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README.md

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@@ -11,22 +11,22 @@ Following are the deployments made in this Capstone assignment
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### Contents
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- [Requirements](#requirements)
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- [Deployments](#development-method)
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- Dataset
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- Model Development
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- DVC setup with AWS S3
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- Docker command for train and test
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- [Deployments](#Deployments)
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- [Dataset]
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- [Model Development]
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- [DVC setup with AWS S3]
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- [Docker command for train and test]
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- [Deployment 01 - Kubernetes]
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- [Manual setup]
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- Architecture Diagram
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- Installation
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- Cluster creation and configuration
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- ArgoCD Deployment
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- Load testing with promethes and Grafana
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- [Architecture Diagram]
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- [Installation]
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- [Cluster creation and configuration]
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- [ArgoCD Deployment]
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- [Load testing with promethes and Grafana]
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- [Deletion Procedure](#deletion-procedure)
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- [Github actions - Automaing Manual setup End to End]
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- [Results Screenshots - D01](#results-screenshots)
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- Video Link
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- [Video Link]
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- [Deployment 02]
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- [Results Screenshots - D02](#results-screenshots)
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- [Deployment 03]
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- lambda
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todo: (17/05/2025)
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1. add manual screenshot - todo
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1. add manual screenshot - done
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2. check in github actions once with debug yaml and then add response time graph in load test in github actions
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3. Do model comparision for 01D
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3. do video for github actions 01A and 01B explanation and merger with manual video
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4. add github actions screenshot
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5. add architecture diagram
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comment load test results in commit
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### Deployments
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### Dataset
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**Download Dataset**
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**Change ALB URLs**
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*Note: In github actions deployment its automatically done with github bots.
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![](./assets/deployment-01-kubernetes/manual/snap_chnage_urls.png)
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**Argo CD Commit**
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![](./assets/deployment-03-lambda/snap_banana.png)
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### Learnings
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1. Learnt about making automated deployment with github actions
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*Mostly other stuffs are already done in previous assignments and its combination of everything
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## Technologies Used
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[PyTorch Lightning](https://github.com/PyTorchLightning/pytorch-lightning) - a lightweight PyTorch wrapper for high-performance AI research. Think of it as a framework for organizing your PyTorch code.

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