GH-1: Centering of image caption.

Author: vidyaWS

README.md

@@ -6,17 +6,17 @@ TwinGraph is used by adding decorators to Python functions to record attributes
 
 TwinGraph scales to hundreds of thousands of containerized compute tasks in a number of different compute nodes/hosts; communication of information between tasks is handled through message queues in an event-driven workflow chain. An example architectural flow of information is shown in Figure 1:
 <center>
-<img src="docs/figures/OverallPicture.png" width=780>
-Figure 1: Overall Information Flow
-</center>
+<img src="docs/figures/OverallPicture.png" width=780></center>
+<center>Figure 1: Overall Information Flow.</center>
+
 
 
 There are a number of key capabilities outlined in the following Figure 2 for TwinGraph. Instead of domain specific language (DSL) for specifying pipelines, TwinGraph uses algorithms defined in native Python (loops, conditionals & recursion) to define the control flow, i.e. dependencies of graph structure on intermediate outputs and runtime stochasticity.
 
 <center>
-<img src="docs/figures/Challenges.png" width=780>
-Figure 2: Challenges in Designing Orchestrators
-</center>
+<img src="docs/figures/Challenges.png" width=780></center>
+<center>Figure 2: Challenges in Designing Orchestrators</center>
+
 
 
 The examples highlighted in the next section provide an overview of the capabilities of TwinGraph.