CKD prediction using classification model

STEP-1: Clone the repository

https://github.com/SAMANTA1401/ckd_prediction

STEP-2: Create a conda environment after opening the repository

conda create -p venv python==3.10 -y

conda activate venv

STEP-3: Install the requirements

pip install -r requirements.txt
pip freeze
pip freeze > requirements.txt

STEP-4: RUN : For data ingestion, data transformation, preproccessing and model training

python -m src.components.data_ingestion

or

dvc init
dvc repro

STEP-5: RUN: For prediction

python app.py

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: 987001014426.dkr.ecr.eu-north-1.amazonaws.com/kidney

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 = eu-north-1

AWS_ECR_LOGIN_URI = demo>>  987001014426.dkr.ecr.eu-north-1.amazonaws.com

ECR_REPOSITORY_NAME = kidney