Chicken-Disesase-Classifier

Introduction

Welcome to the Chicken Disease Classifier project! This is an end-to-end deep learning project using MLOps DVC pipeline, showcasing deployments on AWS platforms. The project aims to classify images of chickens based on various diseases they might have, thereby enabling early detection and effective disease management.

Project Overview

The Chicken Disease Classifier project covers the following key components:

1. Data Ingestion: Loading and preparing the dataset for training and validation.
2. Data Validation: Ensuring the integrity and quality of the dataset.
3. Model Training: Training a deep learning model using the prepared dataset.
4. Model Evaluation: Evaluating the trained model's performance and accuracy.
5. Web Application: Creating a Flask-based web application for image classification.
6. Deployment: Deploying the trained model on Azure and AWS platforms.

• Throughout the project, we'll emphasize modular coding, configuration management, and the use of MLOps tools like DVC for efficient pipeline execution and version control.

Prerequisites

Before you begin, ensure you have the following installed:

• Python (version 3.6 or higher)
• TensorFlow (version 2.x)
• Keras (version 2.x)
• Flask (version 2.x)
• DVC (Data Version Control)
• Docker (for AWS deployment)
• AWS Account (for AWS deployment)

Workflows

1. Update config.yaml
2. Update secrets.yaml [Optional]
3. Update params.yaml
4. Update the entity
5. Update the Configuration Manager in src/config
6. Update the Components
7. Update the pipeline
8. Update the main.py
9. Update the dvc.yaml

How to run?

STEPS:

Clone the repository

https://github.com/CS-Aditya-Rawat/Chicken-Disease-Classifier

STEP 01- Create a conda environment after opening the repository

conda create -n cnncls python=3.8 -y

conda activate cnncls

STEP 02- install the requirements

pip install -r requirements.txt

# Finally run the following command
python app.py

Now,

open up you local host and port

DVC cmd

1. dvc init
2. dvc repro
3. dvc dag

AWS-CICD-Deployment-with-Github-Actions

1. Login to AWS console.

2. Create IAM user for deployment

with specific access

1. EC2 access : It is virtual machine

2. ECR: Elastic Container registry to save your docker image in aws

Description: About the deployment

1. Build docker image of the source code

2. Push your docker image to ECR

3. Launch Your EC2

4. Pull Your image from ECR in EC2

5. Lauch your docker image in EC2

Policy:

1. AmazonEC2ContainerRegistryFullAccess

2. AmazonEC2FullAccess

3. Create ECR repo to store/save docker image

- Save the URI: 566373416292.dkr.ecr.us-east-1.amazonaws.com/chicken

4. Create EC2 machine (Ubuntu)

5. Open EC2 and Install docker in EC2 Machine:

#optinal

sudo apt-get update -y

sudo apt-get upgrade

#required

curl -fsSL https://get.docker.com -o get-docker.sh

sudo sh get-docker.sh

sudo usermod -aG docker ubuntu

newgrp docker

6. Configure EC2 as self-hosted runner:

setting>actions>runner>new self hosted runner> choose os> then run command one by one

7. Setup github secrets:

AWS_ACCESS_KEY_ID=

AWS_SECRET_ACCESS_KEY=

AWS_REGION = us-east-1

AWS_ECR_LOGIN_URI = demo>>  566373416292.dkr.ecr.ap-south-1.amazonaws.com

ECR_REPOSITORY_NAME = simple-app