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# Setup Virtual Environment

```python
conda create -n fastapi-env python=3.12.4
conda activate fastapi-env
pip install -r requirements.txt
```

# Running the server
`uvicorn main:app --reload`
# `uvicorn main:my_first_api --reload`

The command `uvicorn main:app` refers to:
- main: the file main.py (the Python "module").
- app: the object created inside of main.py with the line app = FastAPI().
- --reload: make the server restart after code changes. Only use for development.




```

sudo yum update -y
sudo amazon-linux-extras install docker
sudo service docker start
sudo systemctl start docker
sudo service docker status
sudo groupadd docker
sudo usermod -a -G docker ec2-user
newgrp docker
docker —-version

# create ECR with name: my-flask-app
aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 866824485776.dkr.ecr.us-east-1.amazonaws.com

About

Este proyecto automatiza el despliegue de un modelo de machine learning utilizando GitHub Actions. El pipeline CI/CD construye, prueba y despliega una imagen Docker en Amazon ECR, luego se implementa en una instancia EC2 para predecir la tasa de cancelación de clientes de la empresa Interconnect.

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