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version: "3.8"
services:
# Step 1: Run Deployment
deployment:
build:
context: .
dockerfile: Dockerfile
command: >
bash -c "
zenml init &&
zenml integration install mlflow -y &&
zenml experiment-tracker register mlflow_tracker_customer_churn_new --flavor=mlflow &&
zenml model-deployer register mlflow_customer_churn_new --flavor=mlflow &&
zenml stack register mlflow_stack_customer_churn_new -a default -o default -d mlflow -e mlflow_tracker_customer_churn_new --set &&
zenml stack set mlflow_stack_customer_churn_new &&
python3 run_pipeline.py&&
python3 run_deployment.py
"
volumes:
- .:/app
working_dir: /app
restart: on-failure
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8000/health"] # Adjust URL for deployment health check
interval: 10s
retries: 3
start_period: 5s
timeout: 5s
# Step 2: Run FastAPI service after Deployment is completed
fastapi_service:
build:
context: .
dockerfile: Dockerfile
command: ["uvicorn", "backend.fastapi_app:app", "--host", "0.0.0.0", "--port", "8001"]
depends_on:
- deployment
volumes:
- .:/app
working_dir: /app
ports:
- "8001:8001"
restart: on-failure
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8001/health"] # Adjust URL for FastAPI health check
interval: 10s
retries: 3
start_period: 5s
timeout: 5s
# Step 3: Run Streamlit UI after FastAPI service is up
streamlit:
build:
context: .
dockerfile: Dockerfile
command: ["streamlit", "run", "frontend/main.py"]
depends_on:
- fastapi_service
volumes:
- .:/app
working_dir: /app
ports:
- "8501:8501"
restart: on-failure
healthcheck:
test: ["CMD", "curl", "-f", "http://localhost:8501/health"] # Adjust URL for Streamlit health check
interval: 10s
retries: 3
start_period: 5s
timeout: 5s
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