first copy of general cugraph tutorial.

docs/cugraph/source/tutorials/basic_cugraph.md

@@ -0,0 +1,28 @@
+## Required hardware/software
+
+CuGraph is part of [Rapids](https://docs.rapids.ai/user-guide)
+It 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).**
+
+## 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
+* 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)
+    * [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
+
+## 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.
+
+
+## Cugraph API demo

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)