From ae4813fde2c0a49732a3b1169b9004e0046b55d1 Mon Sep 17 00:00:00 2001 From: acostadon Date: Fri, 4 Oct 2024 14:22:24 -0400 Subject: [PATCH] added original toc and fixed up landing page --- docs/cugraph/source/index.rst | 55 ++++++++++++++++++++--------------- 1 file changed, 32 insertions(+), 23 deletions(-) diff --git a/docs/cugraph/source/index.rst b/docs/cugraph/source/index.rst index 062156e034..5bb819fcd4 100644 --- a/docs/cugraph/source/index.rst +++ b/docs/cugraph/source/index.rst @@ -10,20 +10,18 @@ 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 GPU DataFrames, NetworkX Graphs, or +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 `_ - * NVIDIA GPU, Volta architecture or later, with `compute capability 7.0+ `_ + * NVIDIA GPU, Volta architecture or later, with `compute capability `_ 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 `_). + * NetworkX version 3.0 or newer in order to use use the nx-cuGraph backend. Version 3.3 is required to use `NetworkX Configs `_ `see below <#cugraph-using-networkx-code>`_. Installation The latest RAPIDS System Requirements documentation is located `here `_. @@ -39,27 +37,30 @@ This includes several ways to set up cuGraph **Note: Windows use of RAPIDS depends on prior installation of** `WSL2 `_. -* From Windows +* From windows - * `Conda `_ - * `Docker `_ - * `pip `_ + * `Conda `_ + * `Docker `_ + * `pip `_ -cuGraph Using NetworkX Code +Build From Source -cuGraph is now available as a NetworkX backend using `nx-cugraph `_. -nx-cugraph offers NetworkX users a **zero code change** option to accelerate -their existing NetworkX code using an NVIDIA GPU and cuGraph. +To build from source, check each RAPIDS GitHub README for set up and build instructions. Further links are provided in the `selector tool `_. +If additional help is needed reach out on our `Slack Channel `_. +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 `_. +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 - + # Create an instance of the popular Zachary Karate Club graph from cugraph.datasets import karate G = karate.get_graph() @@ -67,17 +68,25 @@ 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 `_ + +There are several resources containing cuGraph examples, `the cuGraph notebook repository `_ has many examples of loading graph data and running algorithms in Jupyter notebooks. -The `cuGraph test code _` contain python scripts setting up and calling cuGraph algorithms. -A simple example of `testing the degree centrality algorithm `_ -is a good place to start. Some of these show `multi-GPU tests/examples `_ with larger data sets as well. +The `cuGraph test code `_ contains python scripts setting up and calling cuGraph algorithms. +A simple example of `testing the degree centrality algorithm `_ +is a good place to start. Some of these show `multi-GPU tests/examples `_ with larger data sets as well .. 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 ==================