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Clone the repository
https://github.com/saikrishnabudi/Machine-Learning-Project-with-MLflow.git
conda create -n mlproject python=3.12 -y
conda activate mlproject
pip install -r requirements.txt
# Run the app.py
python app.py
Now, open up the local host and port
[Documentation](https://mlflow.org/docs/latest/index.html)
- mlflow ui
[dagshub](https://dagshub.com/dashboard)
MLFLOW_TRACKING_URI=https://dagshub.com/saikrishnabudi/Machine-Learning-Project-with-MLflow.mlflow \
MLFLOW_TRACKING_USERNAME=saikrishnabudi \
MLFLOW_TRACKING_PASSWORD=3efda87a7165edecfd46af3e63ed69f73a2d4801 \
python script.py
Run this to expert as env variables:
export MLFLOW_TRACKING_URI=https://dagshub.com/saikrishnabudi/Machine-Learning-Project-with-MLflow.mlflow
export MLFLOW_TRACKING_USERNAME=saikrishnabudi
export MLFLOW_TRACKING_PASSWORD=3efda87a7165edecfd46af3e63ed69f73a2d4801
#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
- save the URI: 264413606393.dkr.ecr.ap-south-1.amazonaws.com/mlproject
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
Docker version 26.0.2, build 3c863ff
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 = ap-south-1
AWS_ECR_LOGIN_URI = demo>> 264413606393.dkr.ecr.ap-south-1.amazonaws.com
ECR_REPOSITORY_NAME = mlproject
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Are you sure you want to delete this access key?
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?
Are you sure you want to delete this access key?