From 309bbfb4a9fea3b7e0434065d07ae366cc6e9674 Mon Sep 17 00:00:00 2001 From: acostadon Date: Tue, 14 May 2024 15:49:56 -0400 Subject: [PATCH] corrections from review --- .../cugraph/source/tutorials/basic_cugraph.md | 20 +++++++++++-------- 1 file changed, 12 insertions(+), 8 deletions(-) diff --git a/docs/cugraph/source/tutorials/basic_cugraph.md b/docs/cugraph/source/tutorials/basic_cugraph.md index b3486d2d8ba..f9903827f49 100644 --- a/docs/cugraph/source/tutorials/basic_cugraph.md +++ b/docs/cugraph/source/tutorials/basic_cugraph.md @@ -1,28 +1,32 @@ ## Required hardware/software -CuGraph is part of [Rapids](https://docs.rapids.ai/user-guide) -It has the following system requirements +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 3.0 (version 3.3 or higher recommended) **This if for use of nx-cuGraph, [see below](#cugraph-using-networkx-code).** + * NetworkX >= version 3.3 or higherin order to use use [NetworkXCongig](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 for cuGraph +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) -* To use RAPIDS in windows you must install [WSL2](https://learn.microsoft.com/en-us/windows/wsl/install) + +* 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 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/). -This is much simpler but limits users to the current but growing list of suppored algorithms. +Nx-cugraph offers those with existing nwtworkX code a zero code change option with a growing list of supported algorithms. ## Cugraph API demo \ No newline at end of file