docker-compose.override.yml_example

version: "3.9"

services:
    ### to customize the homepage, uncomment this
    #app:
      # volumes:
      #   - $PWD/app/homepage

    web: &web
      restart: always
      # deploy:
      #   mode: replicated
      #   replicas: 1
      #   placement:
      #     constraints:
      #       - node.hostname == frontend0

    channelserver:
      restart: always
      # deploy:
      #   mode: replicated
      #   replicas: 1
      #   placement:
      #     constraints:
      #       - node.hostname == frontend0

    db:
      restart: always
      # deploy:
      #   mode: replicated
      #   replicas: 1
      #   placement:
      #     constraints:
      #       - node.hostname == db0

      # For development purposes, expose db on localhost
      # ports:
      #   - 127.0.0.1:5432:5432

    redis:
      restart: always

      # For development purposes, expose redis on localhost
      # ports:
      #   - 127.0.0.1:6379:6379

    nginx:
      restart: always
      ports:
       - "80:80"
      # - "443:443"
      # deploy:
      #   mode: replicated
      #   replicas: 1
      #   placement:
      #     constraints:
      #       - node.hostname == frontend0

      ### To enable SSL, generate keys (eg with letsencrypt/certbot)
      ### copy nginx/ssl_certificates.conf_example and edit it
      ## if need be to correspond to the volume below
      ### and uncomment this block and the port 443
      # volumes:
      # - type: bind
      #   source: $PWD/nginx/ssl.conf
      #   target: /etc/nginx/conf.d/nginx.conf
      # - type: bind
      #   source: $PWD/nginx/ssl_certificates.conf
      #   target: /etc/nginx/conf.d/ssl_certificates.conf
      # - $PWD/nginx/certs/:/etc/certs/

    flower:
      restart: always
      # deploy:
      #   mode: replicated
      #   replicas: 1
      #   placement:
      #     constraints:
      #       - node.hostname == frontend0

    # cpus and mem_limit imposes a hard limit on cpus usage,
    # needed to keep some for http/db when working with a single machine
    #
    # according to the docker documentation:
    # Memory reservation is a kind of memory soft limit that allows for greater sharing of memory.
    # Under normal circumstances, containers can use as much of the memory as needed and are constrained only by the hard limits set with the -m/--memory option (mem_limit in docker-compose).
    # When memory reservation is set, Docker detects memory contention or low memory and forces containers to restrict their consumption to a reservation limit.

    # the shm_size argument is needed when using KRAKEN_TRAINING_LOAD_THREADS

    # !!!!
    # example values here are given for 16 cores and 16g of memory keeping 2 cores and 1g of memory at all time
    # for http & db assuming a low amount of concurrent users <50

    celery-main:
      restart: always
      # deploy:
      #   mode: replicated
      #   replicas: 1
      #   placement:
      #     constraints:
      #       - node.hostname == compute0
      #   resources:
      #     limits:
      #       cpus: '6'
      #       memory: 15g
      #     reservations:
      #       memory: 4g

    celery-low-priority:
      restart: always
      # deploy:
      #   mode: replicated
      #   replicas: 1
      #   placement:
      #     constraints:
      #       - node.hostname == compute1
      #   resources:
      #     limits:
      #       cpus: '2'
      #       memory: 15g
      #     reservations:
      #       memory: 1g

    celery-gpu:
      restart: always
      # deploy:
      #   mode: replicated
      #   replicas: 1
      #   placement:
      #     constraints:
      #       - node.labels.type == gpu
      #   resources:
      #     limits:
      #       cpus: '6'
      #       memory: 15g
      #     reservations:
      #       memory: 1g
      #       devices:
      #         - capabilities:
      #           - gpu

      # shm_size: '3gb'
      # runtime: nvidia
      # environment:
      #   - KRAKEN_TRAINING_DEVICE=cuda:0
      #   - NVIDIA_VISIBLE_DEVICES=all
      #   - NVIDIA_DRIVER_CAPABILITIES=all

    # add more workers for every physical gpu
    # celery-gpu2:
    #    <<: *celery-gpu
    #    environment:
    #       - KRAKEN_TRAINING_DEVICE=cuda:1

  # celerybeat:
     #   restart: always
  
  prometheus:
    image: prom/prometheus:v2.1.0
    volumes:
      - ./prometheus/:/etc/prometheus/
      - prometheus_data:/prometheus
    command:
      - '--config.file=/etc/prometheus/prometheus.yml'
      - '--storage.tsdb.path=/prometheus'
      - '--web.console.libraries=/usr/share/prometheus/console_libraries'
      - '--web.console.templates=/usr/share/prometheus/consoles'
    ports:
      - 9090:9090
  #  links:
  #    - alertmanager:alertmanager
  
  alertmanager:
    image: prom/alertmanager:v0.18.0
    deploy:
      replicas: 1
    volumes:
      - ./prometheus/alertmanager.yml:/etc/alertmanager/alertmanager.yml
      - ./prometheus/email.tmpl:/etc/alertmanager/email.tmpl
    command:
      - '--config.file=/etc/alertmanager/alertmanager.yml'
      - '--storage.path=/alertmanager'
      - '--log.level=debug'
  
  container-exporter:
    image: prom/container-exporter
    volumes:
      - /sys/fs/cgroup:/cgroup
      - /var/run/docker.sock:/var/run/docker.sock
    ports:
      - 9104:9104

#volumes:
#   static:
#    driver: local
#    driver_opts:
#      type: none
#      o: 'bind'
#      device: $PWD/static/
#   prometheus_data: {}
#
#   media:
#    driver: local
#    driver_opts:
#      type: none
#      o: 'bind'
#      device: $PWD/media/