mlflow-docker-minio/docker-compose.yml

version: "3.9"
services:
  s3:
    image: minio/minio:RELEASE.2021-11-24T23-19-33Z
    restart: unless-stopped
    ports:
      - "9000:9000"
      - "9001:9001"
    environment:
      - MINIO_ROOT_USER=${AWS_ACCESS_KEY_ID}
      - MINIO_ROOT_PASSWORD=${AWS_SECRET_ACCESS_KEY}
    command: server /data --console-address ":9001"
    networks:
      - internal
      - public
    volumes:
      - minio_volume:/data
  db:
    image: mysql/mysql-server:5.7.28
    restart: unless-stopped
    container_name: mlflow_db
    expose:
      - "3306"
    environment:
      - MYSQL_DATABASE=${MYSQL_DATABASE}
      - MYSQL_USER=${MYSQL_USER}
      - MYSQL_PASSWORD=${MYSQL_PASSWORD}
      - MYSQL_ROOT_PASSWORD=${MYSQL_ROOT_PASSWORD}
    volumes:
      - db_volume:/var/lib/mysql
    networks:
      - internal
  mlflow:
    image: ubuntu/mlflow:2.1.1_1.0-22.04
    container_name: tracker_mlflow
    restart: unless-stopped
    ports:
      - "5000:5000"
    environment:
      - AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID}
      - AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY}
      - AWS_DEFAULT_REGION=${AWS_REGION}
      - MLFLOW_S3_ENDPOINT_URL=http://s3:9000
    networks:
      - public
      - internal
    entrypoint: mlflow server --backend-store-uri mysql+pymysql://${MYSQL_USER}:${MYSQL_PASSWORD}@db:3306/${MYSQL_DATABASE} --default-artifact-root s3://${AWS_BUCKET_NAME}/ --artifacts-destination s3://${AWS_BUCKET_NAME}/ -h 0.0.0.0
    depends_on:
      wait-for-db:
        condition: service_completed_successfully
  create_s3_buckets:
    image: minio/mc
    depends_on:
      - "s3"
    entrypoint: >
      /bin/sh -c "
      until (/usr/bin/mc alias set minio http://s3:9000 '${AWS_ACCESS_KEY_ID}' '${AWS_SECRET_ACCESS_KEY}') do echo '...waiting...' && sleep 1; done;
      /usr/bin/mc mb minio/${AWS_BUCKET_NAME};
      exit 0;
      "
    networks:
      - internal
  wait-for-db:
    image: atkrad/wait4x
    depends_on:
      - db
    command: tcp db:3306 -t 90s -i 250ms
    networks:
      - internal
  run_test_experiment:
    build:
      context: ./test_experiment
      dockerfile: Dockerfile
    depends_on:
      - "mlflow"
    environment:
      - AWS_ACCESS_KEY_ID=${AWS_ACCESS_KEY_ID}
      - AWS_SECRET_ACCESS_KEY=${AWS_SECRET_ACCESS_KEY}
      - AWS_DEFAULT_REGION=${AWS_REGION}
      - MLFLOW_S3_ENDPOINT_URL=http://s3:9000
      - MLFLOW_TRACKING_URI=http://mlflow:5000
    entrypoint: >
      /bin/sh -c "
      python3 mlflow_tracking.py;
      exit 0;
      "
    networks:
      - internal
networks:
  internal:
  public:
    driver: bridge
volumes:
  db_volume:
  minio_volume: