- 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/entbappy/End-to-end-Machine-Learning-Project-with-MLflow
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/entbappy/End-to-end-Machine-Learning-Project-with-MLflow.mlflow
MLFLOW_TRACKING_USERNAME=entbappy
MLFLOW_TRACKING_PASSWORD=6824692c47a369aa6f9eac5b10041d5c8edbcef0
python script.py
Run this to export as env variables:
export MLFLOW_TRACKING_URI=https://dagshub.com/entbappy/End-to-end-Machine-Learning-Project-with-MLflow.mlflow
export MLFLOW_TRACKING_USERNAME=entbappy
export MLFLOW_TRACKING_PASSWORD=6824692c47a369aa6f9eac5b10041d5c8edbcef0
#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: 566373416292.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 = us-east-1
AWS_ECR_LOGIN_URI = demo>> 566373416292.dkr.ecr.ap-south-1.amazonaws.com
ECR_REPOSITORY_NAME = simple-app
MLflow
- Its Production Grade
- Trace all of your expriements
- Logging & tagging your model