docker-compose.yml

version: '2.3'

services:
  neo4j:
    image: neo4j:3.1
    # ports:
    #  - 6006:6006
    #  - 7474:7474
    #  - 7687:7687
    volumes:
      - ~/neo4j/data:/data
    networks:
      - default
  smtag:
    image: embo/py-smtag:tldev
    # build: .


    ################################################################################################
    # https://stackoverflow.com/a/52063921
    # https://devblogs.nvidia.com/gpu-containers-runtime/
    # Needed to make use of the NVIDIA docker feautres
    #
    runtime: nvidia

    ################################################################################################
    # https://docs.nvidia.com/deeplearning/frameworks/user-guide/index.html#setincshmem
    # Certain applications, such as PyTorch use shared memory buffers to communicate between
    # processes. By default, Docker containers are allotted 64MB of shared memory. This can be
    # insufficient, particularly when using all 8 GPUs. To increase the shared memory limit to a
    # specified size, for example 1GB, include the --shm-size=1g flag in your docker run command.
    #
    # Alternatively, you can specify the --ipc=host flag to re-use the host’s shared memory space
    # inside the container. Though this latter approach has security implications as any data in
    # shared memory buffers could be visible to other containers.
    ipc: host

    ################################################################################################
    # Define the working directory of the docker container. This is like defining
    # from which directory do you want your commands to be executed
    #
    working_dir: /workspace/py-smtag
    volumes:
      - .:/workspace/py-smtag
      #- ./resources:/workspace/resources
    env_file:
      - .env
    #environment:
    #- SMTAG_WORKING_DIRECTORY=/workspace/resources
    networks:
      - default

#networks:
#  default:
#    driver: bridge