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 version: "3.8"
 
 # 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
-      - /lib/firmware/mali_csffw.bin:/lib/firmware/mali_csffw.bin:ro # Mali firmware for your chipset (not always required depending on the driver)
-      - /usr/lib/libmali.so:/usr/lib/libmali.so:ro # Mali driver for your chipset (always required)
 
   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
 
   openvino-wsl:
     devices:
       - /dev/dri:/dev/dri
       - /dev/dxg:/dev/dxg
     volumes:
       - /dev/bus/usb:/dev/bus/usb
       - /usr/lib/wsl:/usr/lib/wsl
 
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