first copy of general cugraph tutorial. (#4396)

Adding a tutorial to get started with cugraph.

There are more to follow but this is identified as an important one.

closes #4385

Authors:
  - Don Acosta (https://github.com/acostadon)

Approvers:
  - Brad Rees (https://github.com/BradReesWork)

URL: #4396

docs/cugraph/source/tutorials/basic_cugraph.md

@@ -0,0 +1,38 @@
+# 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.

docs/cugraph/source/tutorials/how_to_guides.md

@@ -1,5 +1,5 @@
 # How To Guides
-- Basic use of cuGraph, on the page
+- [Basic use of cuGraph](./basic_cugraph.md)
 - Property graph with analytic flow
 - GNN – model building
 - cuGraph Service – client/server setup and use (ucx)