immich搭建日记

新鲜感还没过总要找点好玩的，immich算是比较知名的了。

开始

搭建方式是使用docker-compose.yml​

官方自带如下，总共分了4个部分，server、learn、redis、postgres

#
# WARNING: To install Immich, follow our guide: https://docs.immich.app/install/docker-compose
#
# Make sure to use the docker-compose.yml of the current release:
#
# https://github.com/immich-app/immich/releases/latest/download/docker-compose.yml
#
# The compose file on main may not be compatible with the latest release.

name: immich

services:
  immich-server:
    container_name: immich_server
    image: ghcr.xiaoji.de/immich-app/immich-server:${IMMICH_VERSION:-release}
    # extends:
    #   file: hwaccel.transcoding.yml
    #   service: cpu # 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
      - /home/yuxh/Media/Image:/data
      - ${UPLOAD_LOCATION}:/extlib
      - /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, rocm, openvino, rknn] to the image tag.
    # Example tag: ${IMMICH_VERSION:-release}-cuda
    image: ghcr.xiaoji.de/immich-app/immich-machine-learning:${IMMICH_VERSION:-release}
    # extends: # uncomment this section for hardware acceleration - see https://docs.immich.app/features/ml-hardware-acceleration
    #   file: hwaccel.ml.yml
    #   service: cpu # set to one of [armnn, cuda, rocm, openvino, openvino-wsl, rknn] for accelerated inference - use the `-wsl` version for WSL2 where applicable
    volumes:
      - model-cache:/cache
    env_file:
      - .env
    restart: always
    healthcheck:
      disable: false

  redis:
    container_name: immich_redis
    image: docker.io/valkey/valkey:8-bookworm@sha256:fea8b3e67b15729d4bb70589eb03367bab9ad1ee89c876f54327fc7c6e618571
    healthcheck:
      test: redis-cli ping || exit 1
    restart: always

  database:
    container_name: immich_postgres
    image: ghcr.xiaoji.de/immich-app/postgres:14-vectorchord0.4.3-pgvectors0.2.0@sha256:bcf63357191b76a916ae5eb93464d65c07511da41e3bf7a8416db519b40b1c23
    environment:
      POSTGRES_PASSWORD: ${DB_PASSWORD}
      POSTGRES_USER: ${DB_USERNAME}
      POSTGRES_DB: ${DB_DATABASE_NAME}
      POSTGRES_INITDB_ARGS: '--data-checksums'
      # Uncomment the DB_STORAGE_TYPE: 'HDD' var if your database isn't stored on SSDs
      # DB_STORAGE_TYPE: 'HDD'
    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
    shm_size: 128mb
    restart: always

volumes:
  model-cache:

其中你要选择，要不要跑机器学习，如果不要，可以去掉immich-machine-learning， 因为多数都是在nas这种弱性能设备来安装，所以跑起来很难。

然后就各显神通了，比如我用了/home/yuxh/Media/Image:/data​来映射我本地图片上去。

机器学习

可以选择用PC来机器学习，大概思路就是，现在windows有wsl可以装linux，然后再装docker，去单独跑那个immich-machine-learning镜像，开放外部访问给immich。

但是我看了下，有点麻烦，我选择直接安装Docker Desktop，基本一条龙就达成了。

docker-compose.yml如下：

name: immich_remote_ml

services:
  immich-machine-learning:
    container_name: immich_machine_learning
    # 如果需要硬件加速，可以在镜像标签中添加 -[armnn, cuda, openvino]
    # 例如：${IMMICH_VERSION:-release}-cuda
    image: ghcr.io/immich-app/immich-machine-learning:${IMMICH_VERSION:-release}
    # extends:
    #   file: hwaccel.ml.yml
    #   service: # 设置为 [armnn, cuda, openvino, openvino-wsl] 之一以启用加速推理
    volumes:
      - ./model-cache:/cache
    environment:
      - http_proxy=192.168.5.5:7890
      - https_proxy=192.168.5.5:7890
    restart: always
    ports:
      - 3003:3003

volumes:
  model-cache:

下载模型

​git clone https://huggingface.co/immich-app/buffalo_l​

​git clone https://huggingface.co/immich-app/XLM-Roberta-Large-Vit-B-16Plus​

​buffalo_l​是人脸识别，XLM-Roberta-Large-Vit-B-16Plus​是中文检索。

yaml里，我们映射了model-cache目录，所以目录结构大概如下：

​image​

​image​

‍

‍