docker-compose.yml

# =============================================================================
# docker/docker-compose.yml — Local deployment of the prediction service
# =============================================================================
# Usage:
#   docker compose -f docker/docker-compose.yml up --build
#
# Prerequisites:
#   - At least one model promoted to Production via the pipeline.
#   - A .env file in the project root (copy from .env.example).
# =============================================================================

services:
  prediction-api:
    build:
      context: ..
      dockerfile: docker/Dockerfile
    container_name: mlops-prediction-api
    ports:
      - "${API_PORT:-8000}:8000"
    volumes:
      # Read-only mount of the MLflow tracking store for model loading.
      - ../mlruns:/app/mlruns:ro
      # Read-only mount of config files so deployment.yaml and pipeline configs
      # can be read at runtime without baking them into the image.
      - ../src/config:/app/src/config:ro
      # Read-only mount of model artifacts for joblib-based model loading.
      - ../artifacts:/app/artifacts:ro
      - ../data/processed:/app/data/processed:ro
    env_file:
      - ../.env
    environment:
      - MLFLOW_TRACKING_URI=/app/mlruns
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8000/health')"]
      interval: 30s
      timeout: 5s
      start_period: 60s
      retries: 3