Workflows

1 Update config.yaml 2 Update params.yaml 3 Update entity 4 Update the configuration manager in src config 5 update the conponents 6 update the pipeline 7 update the main.py 8 update the app.py

How to run?

STEPS:

Clone the repository https://github.com/NimraAslamkhan/MLPROJECT-student-performance-prediction

STEP 01- Create a conda environment after opening the repository

conda create -n summary python=3.12.1 -y conda activate summary

STEP 02- install the requirements

pip install -r requirements.txt

Finally run the following command

python app.py open up you local host and port Author: NimraAslmaKhan Data Scientist Email: [email protected]

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

4. Create EC2 machine (Ubuntu)

5Open 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

6Configure EC2 as self-hosted runner:

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

7Setup 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