docker-compose.yml

version: '3.9'

volumes:
  s3-data: {}
  mlflow-db-data: {}
  mongo-data: {}
  prometheus-data: {}
  grafana-data: {}

services:
  mlflow-db:
    image: postgres:14.3
    container_name: mlflow-db
    environment:
      POSTGRES_DB: ${POSTGRES_DB}
      POSTGRES_USER: ${POSTGRES_USER}
      POSTGRES_PASSWORD: ${POSTGRES_PASSWORD}
    expose:
      - "5432"
    ports:
      - "127.0.0.1:5432:5432"
    volumes:
      - "mlflow-db-data:/var/lib/postgresql/"

  minio:
    image: minio/minio:RELEASE.2022-05-19T18-20-59Z
    container_name: minio
    command: server /data --console-address ":9001"
    expose:
      - "9000"
      - "9001"
    ports:
      - "127.0.0.1:9000:9000"
      - "127.0.0.1:9001:9001"
    volumes:
      - "s3-data:/data"
    environment:
      MINIO_SITE_REGION: ${AWS_DEFAULT_REGION}
      MINIO_ROOT_USER: ${AWS_ACCESS_KEY_ID}
      MINIO_ROOT_PASSWORD: ${AWS_SECRET_ACCESS_KEY}

  createbuckets:
    image: minio/mc
    container_name: createbuckets
    depends_on:
      - minio
    entrypoint: >
      /bin/sh -c "
      echo sleep 15;
      sleep 15;
      /usr/bin/mc config host add myminio http://minio:9000 ${AWS_ACCESS_KEY_ID} ${AWS_SECRET_ACCESS_KEY};
      /usr/bin/mc mb myminio/${EXPERIMENT_NAME};
      exit 0;
      "

  mlflow-server:
    build:
      context: ./app
      dockerfile: Dockerfile
    image: peco602/maternal-health-risk-predictor:latest
    container_name: mlflow-server
    environment:
      AWS_REGION: ${AWS_REGION}
      AWS_DEFAULT_REGION: ${AWS_DEFAULT_REGION}
      AWS_ACCESS_KEY_ID: ${AWS_ACCESS_KEY_ID}
      AWS_SECRET_ACCESS_KEY: ${AWS_SECRET_ACCESS_KEY}
      MLFLOW_S3_ENDPOINT_URL: http://minio:9000
    expose:
      - "5000"
    ports:
      - "127.0.0.1:5000:5000"
    command: mlflow server --host 0.0.0.0 --backend-store-uri postgresql://${POSTGRES_USER}:${POSTGRES_PASSWORD}@mlflow-db:5432/${POSTGRES_DB} --default-artifact-root s3://${EXPERIMENT_NAME}/mlflow
    depends_on:
      - mlflow-db
      - minio

  mongo:
    image: mongo
    container_name: mongo
    ports:
      - "127.0.0.1:27017:27017"
    volumes:
      - mongo-data:/data/db

  prometheus:
    image: prom/prometheus
    container_name: prometheus
    volumes:
      - ./monitoring/config/prometheus.yml:/etc/prometheus/prometheus.yml
      - prometheus-data:/prometheus
    command:
      - '--config.file=/etc/prometheus/prometheus.yml'
      - '--storage.tsdb.path=/prometheus'
    ports:
      - "127.0.0.1:9090:9090"
    restart: always

  grafana:
    image: grafana/grafana
    container_name: grafana
    user: "472"
    depends_on:
      - prometheus
    ports:
      - "127.0.0.1:3000:3000"
    volumes:
      - ./monitoring/config/grafana_datasources.yaml:/etc/grafana/provisioning/datasources/datasource.yaml:ro
      - ./monitoring/config/grafana_dashboards.yaml:/etc/grafana/provisioning/dashboards/dashboards.yaml:ro
      - ./monitoring/dashboards:/opt/grafana/dashboards
      - grafana-data:/var/lib/grafana
    restart: always

  evidently-service:
    build:
      context: monitoring
      dockerfile: Dockerfile
    image: peco602/maternal-health-risk-monitoring-service:latest
    container_name: evidently-service
    environment:
      MONGODB_URI: "mongodb://mongo:27017"
      EXPERIMENT_NAME: ${EXPERIMENT_NAME}
      MIN_AGE: ${MIN_AGE}
      MAX_AGE: ${MAX_AGE}
    depends_on:
      - grafana
    volumes:
      - ./data:/app/datasets
      - ./monitoring/config.yaml:/app/config.yaml
    ports:
      - "127.0.0.1:8085:8085"

  web-app:
    build:
      context: ./app
      dockerfile: Dockerfile
    image: peco602/maternal-health-risk-predictor:latest
    container_name: web-app
    environment:
      AWS_REGION: ${AWS_REGION}
      AWS_DEFAULT_REGION: ${AWS_DEFAULT_REGION}
      AWS_ACCESS_KEY_ID: ${AWS_ACCESS_KEY_ID}
      AWS_SECRET_ACCESS_KEY: ${AWS_SECRET_ACCESS_KEY}
      MLFLOW_ENABLED: "True"
      MLFLOW_TRACKING_URI: http://mlflow-server:5000
      DEFAULT_MODEL_ENABLED: ${DEFAULT_MODEL_ENABLED}
      MLFLOW_S3_ENDPOINT_URL: http://minio:9000
      MONITORING_ENABLED: "True"
      EVIDENTLY_SERVICE_URI: http://evidently-service:8085
      MONGODB_URI: "mongodb://mongo:27017"
      EXPERIMENT_NAME: ${EXPERIMENT_NAME}
      MIN_AGE: ${MIN_AGE}
      MAX_AGE: ${MAX_AGE}
    command: "gunicorn --bind=0.0.0.0:8081 predict:app"
    expose:
      - "8081"
    ports:
      - "80:8081"
    depends_on:
      - mlflow-server
      - evidently-service
      - mongo
    restart: on-failure

  prefect:
    build:
      context: ./app
      dockerfile: Dockerfile
    image: peco602/maternal-health-risk-predictor:latest
    container_name: prefect
    environment:
      KAGGLE_USERNAME: ${KAGGLE_USERNAME}
      KAGGLE_KEY: ${KAGGLE_KEY}
      EXPERIMENT_NAME: ${EXPERIMENT_NAME}
      AWS_REGION: ${AWS_REGION}
      AWS_DEFAULT_REGION: ${AWS_DEFAULT_REGION}
      AWS_ACCESS_KEY_ID: ${AWS_ACCESS_KEY_ID}
      AWS_SECRET_ACCESS_KEY: ${AWS_SECRET_ACCESS_KEY}
      MLFLOW_S3_ENDPOINT_URL: http://minio:9000
      MLFLOW_TRACKING_URI: http://mlflow-server:5000
      MODEL_SEARCH_ITERATIONS: ${MODEL_SEARCH_ITERATIONS}
    command: "prefect orion start --host=0.0.0.0"
    volumes:
      - ./data:/app/data
    expose:
      - "4200"
    ports:
      - "127.0.0.1:4200:4200"
    depends_on:
      - mlflow-server
    restart: on-failure