fixes per review and adding reference to test and notebook examples.

rapidsai · May 28, 2024 · a1a1e58 · a1a1e58
1 parent 309bbfb
commit a1a1e58
Showing 1 changed file with 8 additions and 2 deletions.
diff --git a/docs/cugraph/source/tutorials/basic_cugraph.md b/docs/cugraph/source/tutorials/basic_cugraph.md
@@ -1,10 +1,12 @@
+# 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 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).**
+ * NetworkX >= version 3.3 or newer in 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).
@@ -29,4 +31,8 @@ While the steps above are required to use the full suite of cuGraph graph analyt
 Nx-cugraph offers those with existing nwtworkX code a zero code change option with a growing list of supported algorithms.
 
 
-## Cugraph API demo
+## Cugraph API demo
+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.