docker-compose.yaml

version: "3.7"

services:

  rstudio:
    image: "ghcr.io/${CONTAINER_REG_USER}/${CONTAINER_TIDYMODELS_REG_NAME}:latest"
    container_name: "${PROJECT_NAME}_rstudio"
    hostname: rstudio
    stop_grace_period: 60s
    restart: always
    environment:
      USER: $R_STUDIO_USER
      PASSWORD: $R_STUDIO_PASSWORD
      ROOT: "TRUE"
      DISABLE_AUTH: "TRUE"
      R_SEED: $R_SEED
      MLFLOW_TRACKING_URI: $MLFLOW_TRACKING_URI
      MLFLOW_S3_ENDPOINT_URL: http://minio:9000
      AWS_ACCESS_KEY_ID: $AWS_ACCESS_KEY_ID
      AWS_SECRET_ACCESS_KEY: $AWS_SECRET_ACCESS_KEY
    ports:
      - ${R_STUDIO_PORT}:8787 
    ulimits:
      memlock:
        soft: -1
        hard: -1
    volumes:
      - ./tutorials:/home/user

  minio:
    image: "minio/minio:${MINIO_TAG}"
    container_name: "${PROJECT_NAME}_minio"
    hostname: minio
    working_dir: "/minio/storage"
    volumes:
        - ./data/minio:/minio/storage
    ports:
      - "${MINIO_PORT}:9000"
    environment:
      MINIO_ACCESS_KEY: $MINIO_ACCESS_KEY
      MINIO_SECRET_KEY: $MINIO_SECRET_KEY
    command: server /minio/storage
        
  mlflow:
    image: "ghcr.io/${CONTAINER_REG_USER}/${CONTAINER_MLFOW_REG_NAME}:latest"
    container_name: "${PROJECT_NAME}_mlflow"
    hostname: mlflow
    restart: always
    stop_grace_period: 60s
    environment:
      MLFLOW_LABEL: $MLFLOW_LABEL
      MLFLOW_BACKEND_STORE_URI: $MLFLOW_BACKEND_STORE_URI
      MLFLOW_S3_ENDPOINT_URL: http://minio:9000
      AWS_ACCESS_KEY_ID: $AWS_ACCESS_KEY_ID
      AWS_SECRET_ACCESS_KEY: $AWS_SECRET_ACCESS_KEY
    ports:
      - ${MLFLOW_PORT}:5000
    command: >
      mlflow server --serve-artifacts --host 0.0.0.0 --port 5000 --backend-store-uri "${MLFLOW_BACKEND_STORE_URI}" --default-artifact-root s3://mlflow/

  postgres:
    image: "postgres:${POSTGRES_TAG}"
    container_name: "${PROJECT_NAME}_postgres"
    hostname: postgres
    restart: always
    stop_grace_period: 60s
    environment:
      POSTGRES_PASSWORD: $DB_PASSWORD
      POSTGRES_DB: $DB_NAME
      POSTGRES_USER: $DB_USER
    ports:
      - ${DB_PORT}:5432
    volumes:
      - ./data/postgres:/var/lib/postgresql/data

  grafana:
    image: "grafana/grafana:${GRAFANA_TAG}"
    container_name: "${PROJECT_NAME}_grafana"
    user: "0"
    hostname: grafana
    restart: always
    stop_grace_period: 60s
    ports:
      - "${GRAFANA_PORT}:3000"
    environment:
      GF_INSTALL_PLUGINS: $GF_INSTALL_PLUGINS
    volumes:
      - ./data/grafana:/var/lib/grafana
      - ./services/grafana/grafana.ini:/etc/grafana/grafana.ini

  adminer:
    image: "adminer:${ADMINER_TAG}"
    container_name: "${PROJECT_NAME}_adminer"
    stop_grace_period: 60s
    restart: always
    ports:
      - ${ADMINER_PORT}:8080

  # zookeeper:
  #   image: "confluentinc/cp-zookeeper:${ZOOKEEPER_TAG}"
  #   container_name: "${PROJECT_NAME}_zookeeper"
  #   hostname: zookeeper
  #   stop_grace_period: 60s
  #   environment:
  #     ZOOKEEPER_CLIENT_PORT: $ZOOKEEPER_CLIENT_PORT
  #   ports:
  #     - "${ZOOKEEPER_PORT}:32181"

  # kafka:
  #   image: "confluentinc/cp-kafka:${KAFKA_TAG}"
  #   container_name: "${PROJECT_NAME}_kafka"
  #   hostname: kafka
  #   stop_grace_period: 60s
  #   environment:
  #     KAFKA_BROKER_ID: 1
  #     KAFKA_ZOOKEEPER_CONNECT: zookeeper:32181
  #     KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://localhost:9092
  #     KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1
  #     KAFKA_AUTO_CREATE_TOPICS_ENABLE: true
  #   ports:
  #     - "${KAFKA_PORT}:9092"
  #   restart: always
  #   depends_on:
  #     - zookeeper

  rstudio_mlflow_serve_lm:
    image: "ghcr.io/${CONTAINER_REG_USER}/${CONTAINER_TIDYMODELS_REG_NAME}:latest"
    container_name: "${PROJECT_NAME}_rstudio_mlflow_serve_lm"
    hostname: rstudio_mlflow_serve_lm
    stop_grace_period: 60s
    restart: always
    environment:
      USER: $R_STUDIO_USER
      PASSWORD: $R_STUDIO_PASSWORD
      ROOT: "TRUE"
      DISABLE_AUTH: "TRUE"
      R_SEED: $R_SEED
      MLFLOW_TRACKING_URI: http://mlflow:5000
      MLFLOW_S3_ENDPOINT_URL: http://minio:9000
      AWS_ACCESS_KEY_ID: $AWS_ACCESS_KEY_ID
      AWS_SECRET_ACCESS_KEY: $AWS_SECRET_ACCESS_KEY
    ports:
      - ${R_STUDIO_MLFLOW_SERVE_PORT}:9000 
    ulimits:
      memlock:
        soft: -1
        hard: -1
    volumes:
      - ./tutorials:/home/user
    command: conda run -n r-mlflow-1.30.0 mlflow models serve -m "models:/sw_lm/1" -h 0.0.0.0 -p 9000 

networks:
  default:
      name: docker-r-mlops