GH-3: Further fixes and clean up in Readme

Author: vidyaWS

README.md

@@ -2,7 +2,7 @@
 
 TwinGraph provides a Python-based high-throughput container orchestration framework for simulation, predictive modeling and optimization workflows. It supports asynchronous multi-host computing, dynamic directed-acyclic-graph (DAG) pipelines, and recording of custom workflow artifacts and attributes within a graph database, for repeatability and auditability.
 
-TwinGraph is used by adding decorators to Python functions to record attributes associated with these functions, such as inputs/outputs, source code and compute platform in a [TinkerGraph](https://tinkerpop.apache.org/docs/current/reference/) or [Amazon Neptune](https://aws.amazon.com/neptune/) database. It is also optionally a graph orchestrator using [Celery](https://docs.celeryq.dev/en/stable/getting-started/introduction.html) in the backend and runs the decorated functions on a chosen compute ([AWS Batch](https://aws.amazon.com/batch/), [AWS Lambda](https://aws.amazon.com/lambda/), [Amazon EKS](https://aws.amazon.com/eks/)) and container orchestrator([Kubernetes](https://kubernetes.io/), [Docker Compose](https://docs.docker.com/compose/)) in an asynchronous manner.
+TwinGraph is used by adding decorators to Python functions to record attributes associated with these functions, such as inputs/outputs, source code and compute platform in a [TinkerGraph](https://tinkerpop.apache.org/docs/current/reference/) or [Amazon Neptune](https://aws.amazon.com/neptune/) database. It is also optionally a graph orchestrator using [Celery](https://docs.celeryq.dev/en/stable/getting-started/introduction.html) in the backend and runs the decorated functions on a chosen compute ([AWS Batch](https://aws.amazon.com/batch/), [AWS Lambda](https://aws.amazon.com/lambda/), [Amazon EKS](https://aws.amazon.com/eks/)) and container orchestrator ([Kubernetes](https://kubernetes.io/), [Docker Compose](https://docs.docker.com/compose/)) in an asynchronous manner.
 
 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:
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