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Clone the repository
https://github.com/Subash7Lingden/Machine-Learning-Project-with-mlflow
conda create -n mlproj python=3.8 -y
conda activate mlproj
pip install -r requirements.txt
# Finally run tht following command
python app.py
Now,
open up your local host and port
MLFLOW_TRACKING_URI=https://dagshub.com/Subash7Lingden/Machine-Learning-Project-with-mlflow.mlflow MLFLOW_TRACKING_USERNAME=Subash7Lingden MLFLOW_TRACKING_PASSWORD=e47cb2181458071797d31f2e25e11e1defd6bd99 python script.py
Run this to export as env variables:
set MLFLOW_TRACKING_URI = https://dagshub.com/Subash7Lingden/Machine-Learning-Project-with-mlflow.mlflow
set MLFLOW_TRACKING_USERNAME = Subash7Lingden
set MLFLOW_TRACKING_PASSWORD = e47cb2181458071797d31f2e25e11e1defd6bd99
AWS -CICD-Deployment-with-Github-Actions
# with specific access
a. EC2 access: it is virtual machine
b. ECR: Elastic Container registry to save your docker image in aws
# Description: About the deployment
a. Build docker image if the source code
b. Push your docker image to ECR
c. Lunch your EC2
d. Pull Your image from ECR in EC2
e. Lunch your docker image in EC
# Policy:
a. AmazonEC2CountanerRegitstyFullAccess
b. AmazonEC2FullAccess
#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
setting>actions>runner>new self hosted runner> choose os> then run command one by one
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
MLflow
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