matrix.
 
-
 ## Vision
+
 The vision of RAPIDS cuGraph is to ___make graph analysis ubiquitous to the
 point that users just think in terms of analysis and not technologies or
 frameworks___. This is a goal that many of us on the cuGraph team have been
@@ -48,7 +48,6 @@ high-speed ETL, statistics, and machine learning. To make things even better,
 RAPIDS and DASK allows cuGraph to scale to multiple GPUs to support
 multi-billion edge graphs.
 
-
 ## Terminology
 
 cuGraph is a collection of GPU accelerated graph algorithms and graph utility

diff --git a/docs/cugraph/source/basics/index.rst b/docs/cugraph/source/basics/index.rst
diff --git a/docs/cugraph/source/basics/nx_transition.rst b/docs/cugraph/source/basics/nx_transition.rst
diff --git a/docs/cugraph/source/index.rst b/docs/cugraph/source/index.rst
@@ -10,23 +10,33 @@ 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. 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.
+graph algorithms using data stored in cuDF/Pandas DataFrames or CuPy/SciPy
+sparse matrices.
 
+---------------------------
+cuGraph Using NetworkX Code
+---------------------------
+
+cuGraph is now available as a NetworkX backend using `nx-cugraph <https://rapids.ai/nx-cugraph/>`_.
+Our major integration effort with NetworkX offers NetworkX users a **zero code change** option to accelerate
+their existing NetworkX code using an NVIDIA GPU and cuGraph.
+
+Check out `zero code change accelerated NetworkX <nx_cugraph/index.rst>`_. If you would like to continue using standard cuGraph, then continue down below.
+
+----------------------------
 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+`_
+ * NVIDIA GPU, Volta architecture or later, with `compute capability 7.0+ <https://developer.nvidia.com/cuda-gpus>`_
  * 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>`_.
+++++++++++++
+
+Please see the latest `RAPIDS System Requirements documentation <https://docs.rapids.ai/install#system-req>`_.
 
 This includes several ways to set up cuGraph
 
@@ -41,17 +51,9 @@ This includes several ways to set up cuGraph
 
 * 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>`_
-
-
-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.
-
+  * `Conda <https://docs.rapids.ai/install#wsl2-conda>`_
+  * `Docker <https://docs.rapids.ai/install#wsl2-docker>`_
+  * `pip <https://docs.rapids.ai/install#wsl2-pip>`_
 
  Cugraph API Example
 
@@ -67,19 +69,32 @@ their existing NetworkX code using an NVIDIA GPU and cuGraph.
   # 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.
+ There are several resources containing cuGraph examples, the cuGraph `notebook repository <https://github.com/rapidsai/cugraph/blob/HEAD/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>`_ contains script examples of setting up and calling cuGraph algorithms.
+
+ A simple example of `testing the degree centrality algorithm <https://github.com/rapidsai/cugraph/blob/HEAD/python/cugraph/cugraph/tests/centrality/test_degree_centrality.py>`_ is a good place to start. There are also `multi-GPU examples <https://github.com/rapidsai/cugraph/blob/HEAD/python/cugraph/cugraph/tests/centrality/test_degree_centrality_mg.py>`_ with larger data sets as well.
+
+----
+
+~~~~~~~~~~~~~~~~~
+Table of Contents
+~~~~~~~~~~~~~~~~~
 
 .. toctree::
    :maxdepth: 2
 
-   top_toc
+   basics/index
+   nx_cugraph/index
+   installation/index
+   tutorials/index
+   graph_support/index
+   wholegraph/index
+   references/index
+   api_docs/index
 
+~~~~~~~~~~~~~~~~~~
 Indices and tables
-==================
+~~~~~~~~~~~~~~~~~~
 
 * :ref:`genindex`
 * :ref:`search`
diff --git a/docs/cugraph/source/nx_cugraph/benchmarks.md b/docs/cugraph/source/nx_cugraph/benchmarks.md
@@ -15,14 +15,12 @@ class="title-ref"></span></figcaption>
 
 ## Reproducing Benchmarks
 
-Below are the steps to reproduce the results on your workstation. These are documented in this [README](https://github.com/rapidsai/cugraph/blob/HEAD/benchmarks/nx-cugraph/pytest-based).
+Below are the steps to reproduce the results on your own.
 
 1. Clone the latest <https://github.com/rapidsai/cugraph>
 
-2. Follow the instructions to build an environment
+2. Follow the instructions to build and activate an environment
 
-3. Activate the environment
+4. Install the latest `nx-cugraph` by following the [Installation Guide](installation.md)
 
-4. Install the latest `nx-cugraph` by following the [guide](installation.md)
-
-5. Follow the instructions written in the README here: `cugraph/benchmarks/nx-cugraph/pytest-based/`
+5. Follow the instructions written in the README [here](https://github.com/rapidsai/cugraph/blob/HEAD/benchmarks/nx-cugraph/pytest-based)