Final review

rapidsai · Oct 4, 2024 · 1872c0f · 1872c0f
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diff --git a/docs/cugraph/source/basics/index.rst → docs/cugraph/source/basics/index.md b/docs/cugraph/source/basics/index.rst → docs/cugraph/source/basics/index.md
@@ -1,6 +1,4 @@
-====================
-cuGraph Introduction
-====================
+# cuGraph Introduction
 
 The Data Scientist has a collection of techniques within their
 proverbial toolbox. Data engineering, statistical analysis, and
@@ -22,9 +20,7 @@ into the RAPIDS data science ecosystem and allows the data scientist to easily
 call graph algorithms using data stored in a GPU DataFrame, NetworkX Graphs, or even
 CuPy or SciPy sparse Matrix.
 
--------------------
-Vision
--------------------
+## Vision
 
 The vision of RAPIDS cuGraph is to ___make graph analysis ubiquitous to the
 point that users just think in terms of analysis and not technologies or
@@ -52,9 +48,7 @@ high-speed ETL, statistics, and machine learning. To make things even better,
 RAPIDS and DASK allows cuGraph to scale to multiple GPUs to support
 multi-billion edge graphs.
 
--------------------
-Terminology
--------------------
+## Terminology
 
 cuGraph is a collection of GPU accelerated graph algorithms and graph utility
 functions. The application of graph analysis covers a lot of areas.

diff --git a/docs/cugraph/source/index.rst b/docs/cugraph/source/index.rst
@@ -21,7 +21,7 @@ cuGraph is now available as a NetworkX backend using `nx-cugraph <https://rapids
 Our major integration effort with NetworkX offers NetworkX users a **zero code change** option to accelerate
 their existing NetworkX code using an NVIDIA GPU and cuGraph.
 
-Check out `zero code change accelerated NetworkX <nx_cugraph/index>`_. If you would like to continue using standard cuGraph, then continue down below.
+Check out `zero code change accelerated NetworkX <nx_cugraph/index.rst>`_. If you would like to continue using standard cuGraph, then continue down below.
 
 ----------------------------
 Getting started with cuGraph

diff --git a/docs/cugraph/source/nx_cugraph/how-it-works.md b/docs/cugraph/source/nx_cugraph/how-it-works.md
@@ -97,14 +97,14 @@ Wall time: 5.32 s
 ```
 This run will be much faster, typically around 5 seconds depending on your GPU.
 
-<div style="color: black; padding: 10px; border-radius: 5px;">
+<div style="color: black;">
 
 *Note, the examples above were run using the following specs*:
 
-    NetworkX 3.4
-    nx-cugraph 24.10
-    CPU: Intel(R) Xeon(R) Gold 6128 CPU @ 3.40GHz 45GB RAM
-    GPU: NVIDIA Quadro RTX 8000 80GB RAM
+&nbsp;&nbsp;&nbsp;&nbsp;*NetworkX 3.4* <br>
+&nbsp;&nbsp;&nbsp;&nbsp;*nx-cugraph 24.10* <br>
+&nbsp;&nbsp;&nbsp;&nbsp;*CPU: Intel(R) Xeon(R) Gold 6128 CPU @ 3.40GHz 45GB RAM* <br>
+&nbsp;&nbsp;&nbsp;&nbsp;*GPU: NVIDIA Quadro RTX 8000 80GB RAM* <br>
 
 </div>