# Configurations for hardware-accelerated machine learning

# If using Unraid or another platform that doesn't allow multiple Compose files,
# you can inline the config for a backend by copying its contents
# into the immich-machine-learning service in the docker-compose.yml file.

# See https://immich.app/docs/features/ml-hardware-acceleration for info on usage.

services:
#  armnn:
#    devices:
#      - /dev/mali0:/dev/mali0
#    volumes:
#      - /lib/firmware/mali_csffw.bin:/lib/firmware/mali_csffw.bin:ro
#      - /usr/lib/libmali.so:/usr/lib/libmali.so:ro

#  cpu: {}

#  cuda:
#    deploy:
#      resources:
#        reservations:
#          devices:
#            - driver: nvidia
#              count: 1
#              capabilities:
#                - gpu

  openvino:
#    device_cgroup_rules:
#      - 'c 189:* rmw'
#    devices:
#      - /dev/dri:/dev/dri
    volumes:
      - /dev/bus/usb:/dev/bus/usb
      - /dev/dri:/dev/dri
    hostname: immich-ml
    networks:
      - traefik_backend

#  openvino-wsl:
#    devices:
#      - /dev/dri:/dev/dri
#      - /dev/dxg:/dev/dxg
#    volumes:
#      - /dev/bus/usb:/dev/bus/usb
#      - /usr/lib/wsl:/usr/lib/wsl

networks:
  postgresql_db-backend:
    external: true
  traefik_backend:
    external: true