From a14cfab6dc63396164894556328787dcc47d2e00 Mon Sep 17 00:00:00 2001 From: acostadon Date: Wed, 25 Sep 2024 14:42:23 -0400 Subject: [PATCH 01/10] Implementing some of the VDR feedback --- docs/cugraph/source/index.rst | 39 ++++++++++++++++++++++++++++++++++- 1 file changed, 38 insertions(+), 1 deletion(-) diff --git a/docs/cugraph/source/index.rst b/docs/cugraph/source/index.rst index 9ea9e4d65cf..a0352588ff5 100644 --- a/docs/cugraph/source/index.rst +++ b/docs/cugraph/source/index.rst @@ -39,7 +39,44 @@ even CuPy or SciPy sparse Matrices. Note: We are redoing all of our documents, please be patient as we update the docs and links -| +|# Getting started with cuGraph + +## 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 11.2, 11.4, 11.5, 11.8, 12.0 or 12.2 + * Python version 3.9, 3.10, or 3.11 + * 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).** + +## 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#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 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). + +## 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 +Coming soon ! + + +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. .. toctree:: :maxdepth: 2 From 9e6b4e5887318a05054cb7c0121f59118f2131af Mon Sep 17 00:00:00 2001 From: acostadon Date: Fri, 27 Sep 2024 10:43:57 -0400 Subject: [PATCH 02/10] adding some clean up to new cugraph top page --- docs/cugraph/source/index.rst | 38 ++++++++++++++++------------------- 1 file changed, 17 insertions(+), 21 deletions(-) diff --git a/docs/cugraph/source/index.rst b/docs/cugraph/source/index.rst index a0352588ff5..71a0298ae70 100644 --- a/docs/cugraph/source/index.rst +++ b/docs/cugraph/source/index.rst @@ -1,4 +1,4 @@ -RAPIDS Graph documentation +# RAPIDS Graph documentation ========================== .. image:: images/cugraph_logo_2.png :width: 600 @@ -6,11 +6,8 @@ RAPIDS Graph documentation *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 +. RAPIDS Graph covers a range of graph libraries and packages, that includes: + * - Core - GNN - Extension * - :abbr:`cugraph (Python wrapper with lots of convenience functions)` @@ -28,9 +25,8 @@ RAPIDS Graph documentation .. | -~~~~~~~~~~~~ -Introduction -~~~~~~~~~~~~ +# 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 @@ -39,40 +35,40 @@ even CuPy or SciPy sparse Matrices. Note: We are redoing all of our documents, please be patient as we update the docs and links -|# Getting started with cuGraph +# Getting started with cuGraph ## 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 11.2, 11.4, 11.5, 11.8, 12.0 or 12.2 - * Python version 3.9, 3.10, or 3.11 - * 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 has the following system requirements: + * NVIDIA GPU, Volta architecture or later, with [compute capability](https://developer.nvidia.com/cuda-gpus) 7.0+ + * CUDA 11.2, 11.4, 11.5, 11.8, 12.0 or 12.2 + * Python version 3.9, 3.10, or 3.11 + * 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).** ## 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 + * 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 choose one of the following: + * 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 + * 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 +## 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. +nx-cugraph offers those with existing NetworkX code, a **zero code change** option with a growing list of supported algorithms. -## Cugraph API Example +## cugraph API Example Coming soon ! From d4ecf7f207955c6048943d8632c1dac2eafc4cf0 Mon Sep 17 00:00:00 2001 From: acostadon Date: Sun, 29 Sep 2024 10:47:32 -0400 Subject: [PATCH 03/10] latest cugraph landing page --- docs/cugraph/source/index.rst | 119 ++++++++++++++++------------------ 1 file changed, 55 insertions(+), 64 deletions(-) diff --git a/docs/cugraph/source/index.rst b/docs/cugraph/source/index.rst index 71a0298ae70..167971e33e7 100644 --- a/docs/cugraph/source/index.rst +++ b/docs/cugraph/source/index.rst @@ -1,91 +1,82 @@ -# RAPIDS Graph documentation +RAPIDS Graph documentation ========================== + .. image:: images/cugraph_logo_2.png :width: 600 -*Making graph analytics fast and easy regardless of scale* - - -. RAPIDS Graph covers a range of graph libraries and packages, that includes: - * - 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 -============== + +~~~~~~~~~~~~ +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. A major integration effort with NetworkX is focused on +**zero code change** use of nx-cuGraph as a backend for NetworkX calls. This allows a near seamless +transition to GPU accelerated graph analytics. -Note: We are redoing all of our documents, please be patient as we update -the docs and links +Getting started with cuGraph -# Getting started with cuGraph +Required hardware/software for cuGraph and `RAPIDS `_ + * NVIDIA GPU, Volta architecture or later, with `compute capability 7.0+`_ + * CUDA 11.4-11.8, 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 `_ `see below <#cugraph-using-networkx-code>`_. -## Required hardware/software +Installation +The latest RAPIDS System Requirements documentation is located `here `_. -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 11.2, 11.4, 11.5, 11.8, 12.0 or 12.2 - * Python version 3.9, 3.10, or 3.11 - * 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).** +This includes several ways to set up cuGraph + +* From Unix + + * `Conda `_ + * `Docker `_ + * `pip `_ + + +**Note: Windows use of RAPIDS depends on prior installation of** `WSL2 `_. + +* From windows + + * `Conda `_ + * `Docker `_ + * `pip `_ -## 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#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 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 +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). +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](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 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 + Cugraph API Example Coming soon ! -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. +Until then, `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. .. toctree:: - :maxdepth: 2 - :caption: Contents: - - basics/index - nx_cugraph/index - installation/index - tutorials/index - graph_support/index - wholegraph/index - references/index - api_docs/index + :maxdepth: 1 + :caption: cuGraph documentation Contents: + + basics/index + nx_cugraph/index + installation/index + tutorials/index + graph_support/index + wholegraph/index + references/index + api_docs/index Indices and tables ================== From 3f6632beb57857ee979e1b32512ecf10e320c791 Mon Sep 17 00:00:00 2001 From: acostadon Date: Sun, 29 Sep 2024 15:50:46 -0400 Subject: [PATCH 04/10] fixed section headers and added content to cugraph landing page --- docs/cugraph/source/index.rst | 29 ++++++++++------------------- docs/cugraph/source/top_toc.rst | 13 +++++++++++++ 2 files changed, 23 insertions(+), 19 deletions(-) create mode 100644 docs/cugraph/source/top_toc.rst diff --git a/docs/cugraph/source/index.rst b/docs/cugraph/source/index.rst index 167971e33e7..f5c5c4a828d 100644 --- a/docs/cugraph/source/index.rst +++ b/docs/cugraph/source/index.rst @@ -11,15 +11,15 @@ 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. A major integration effort with NetworkX is focused on -**zero code change** use of nx-cuGraph as a backend for NetworkX calls. This allows a near seamless -transition to GPU accelerated graph analytics. +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+`_ - * CUDA 11.4-11.8, 12.0-12.5 + * 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 `_ `see below <#cugraph-using-networkx-code>`_. @@ -44,11 +44,10 @@ This includes several ways to set up cuGraph * `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 `_. If additional help is needed reach out on our `Slack Channel `_. +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 _. @@ -56,7 +55,7 @@ Nx-cugraph offers those with existing NetworkX code, a **zero code change** opti Cugraph API Example -Coming soon ! + Until then, `the cuGraph notebook repository `_ @@ -66,18 +65,10 @@ A simple example of `testing the degree centrality algorithm `_ with larger data sets as well. .. toctree:: - :maxdepth: 1 - :caption: cuGraph documentation Contents: - - basics/index - nx_cugraph/index - installation/index - tutorials/index - graph_support/index - wholegraph/index - references/index - api_docs/index + :maxdepth: 2 + top_toc + Indices and tables ================== diff --git a/docs/cugraph/source/top_toc.rst b/docs/cugraph/source/top_toc.rst new file mode 100644 index 00000000000..aa06962a1b2 --- /dev/null +++ b/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 \ No newline at end of file From 605b6848a44ee5a0b9bf876d30c589c86cfc1b3d Mon Sep 17 00:00:00 2001 From: acostadon Date: Mon, 30 Sep 2024 08:23:53 -0400 Subject: [PATCH 05/10] added API example and fixed style issue --- docs/cugraph/source/index.rst | 13 ++++++++++++- docs/cugraph/source/top_toc.rst | 2 +- 2 files changed, 13 insertions(+), 2 deletions(-) diff --git a/docs/cugraph/source/index.rst b/docs/cugraph/source/index.rst index f5c5c4a828d..b66ac6dee30 100644 --- a/docs/cugraph/source/index.rst +++ b/docs/cugraph/source/index.rst @@ -56,9 +56,20 @@ Nx-cugraph offers those with existing NetworkX code, a **zero code change** opti Cugraph API Example + .. code-block:: python + # Import needed libraries + import cugraph + import cudf -Until then, `the cuGraph notebook repository `_ + # 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 `_ 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 `_ diff --git a/docs/cugraph/source/top_toc.rst b/docs/cugraph/source/top_toc.rst index aa06962a1b2..8e31e70ca78 100644 --- a/docs/cugraph/source/top_toc.rst +++ b/docs/cugraph/source/top_toc.rst @@ -10,4 +10,4 @@ graph_support/index wholegraph/index references/index - api_docs/index \ No newline at end of file + api_docs/index From dee078ec8226ced21c0808082b4ed1a6cb45cc58 Mon Sep 17 00:00:00 2001 From: acostadon Date: Mon, 30 Sep 2024 09:56:53 -0400 Subject: [PATCH 06/10] Fixed style errors --- docs/cugraph/source/index.rst | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/cugraph/source/index.rst b/docs/cugraph/source/index.rst index b66ac6dee30..8a8c8a7fa13 100644 --- a/docs/cugraph/source/index.rst +++ b/docs/cugraph/source/index.rst @@ -66,14 +66,14 @@ Nx-cugraph offers those with existing NetworkX code, a **zero code change** opti from cugraph.datasets import karate G = karate.get_graph() - # Call cugraph.degree_centrality + # 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 _` 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. .. toctree:: :maxdepth: 2 From 228f149ceb90b696c5e02eb87e97298cdda7c754 Mon Sep 17 00:00:00 2001 From: acostadon Date: Mon, 30 Sep 2024 10:03:40 -0400 Subject: [PATCH 07/10] fixed style error --- docs/cugraph/source/index.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/cugraph/source/index.rst b/docs/cugraph/source/index.rst index 8a8c8a7fa13..c68c8592b56 100644 --- a/docs/cugraph/source/index.rst +++ b/docs/cugraph/source/index.rst @@ -46,7 +46,7 @@ This includes several ways to set up cuGraph 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 `_. +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 @@ -79,7 +79,7 @@ is a good place to start. Some of these show `multi-GPU tests/examples Date: Mon, 30 Sep 2024 12:04:00 -0400 Subject: [PATCH 08/10] responded to review comments --- docs/cugraph/source/index.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/cugraph/source/index.rst b/docs/cugraph/source/index.rst index c68c8592b56..f937185f497 100644 --- a/docs/cugraph/source/index.rst +++ b/docs/cugraph/source/index.rst @@ -37,7 +37,7 @@ 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 `_ @@ -49,7 +49,7 @@ 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 `_. If additional help is needed reach out on our `Slack Channel `_. -CuGraph Using NetworkX Code +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. From a59073365ee1fa60c9ea7cb7f5ee9523280d4abe Mon Sep 17 00:00:00 2001 From: rlratzel Date: Wed, 2 Oct 2024 15:30:39 -0500 Subject: [PATCH 09/10] Updates nx-cugraph text, removed build from source section in favor of TOC entry for the same topic. --- docs/cugraph/source/index.rst | 26 ++++++++++++-------------- 1 file changed, 12 insertions(+), 14 deletions(-) diff --git a/docs/cugraph/source/index.rst b/docs/cugraph/source/index.rst index f937185f497..f1688552716 100644 --- a/docs/cugraph/source/index.rst +++ b/docs/cugraph/source/index.rst @@ -10,10 +10,12 @@ 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 -**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. +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 @@ -21,7 +23,7 @@ Required hardware/software for cuGraph and `RAPIDS 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 `_ `see below <#cugraph-using-networkx-code>`_. + * 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 `_. @@ -44,25 +46,21 @@ This includes several ways to set up cuGraph * `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 `_. -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 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. 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 + # Create an instance of the popular Zachary Karate Club graph from cugraph.datasets import karate G = karate.get_graph() From fb8f4ed8e3cece74ba59476be2917c3fc9f63c46 Mon Sep 17 00:00:00 2001 From: rlratzel Date: Wed, 2 Oct 2024 15:33:49 -0500 Subject: [PATCH 10/10] Fixed link. --- docs/cugraph/source/index.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/cugraph/source/index.rst b/docs/cugraph/source/index.rst index f1688552716..259a36b8fd6 100644 --- a/docs/cugraph/source/index.rst +++ b/docs/cugraph/source/index.rst @@ -48,7 +48,7 @@ This includes several ways to set up cuGraph cuGraph Using NetworkX Code -cuGraph is now available as a NetworkX backend using `nx-cugraph _. +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.