docker-compose.yml

version: "3.3"
services:
  # sometimes when building the controller it does not copy the contents of the datasets folder.
  # This is probably a caching docker caching issue, which can be fixed with:
  # 'make clean-hard'
  controller:
    build:
      context: ./
      dockerfile: ./controller/Dockerfile
    container_name: controller
    depends_on:
      - mongo
    # we map the port of the controller to the host.
    # This way we can either connect locally with localhost:5001
    # or from a docker container that accesses host.docker.internal:5001
    ports:
      - "5001:5001"
    environment:
      # The environment variables are set manually here in docker-compose.
      # But in kubernetes the environment variables are set as <SERVICE_NAME>_SERVICE_HOST and <SERVICE_NAME>_SERVICE_PORT
      # Therefore the services must be named accordingly in the kubernetes files.
      - AUTOKERAS_SERVICE_HOST=autokeras
      - AUTOKERAS_SERVICE_PORT=50052
      - MLJAR_SERVICE_HOST=mljar
      - MLJAR_SERVICE_PORT=50053
      #- MCFLY_SERVICE_HOST=mcfly
      #- MCFLY_SERVICE_PORT=50054
      #- SKLEARN_SERVICE_HOST=sklearn
      #- SKLEARN_SERVICE_PORT=50055
      - FLAML_SERVICE_HOST=flaml
      - FLAML_SERVICE_PORT=50056
      - AUTOGLUON_SERVICE_HOST=gluon
      - AUTOGLUON_SERVICE_PORT=50057
      #- AUTOCVE_SERVICE_HOST=autocve
      #- AUTOCVE_SERVICE_PORT=50058
      - PYTORCH_SERVICE_HOST=pytorch
      - PYTORCH_SERVICE_PORT=50059
      #- ALPHAD3M_SERVICE_HOST=alphad3m
      #- ALPHAD3M_SERVICE_PORT=50060
      - EVALML_SERVICE_HOST=evalml
      - EVALML_SERVICE_PORT=50062
      - PYCARET_SERVICE_HOST=pycaret
      - PYCARET_SERVICE_PORT=50063
      - TPOT_SERVICE_HOST=tpot
      - TPOT_SERVICE_PORT=50064
      - GAMA_SERVICE_HOST=gama
      - GAMA_SERVICE_PORT=50065
      - LAMA_SERVICE_HOST=lama
      - LAMA_SERVICE_PORT=50066
      - H2O_SERVICE_HOST=h2o
      - H2O_SERVICE_PORT=50067
      - PERSISTENCE_LOGGING_LEVEL=DEBUG
      - SERVER_LOGGING_LEVEL=DEBUG
      - ONTOLOGY_LOGGING_LEVEL=DEBUG
      - BLACKBOARD_LOGGING_LEVEL=DEBUG
      - WIN_DEV_MACHINE=NO
      - KUBERNETES_CLUSTER=NO
    volumes:
      - datasets:/app/app-data/datasets
      - training:/app/app-data/training
      - training-autokeras:/app/app-data/training/autokeras
      - training-mljar:/app/app-data/training/mljar
      #- training-sklearn:/app/app-data/training/sklearn
      - training-flaml:/app/app-data/training/flaml
      - training-gluon:/app/app-data/training/gluon
      #- training-autocve:/app/app-data/training/autocve
      #- training-pytorch:/app/app-data/training/pytorch
      #- training-alphad3m:/app/app-data/training/alphad3m
      #- training-mcfly:/app/app-data/training/mcfly
      - training-evalml:/app/app-data/training/evalml
      - training-pycaret:/app/app-data/training/pycaret
      - training-tpot:/app/app-data/training/tpot
      - training-gama:/app/app-data/training/gama
      - training-lama:/app/app-data/training/lama

  mongo:
    image: mongo
    container_name: mongo
    ports:
      - "27017:27017"

    # limit log verbosity
    command: --quiet --logpath /dev/null

    environment:
      MONGO_INITDB_ROOT_USERNAME: root
      MONGO_INITDB_ROOT_PASSWORD: example
    volumes:
      - mongodata:/data/configdb
      - mongodata:/data/db

#     adapters do not need to be available from the host machine -> no port mappings

  h2o:
      build:
          context: ./
          dockerfile: ./adapters/H2O/Dockerfile
      container_name: h2o
      environment:
          - GRPC_SERVER_PORT=50067
          - EXPLAINER_DASHBOARD_PORT_START=6110
          - EXPLAINER_DASHBOARD_PORT_END=6119
          - DOCKER_COMPOSE=YES
          - DOCKER_OR_DEBUG_XAI_DASHBOARD_IP=172.17.208.1
          - ADAPTER_NAME=h2o
          - JOB_FILE_NAME=h2o-job.json
          - EXPORT_ZIP_FILE_NAME=h2o-export
          - GRPC_SERVER_ADDRESS=0.0.0.0
          - TRAINING_PATH=app-data/training
          - JOB_FOLDER_NAME=job
          - MODEL_FOLDER_NAME=model
          - EXPORT_FOLDER_NAME=export
          - RESULT_FOLDER_NAME=result
          - TEMPLATES_PATH=app-data/templates
          - LOCAL_EXECUTION=NO
          - RUNNING_IN_WSL=NO
          - DASHBOARD_FOLDER_NAME=dashboard
          - WSL_METAAUTOML_PATH=/mnt/c/Users/alex/Desktop/MetaAutoML
          - ONTOLOGY_PATH=ML_Ontology.ttl
      volumes:
          - datasets:/app/app-data/datasets
          - training-h2o:/app/app-data/training
      ports:
          - "6110-6119:6110-6119"

  autokeras:
    build:
      context: ./
      dockerfile: ./adapters/AutoKeras/Dockerfile
    container_name: autokeras
    depends_on:
      - mljar
    environment:
        - GRPC_SERVER_PORT=50052
        - EXPLAINER_DASHBOARD_PORT_START=6000
        - EXPLAINER_DASHBOARD_PORT_END=6009
        - DOCKER_COMPOSE=YES
        - DOCKER_OR_DEBUG_XAI_DASHBOARD_IP=172.17.208.1
        - ADAPTER_NAME=autokeras
        - JOB_FILE_NAME=autokeras-job.json
        - EXPORT_ZIP_FILE_NAME=autokeras-export
        - GRPC_SERVER_ADDRESS=0.0.0.0
        - TRAINING_PATH=app-data/training
        - JOB_FOLDER_NAME=job
        - MODEL_FOLDER_NAME=model
        - EXPORT_FOLDER_NAME=export
        - RESULT_FOLDER_NAME=result
        - TEMPLATES_PATH=app-data/templates
        - LOCAL_EXECUTION=NO
        - RUNNING_IN_WSL=NO
        - DASHBOARD_FOLDER_NAME=dashboard
        - WSL_METAAUTOML_PATH=/mnt/c/Users/alex/Desktop/MetaAutoML
        - ONTOLOGY_PATH=ML_Ontology.ttl
    volumes:
      - datasets:/app/app-data/datasets
      - training-autokeras:/app/app-data/training
    ports:
      - "6000-6009:6000-6009"

  mljar:
    build:
      context: ./
      dockerfile: ./adapters/MLJAR/Dockerfile
    container_name: mljar
    depends_on:
      - flaml
    environment:
        - GRPC_SERVER_PORT=50053
        - EXPLAINER_DASHBOARD_PORT_START=6010
        - EXPLAINER_DASHBOARD_PORT_END=6019
        - DOCKER_COMPOSE=YES
        - DOCKER_OR_DEBUG_XAI_DASHBOARD_IP=172.17.208.1
        - ADAPTER_NAME=mljar
        - JOB_FILE_NAME=mljar-job.json
        - EXPORT_ZIP_FILE_NAME=mljar-export
        - GRPC_SERVER_ADDRESS=0.0.0.0
        - TRAINING_PATH=app-data/training
        - JOB_FOLDER_NAME=job
        - MODEL_FOLDER_NAME=model
        - EXPORT_FOLDER_NAME=export
        - RESULT_FOLDER_NAME=result
        - TEMPLATES_PATH=app-data/templates
        - LOCAL_EXECUTION=NO
        - RUNNING_IN_WSL=NO
        - DASHBOARD_FOLDER_NAME=dashboard
        - WSL_METAAUTOML_PATH=/mnt/c/Users/alex/Desktop/MetaAutoML
        - ONTOLOGY_PATH=ML_Ontology.ttl
    volumes:
      - datasets:/app/app-data/datasets
      - training-mljar:/app/app-data/training
    ports:
      - "6010-6019:6010-6019"

# #   mcfly:
# #     build:
# #       context: ./
# #       dockerfile: ./adapters/Mcfly/Dockerfile
# #     container_name: mcfly
# #     environment:
# #       - GRPC_SERVER_PORT=50054
# #     volumes:
# #       - datasets:/app/app-data/datasets
# #       - training-mcfly:/app/app-data/training

# #   sklearn:
# #     build:
# #       context: ./
# #       dockerfile: ./adapters/AutoSklearn/Dockerfile
# #     container_name: sklearn
# #     environment:
# #       - GRPC_SERVER_PORT=50055
# #       - EXPLAINER_DASHBOARD_PORT_START=6020
# #       - EXPLAINER_DASHBOARD_PORT_END=6029
# #     volumes:
# #       - datasets:/app/app-data/datasets
# #       - training-sklearn:/app/app-data/training
# #     ports:
# #       - "6020-6029:6020-6029"

  flaml:
    build:
      context: ./
      dockerfile: ./adapters/FLAML/Dockerfile
    container_name: flaml
    depends_on:
      - gluon
    environment:
        - GRPC_SERVER_PORT=50056
        - EXPLAINER_DASHBOARD_PORT_START=6030
        - EXPLAINER_DASHBOARD_PORT_END=6039
        - DOCKER_COMPOSE=YES
        - DOCKER_OR_DEBUG_XAI_DASHBOARD_IP=172.17.208.1
        - ADAPTER_NAME=flaml
        - JOB_FILE_NAME=flaml-job.json
        - EXPORT_ZIP_FILE_NAME=flaml-export
        - GRPC_SERVER_ADDRESS=0.0.0.0
        - TRAINING_PATH=app-data/training
        - JOB_FOLDER_NAME=job
        - MODEL_FOLDER_NAME=model
        - EXPORT_FOLDER_NAME=export
        - RESULT_FOLDER_NAME=result
        - TEMPLATES_PATH=app-data/templates
        - LOCAL_EXECUTION=NO
        - RUNNING_IN_WSL=NO
        - DASHBOARD_FOLDER_NAME=dashboard
        - WSL_METAAUTOML_PATH=/mnt/c/Users/alex/Desktop/MetaAutoML
        - ONTOLOGY_PATH=ML_Ontology.ttl
    volumes:
      - datasets:/app/app-data/datasets
      - training-flaml:/app/app-data/training
    ports:
      - "6030-6039:6030-6039"

  gluon:
    build:
      context: ./
      dockerfile: ./adapters/AutoGluon/Dockerfile
    container_name: gluon
    depends_on:
      - evalml
    environment:
        - GRPC_SERVER_PORT=50057
        - EXPLAINER_DASHBOARD_PORT_START=6040
        - EXPLAINER_DASHBOARD_PORT_END=6049
        - DOCKER_COMPOSE=YES
        - DOCKER_OR_DEBUG_XAI_DASHBOARD_IP=172.17.208.1
        - ADAPTER_NAME=autogluon
        - JOB_FILE_NAME=autogluon-job.json
        - EXPORT_ZIP_FILE_NAME=autogluon-export
        - GRPC_SERVER_ADDRESS=0.0.0.0
        - TRAINING_PATH=app-data/training
        - JOB_FOLDER_NAME=job
        - MODEL_FOLDER_NAME=model
        - EXPORT_FOLDER_NAME=export
        - RESULT_FOLDER_NAME=result
        - TEMPLATES_PATH=app-data/templates
        - LOCAL_EXECUTION=NO
        - RUNNING_IN_WSL=NO
        - DASHBOARD_FOLDER_NAME=dashboard
        - WSL_METAAUTOML_PATH=/mnt/c/Users/alex/Desktop/MetaAutoML
        - ONTOLOGY_PATH=ML_Ontology.ttl
    volumes:
      - datasets:/app/app-data/datasets
      - training-gluon:/app/app-data/training
    ports:
      - "6040-6049:6040-6049"

# #   #autocve:
# #   #  build:
# #   #    context: ./
# #   #    dockerfile: ./adapters/AutoCVE/Dockerfile
# #   #  container_name: autocve
# #   #  environment:
# #   #    - GRPC_SERVER_PORT=50058
# #   #  volumes:
# #   #    - datasets:/app/app-data/datasets
# #   #    - training-autocve:/app/app-data/training

# #   pytorch:
# #     build:
# #       context: ./
# #       dockerfile: ./adapters/AutoPytorch/Dockerfile
# #     container_name: pytorch
# #     environment:
# #       - GRPC_SERVER_PORT=50059
# #       - EXPLAINER_DASHBOARD_PORT_START=6050
# #       - EXPLAINER_DASHBOARD_PORT_END=6059
# #     volumes:
# #       - datasets:/app/app-data/datasets
# #       - training-pytorch:/app/app-data/training
# #     ports:
# #       - "6050-6059:6050-6059"

# #   #alphad3m:
# #   #  build:
# #   #    context: ./
# #   #    dockerfile: ./adapters/AlphaD3M/Dockerfile
# #   #  container_name: alphad3m
# #   #  environment:
# #   #    - GRPC_SERVER_PORT=50060
# #   #  volumes:
# #   #    - datasets:/app/app-data/datasets
# #   #    - training-alphad3m:/app/app-data/training


  evalml:
    build:
      context: ./
      dockerfile: ./adapters/EvalML/Dockerfile
    container_name: evalml
    depends_on:
      - pycaret
    environment:
        - GRPC_SERVER_PORT=50062
        - EXPLAINER_DASHBOARD_PORT_START=6060
        - EXPLAINER_DASHBOARD_PORT_END=6069
        - DOCKER_COMPOSE=YES
        - DOCKER_OR_DEBUG_XAI_DASHBOARD_IP=172.17.208.1
        - ADAPTER_NAME=evalml
        - JOB_FILE_NAME=evalml-job.json
        - EXPORT_ZIP_FILE_NAME=evalml-export
        - GRPC_SERVER_ADDRESS=0.0.0.0
        - TRAINING_PATH=app-data/training
        - JOB_FOLDER_NAME=job
        - MODEL_FOLDER_NAME=model
        - EXPORT_FOLDER_NAME=export
        - RESULT_FOLDER_NAME=result
        - TEMPLATES_PATH=app-data/templates
        - LOCAL_EXECUTION=NO
        - RUNNING_IN_WSL=NO
        - DASHBOARD_FOLDER_NAME=dashboard
        - WSL_METAAUTOML_PATH=/mnt/c/Users/alex/Desktop/MetaAutoML
        - ONTOLOGY_PATH=ML_Ontology.ttl
    volumes:
      - datasets:/app/app-data/datasets
      - training-evalml:/app/app-data/training
    ports:
      - "6060-6069:6060-6069"

  pycaret:
    build:
      context: ./
      dockerfile: ./adapters/PyCaret/Dockerfile
    container_name: pycaret
    depends_on:
      - tpot
    environment:
        - GRPC_SERVER_PORT=50063
        - EXPLAINER_DASHBOARD_PORT_START=6070
        - EXPLAINER_DASHBOARD_PORT_END=6079
        - DOCKER_COMPOSE=YES
        - DOCKER_OR_DEBUG_XAI_DASHBOARD_IP=172.17.208.1
        - ADAPTER_NAME=pycaret
        - JOB_FILE_NAME=pycaret-job.json
        - EXPORT_ZIP_FILE_NAME=pycaret-export
        - GRPC_SERVER_ADDRESS=0.0.0.0
        - TRAINING_PATH=app-data/training
        - JOB_FOLDER_NAME=job
        - MODEL_FOLDER_NAME=model
        - EXPORT_FOLDER_NAME=export
        - RESULT_FOLDER_NAME=result
        - TEMPLATES_PATH=app-data/templates
        - LOCAL_EXECUTION=NO
        - RUNNING_IN_WSL=NO
        - DASHBOARD_FOLDER_NAME=dashboard
        - WSL_METAAUTOML_PATH=/mnt/c/Users/alex/Desktop/MetaAutoML
        - ONTOLOGY_PATH=ML_Ontology.ttl
    volumes:
      - datasets:/app/app-data/datasets
      - training-pycaret:/app/app-data/training
    ports:
      - "6070-6079:6070-6079"

  tpot:
    build:
      context: ./
      dockerfile: ./adapters/TPOT/Dockerfile
    container_name: tpot
    depends_on:
      - gama
    environment:
        - GRPC_SERVER_PORT=50064
        - EXPLAINER_DASHBOARD_PORT_START=6080
        - EXPLAINER_DASHBOARD_PORT_END=6089
        - DOCKER_COMPOSE=YES
        - DOCKER_OR_DEBUG_XAI_DASHBOARD_IP=172.17.208.1
        - ADAPTER_NAME=tpot
        - JOB_FILE_NAME=tpot-job.json
        - EXPORT_ZIP_FILE_NAME=tpot-export
        - GRPC_SERVER_ADDRESS=0.0.0.0
        - TRAINING_PATH=app-data/training
        - JOB_FOLDER_NAME=job
        - MODEL_FOLDER_NAME=model
        - EXPORT_FOLDER_NAME=export
        - RESULT_FOLDER_NAME=result
        - TEMPLATES_PATH=app-data/templates
        - LOCAL_EXECUTION=NO
        - RUNNING_IN_WSL=NO
        - DASHBOARD_FOLDER_NAME=dashboard
        - WSL_METAAUTOML_PATH=/mnt/c/Users/alex/Desktop/MetaAutoML
        - ONTOLOGY_PATH=ML_Ontology.ttl
    volumes:
      - datasets:/app/app-data/datasets
      - training-tpot:/app/app-data/training
    ports:
      - "6080-6089:6080-6089"

  gama:
    build:
      context: ./
      dockerfile: ./adapters/GAMA/Dockerfile
    container_name: gama
    depends_on:
      - lama
    environment:
        - GRPC_SERVER_PORT=50065
        - EXPLAINER_DASHBOARD_PORT_START=6090
        - EXPLAINER_DASHBOARD_PORT_END=6099
        - DOCKER_COMPOSE=YES
        - DOCKER_OR_DEBUG_XAI_DASHBOARD_IP=172.17.208.1
        - ADAPTER_NAME=gama
        - JOB_FILE_NAME=gama-job.json
        - EXPORT_ZIP_FILE_NAME=gama-export
        - GRPC_SERVER_ADDRESS=0.0.0.0
        - TRAINING_PATH=app-data/training
        - JOB_FOLDER_NAME=job
        - MODEL_FOLDER_NAME=model
        - EXPORT_FOLDER_NAME=export
        - RESULT_FOLDER_NAME=result
        - TEMPLATES_PATH=app-data/templates
        - LOCAL_EXECUTION=NO
        - RUNNING_IN_WSL=NO
        - DASHBOARD_FOLDER_NAME=dashboard
        - WSL_METAAUTOML_PATH=/mnt/c/Users/alex/Desktop/MetaAutoML
        - ONTOLOGY_PATH=ML_Ontology.ttl
    volumes:
      - datasets:/app/app-data/datasets
      - training-gama:/app/app-data/training
    ports:
      - "6090-6099:6090-6099"

  lama:
    build:
      context: ./
      dockerfile: ./adapters/LAMA/Dockerfile
    container_name: lama
    environment:
        - GRPC_SERVER_PORT=50066
        - EXPLAINER_DASHBOARD_PORT_START=6100
        - EXPLAINER_DASHBOARD_PORT_END=6109
        - DOCKER_COMPOSE=YES
        - DOCKER_OR_DEBUG_XAI_DASHBOARD_IP=172.17.208.1
        - ADAPTER_NAME=lama
        - JOB_FILE_NAME=lama-job.json
        - EXPORT_ZIP_FILE_NAME=lama-export
        - GRPC_SERVER_ADDRESS=0.0.0.0
        - TRAINING_PATH=app-data/training
        - JOB_FOLDER_NAME=job
        - MODEL_FOLDER_NAME=model
        - EXPORT_FOLDER_NAME=export
        - RESULT_FOLDER_NAME=result
        - TEMPLATES_PATH=app-data/templates
        - LOCAL_EXECUTION=NO
        - RUNNING_IN_WSL=NO
        - DASHBOARD_FOLDER_NAME=dashboard
        - WSL_METAAUTOML_PATH=/mnt/c/Users/alex/Desktop/MetaAutoML
        - ONTOLOGY_PATH=ML_Ontology.ttl
    volumes:
      - datasets:/app/app-data/datasets
      - training-lama:/app/app-data/training
    ports:
      - "6100-6109:6100-6109"

volumes:
  datasets: # shared volume between controller, adapters and frontend/dummy to transfer datasets
   # shared volumes between controller and adapters to transfer training files e.g. *.zip file
  training-autokeras:
  #training-sklearn:
  training-flaml:
  training-gluon:
  training-h2o:
  #training-pytorch:
  #training-autocve:
  training-mljar:
  #training-alphad3m:
  #training-mcfly:
  training-evalml:
  training-pycaret:
  training-tpot:
  training-gama:
  training-lama:

  # shared volume between controller and frontend / dummy to transfer training
  training:
  mongodata: