docker-compose.example.yaml

version: "3"
services:
  notebook: # Notebook
    build:
      image: swiri021/openkbc_msproject:notebookcontainer1
    volumes:
      - your_library_path:/home/jovyan/work/notebook_lib # Anything you want to import, but you can set up our codes from the github
      - your_utils_path:/home/jovyan/work/notebook_utils
      - your_archive_path:/home/jovyan/work/notebook_archive
      - your_resultFile_path:/home/jovyan/work/resultFiles
      - s3data_path_in_your_local:/home/jovyan/MainData # S3 data from our bucket
    ports:
      - 8888:8888
    container_name: notebookContainer

  pipelines: # Pipelines
    build:
      image: swiri021/openkbc_msproject:pipelinecontainer1
    deploy:
      resources:
        limits:
          memory: 4000m
    volumes:
      - pipelines_code_path:/pipelines # This codes from our github(pipeline folder)
      - s3data_path_in_your_local:/MainData
      - your_resultFile_path:/Output # Directly connected to notebook
    ports:
      - 80:5000
    depends_on:
      - redis
    container_name: pipelineContainer
    working_dir: /pipelines/pipeline_controller
    command: conda run -n pipeline_controller_base gunicorn --bind 0.0.0.0:5000 --workers 2 --threads 4 --worker-class gthread connector:app

  redis: # redis
    image: redis:alpine
    command: redis-server
    ports:
      - 6379:6379
    container_name: redisServer

  celery: # celery
    build:
      image: swiri021/openkbc_msproject:celerycontainer1
    volumes: # Celery volume path should be the same with pipeline volume
      - pipelines_code_path:/pipelines
      - s3data_path_in_your_local:/MainData
      - your_resultFile_path:/Output
    working_dir: /pipelines/pipeline_controller/
    command: conda run -n pipeline_controller_base celery -A app.celery worker --loglevel=info
    depends_on:
      - redis
      - pipelines
    container_name: celeryContainer