check app at: http://3.108.62.164:8080/
@ensure_annotations is a decorator to check the input parameters type for production level importance
- Update config.yaml
- Update schema.yaml
- Update params.yaml
- Update the entity
- Update the configuration manager in src config
- Update the components
- Update the pipeline
- Update the main.py
- Update the app.py
Clone the repository
https://github.com/X-sena-X/EndToEndMLop.git
conda create -n mlproj python=3.8 -y
conda activate mlproj
pip install -r requirements.txt
# Finally run the following command
python app.py
Now,
open up you local host and port
- mlflow ui
MLFLOW_TRACKING_URI=https://dagshub.com/X-sena-X/EndToEndMLop.mlflow
MLFLOW_TRACKING_USERNAME=X-sena-X
MLFLOW_TRACKING_PASSWORD=262488a71dcbed1cd58fe6f82a2d1cbe5db8ce9a
python script.py
Run this to export as env variables:
export MLFLOW_TRACKING_URI=https://dagshub.com/X-sena-X/EndToEndMLop.mlflow
export MLFLOW_TRACKING_USERNAME=X-sena-X
export MLFLOW_TRACKING_PASSWORD=262488a71dcbed1cd58fe6f82a2d1cbe5db8ce9a
#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: 611205146900.dkr.ecr.ap-south-1.amazonaws.com/mlproj
#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 = ap-south-1
AWS_ECR_LOGIN_URI = 566373416292.dkr.ecr.ap-south-1.amazonaws.com
ECR_REPOSITORY_NAME = mlproj
MLflow
- Its Production Grade
- Trace all of your expriements
- Logging & tagging your model