Intel Alderlake gen12 Machine learning doesn't use GPU (intel n100)

[balgerion]

The bug

im trying to install immich with all bells and whistles on my minipc Nas with intel n100 Alderlake gen12 gpu.
Gpu i passed to container (please see scrnshots) but Machine learning is using only CPU for fun.
Installation is on TrueNas ElectricEel-24.10.1 but im using normal docker-compose (not APP from truneas - tried that and result is the same)

screenshots from inside machine learning container: (as you can see GPU is not doing much ;) )

The OS that Immich Server is running on

TrueNas ElectricEel-24.10.1

Version of Immich Server

1.123.0

Version of Immich Mobile App

1.123.0

Platform with the issue

• Server
• Web
• Mobile

Your docker-compose.yml content

name: immich
services:
  immich-server:
    container_name: immich_server
    image: ghcr.io/immich-app/immich-server:${IMMICH_VERSION:-release}
    privileged: true
    extends:
      file: hwaccel.transcoding.yml
      service: quicksync # set to one of [nvenc, quicksync, rkmpp, vaapi, vaapi-wsl] for accelerated transcoding
    volumes:
      # Do not edit the next line. If you want to change the media storage location on your system, edit the value of UPLOAD_LOCATION in the .env file
      - ${UPLOAD_LOCATION}:/usr/src/app/upload
      - ${THUMB_LOCATION}:/usr/src/app/upload/thumbs
      - ${ENCODED_VIDEO_LOCATION}:/usr/src/app/upload/encoded-video
      - ${PROFILE_LOCATION}:/usr/src/app/upload/profile
      - ${BACKUP_LOCATION}:/usr/src/app/upload/backups
      - /etc/localtime:/etc/localtime:ro
    env_file:
      - .env
    ports:
      - '2283:2283'
    depends_on:
      - redis
      - database
    restart: always
    healthcheck:
      disable: false

  immich-machine-learning:
    container_name: immich_machine_learning
    # For hardware acceleration, add one of -[armnn, cuda, openvino] to the image tag.
    # Example tag: ${IMMICH_VERSION:-release}-cuda
    image: ghcr.io/immich-app/immich-machine-learning:${IMMICH_VERSION:-release}
    privileged: true
    extends: # uncomment this section for hardware acceleration - see https://immich.app/docs/features/ml-hardware-acceleration
      file: hwaccel.ml.yml
      service: openvino # set to one of [armnn, cuda, openvino, openvino-wsl] for accelerated inference - use the `-w
    volumes:
      - model-cache:/cache
    env_file:
      - .env
    restart: always
    healthcheck:
      disable: false

  redis:
    container_name: immich_redis
    image: docker.io/redis:6.2-alpine@sha256:eaba718fecd1196d88533de7ba49bf903ad33664a92debb24660a922ecd9cac8
    healthcheck:
      test: redis-cli ping || exit 1
    restart: always

  database:
    container_name: immich_postgres
    image: docker.io/tensorchord/pgvecto-rs:pg14-v0.2.0@sha256:90724186f0a3517cf6914295b5ab410db9ce23190a2d9d0b9dd6463e3fa298f0
    environment:
      POSTGRES_PASSWORD: ${DB_PASSWORD}
      POSTGRES_USER: ${DB_USERNAME}
      POSTGRES_DB: ${DB_DATABASE_NAME}
      POSTGRES_INITDB_ARGS: '--data-checksums'
    volumes:
      # Do not edit the next line. If you want to change the database storage location on your system, edit the value of DB_DATA_LOCATION in the .env file
      - ${DB_DATA_LOCATION}:/var/lib/postgresql/data
    healthcheck:
      test: >-
        pg_isready --dbname="$${POSTGRES_DB}" --username="$${POSTGRES_USER}" || exit 1;
        Chksum="$$(psql --dbname="$${POSTGRES_DB}" --username="$${POSTGRES_USER}" --tuples-only --no-align
        --command='SELECT COALESCE(SUM(checksum_failures), 0) FROM pg_stat_database')";
        echo "checksum failure count is $$Chksum";
        [ "$$Chksum" = '0' ] || exit 1
      interval: 5m
      start_interval: 30s
      start_period: 5m
    command: >-
      postgres
      -c shared_preload_libraries=vectors.so
      -c 'search_path="$$user", public, vectors'
      -c logging_collector=on
      -c max_wal_size=2GB
      -c shared_buffers=512MB
      -c wal_compression=on
    restart: always

volumes:
  model-cache:

Your .env content

DB_DATA_LOCATION=/mnt/Balgeriada1/Photos/db
UPLOAD_LOCATION=/mnt/Balgeriada1/Photos/Uploads
THUMB_LOCATION=/mnt/Balgeriada1/Photos/Thumbs
ENCODED_VIDEO_LOCATION=/mnt/Balgeriada1/Photos/Video
PROFILE_LOCATION=/mnt/Balgeriada1/Photos/Profile
BACKUP_LOCATION=/mnt/Balgeriada1/Photos/Backup
TZ=Europe/Warsaw
IMMICH_VERSION=release
DB_PASSWORD=dupa
DB_USERNAME=postgres
DB_DATABASE_NAME=immich


----------------------------------
# Configurations for hardware-accelerated transcoding

# 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-microservices service in the docker-compose.yml file.

# See https://immich.app/docs/features/hardware-transcoding for more info on using hardware transcoding.

services:
  cpu: {}

  nvenc:
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities:
                - gpu
                - compute
                - video

  quicksync:
    devices:
      - /dev/dri:/dev/dri

  rkmpp:
    security_opt: # enables full access to /sys and /proc, still far better than privileged: true
      - systempaths=unconfined
      - apparmor=unconfined
    group_add:
      - video
    devices:
      - /dev/rga:/dev/rga
      - /dev/dri:/dev/dri
      - /dev/dma_heap:/dev/dma_heap
      - /dev/mpp_service:/dev/mpp_service
      #- /dev/mali0:/dev/mali0 # only required to enable OpenCL-accelerated HDR -> SDR tonemapping
    volumes:
      #- /etc/OpenCL:/etc/OpenCL:ro # only required to enable OpenCL-accelerated HDR -> SDR tonemapping
      #- /usr/lib/aarch64-linux-gnu/libmali.so.1:/usr/lib/aarch64-linux-gnu/libmali.so.1:ro # only required to enable OpenCL-accelerated HDR -> SDR tonemapping

  vaapi:
    devices:
      - /dev/dri:/dev/dri

  vaapi-wsl: # use this for VAAPI if you're running Immich in WSL2
    devices:
      - /dev/dri:/dev/dri
    volumes:
      - /usr/lib/wsl:/usr/lib/wsl
    environment:
      - LIBVA_DRIVER_NAME=d3d12


and both hwacell files: 
-----------------------------
# 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 # 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

Reproduction steps

1. normal installation - step by step instruction from site

...

Relevant log output

No response

Additional information

No response

[mertalev]

You need to add -openvino to the end of the image field, see the example comment above it.

[mertalev]

Are other jobs besides smart search or face detection running? It's normal for CPU usage to be very high during the initial import even on high-end machines. If you haven't already, you can enable hardware acceleration for transcoding to lower CPU usage further.

[balgerion]

it’s transcoding video without hardware acceleration - n100 have quick sync support. I have another config error somewhere ?

[mertalev]

You need to enable it in the transcoding settings (also enable the hardware decoding option below it).

[balgerion]

its working! thank you for your time.

[ghost]

its working! thank you for your time.

Hey, just a quick question. How well are the AI features working on the N100? I’m currently using an RTX 3070 Ti, but my power consumption is way too high. Do you have any stats, like how long it takes to process searches, etc.?