Merge pull request #49 from Sai-Suraj-27/documentation_updates

Updated docs, and few other files.

CONTRIBUTING.md

@@ -1,7 +1,7 @@
 # Contributing Guidelines
 
-Welcome to Ianvs. We are excited about the prospect of you joining our [community](https://github.com/kubeedge/community)! The KubeEdge community abides by the CNCF [code of conduct](CODE-OF-CONDUCT.md). Here is an excerpt:
+Welcome to Ianvs. We are excited about the prospect of you joining our [community](https://github.com/kubeedge/community)! The KubeEdge community abides by the CNCF [code of conduct](https://github.com/cncf/foundation/blob/main/code-of-conduct.md). Here is an excerpt:
 
 _As contributors and maintainers of this project, and in the interest of fostering an open and welcoming community, we pledge to respect all people who contribute through reporting issues, posting feature requests, updating documentation, submitting pull requests or patches, and other activities._
 
-To learn more about contributing to the [Ianvs code repo](README.md), check out the [contributing guide](docs/guides). For example, [How to contribute algorithms] and [How to contribute test environments]. 
+To learn more about contributing to the [Ianvs code repo](README.md), check out the [contributing guide](docs/guides). For example, [How to contribute algorithms] and [How to contribute test environments].

README.md

@@ -1,37 +1,42 @@
 # Ianvs
+
 [![CI](https://github.com/kubeedge/ianvs/workflows/CI/badge.svg?branch=main)](https://github.com/sedna/ianvs/actions)
 [![LICENSE SCAN](https://app.fossa.com/api/projects/custom%2B32178%2Fgithub.com%2Fkubeedge%2Fianvs.svg?type=shield)](https://app.fossa.com/projects/custom%2B32178%2Fgithub.com%2Fkubeedge%2Fianvs?ref=badge_shield)
 [![LICENSE](https://img.shields.io/github/license/kubeedge-sedna/ianvs.svg)](/LICENSE)
 
-Ianvs is a distributed synergy AI benchmarking project incubated in KubeEdge SIG AI. Ianvs aims to test the performance of distributed synergy AI solutions following recognized standards, in order to facilitate more efficient and effective development. More detailedly, Ianvs prepares not only test cases with datasets and corresponding algorithms, but also benchmarking tools including simulation and hyper-parameter searching. Ianvs also reveals best practices for developers and end users with presentation tools including leaderboards and test reports. 
+Ianvs is a distributed synergy AI benchmarking project incubated in KubeEdge SIG AI. Ianvs aims to test the performance of distributed synergy AI solutions following recognized standards, in order to facilitate more efficient and effective development. More detailedly, Ianvs prepares not only test cases with datasets and corresponding algorithms, but also benchmarking tools including simulation and hyper-parameter searching. Ianvs also reveals best practices for developers and end users with presentation tools including leaderboards and test reports.
 
 ## Scope
+
 The distributed synergy AI benchmarking Ianvs aims to test the performance of distributed synergy AI solutions following recognized standards, in order to facilitate more efficient and effective development. 
 
 The scope of Ianvs includes
-- Providing end-to-end benchmark toolkits across devices, edge nodes and cloud nodes based on typical distributed-synergy AI paradigms and applications. 
-    - Tools to manage test environment. For example, it would be necessary to support the CRUD (Create, Read, Update and Delete) actions in test environments. Elements of such test environments include algorithm-wise and system-wise configuration.  
+
+- Providing end-to-end benchmark toolkits across devices, edge nodes, and cloud nodes based on typical distributed-synergy AI paradigms and applications.
+    - Tools to manage test environment. For example, it would be necessary to support the CRUD (Create, Read, Update, and Delete) actions in test environments. Elements of such test environments include algorithm-wise and system-wise configuration.  
     - Tools to control test cases. Typical examples include paradigm templates, simulation tools, and hyper-parameter-based assistant tools.
-    - Tools to manage benchmark presentation, e.g., leaderboard and test report generation. 
-- Cooperation with other organizations or communities, e.g., in KubeEdge SIG AI, to establish comprehensive benchmarks and developed related applications, which can include but are not limited to 
+    - Tools to manage benchmark presentation, e.g., leaderboard and test report generation.
+- Cooperation with other organizations or communities, e.g., in KubeEdge SIG AI, to establish comprehensive benchmarks and developed related applications, which can include but are not limited to
     - Dataset collection, re-organization, and publication
-    - Formalized specifications, e.g., standards 
+    - Formalized specifications, e.g., standards
     - Holding competitions or coding events, e.g., open source promotion plan
-    - Maintaining solution leaderboards or certifications for commercial usage 
+    - Maintaining solution leaderboards or certifications for commercial usage
 
 
 ## Architecture
+
 The architectures and related concepts are shown in the below figure. The ianvs is designed to run **within a single node**. Critical components include
+
 - Test Environment Manager: the CRUD of test environments serving for global usage
 - Test Case Controller: control the runtime behavior of test cases like instance generation and vanish 
-    - Generation Assistant: assist users to generate test cases based on certain rules or constraints, e.g., the range of parameters 
+    - Generation Assistant: assist users to generate test cases based on certain rules or constraints, e.g., the range of parameters
     - Simulation Controller: control the simulation process of edge-cloud synergy AI, including the instance generation and vanishment of simulation containers
 - Story Manager: the output management and presentation of the test case, e.g., leaderboards
 
-
 ![](docs/guides/images/ianvs_arch.png)
 
-More details on Ianvs components: 
+More details on Ianvs components:
+
 1. Test-Environment Manager supports the CRUD of Test environments, which basically includes
     - Algorithm-wise configuration
         - Public datasets
@@ -41,12 +46,12 @@ More details on Ianvs components:
     - System-wise configuration
         - Overall architecture
         - System constraints or budgets
-            - End-to-end cross-node 
+            - End-to-end cross-node
             - Per node
-1. Test-case Controller, which includes but is not limited to the following components 
-    - Templates of common distributed-synergy-AI paradigms, which can help the developer to prepare their test case without too much effort. Such paradigms include edge-cloud synergy joint inference, incremental learning, federated learning, and lifelong learning. 
+1. Test-case Controller, which includes but is not limited to the following components
+    - Templates of common distributed-synergy-AI paradigms, which can help the developer to prepare their test case without too much effort. Such paradigms include edge-cloud synergy joint inference, incremental learning, federated learning, and lifelong learning.
     - Simulation tools. Develop simulated test environments for test cases
-    - Other tools to assist test-case generation. For instance, prepare test cases based on a given range of hyper-parameters. 
+    - Other tools to assist test-case generation. For instance, prepare test cases based on a given range of hyper-parameters.
 1. Story Manager, which includes but is not limited to the following components
     - Leaderboard generation
     - Test report generation
@@ -58,27 +63,29 @@ More details on Ianvs components:
 
 Documentation is located on [readthedoc.io](https://ianvs.readthedocs.io/). The documents include the quick start, guides, dataset descriptions, algorithms, user interfaces, stories, and roadmap.
 
-
 ### Installation
+
 Follow the [Ianvs installation document](docs/guides/how-to-install-ianvs.md) to install Ianvs.
 
 ### Examples
+
 Scenario PCB-AoI：[Industrial Defect Detection on the PCB-AoI Dataset](/docs/proposals/scenarios/industrial-defect-detection/pcb-aoi.md).  
 Example PCB-AoI-1：[Testing single task learning in industrial defect detection](/docs/proposals/test-reports/testing-single-task-learning-in-industrial-defect-detection-with-pcb-aoi.md).  
 Example PCB-AoI-2：[Testing incremental learning in industrial defect detection](/docs/proposals/test-reports/testing-incremental-learning-in-industrial-defect-detection-with-pcb-aoi.md).  
 
-
 ## Roadmap
 
-* [2022 H2 Roadmap](docs/roadmap.md)
+- [2022 H2 Roadmap](docs/roadmap.md)
 
 ## Meeting
 
 Routine Community Meeting for KubeEdge SIG AI runs weekly:
+
 - Europe Time: **Thursdays at 16:30-17:30 Beijing Time**.
 ([Convert to your timezone.](https://www.thetimezoneconverter.com/?t=16%3A30&tz=GMT%2B8&))
 
 Resources:
+
 - [Meeting notes and agenda](https://docs.google.com/document/d/12n3kGUWTkAH4q2Wv5iCVGPTA_KRWav_eakbFrF9iAww/edit)
 - [Meeting recordings](https://www.youtube.com/playlist?list=PLQtlO1kVWGXkRGkjSrLGEPJODoPb8s5FM)
 - [Meeting link](https://zoom.us/j/4167237304)
@@ -91,6 +98,7 @@ If you need support, start with the [troubleshooting guide](./docs/troubleshooti
 -->
 
 If you have questions, feel free to reach out to us in the following ways:
+
 - [slack channel](https://app.slack.com/client/TDZ5TGXQW/C01EG84REVB/details)
 
 ## Contributing