DX4600部署immich相册

DX4600部署immich相册

步骤

  1. 开启DX4600远程调试功能
  2. 下载docekr-compose
  3. 下载immich部署文件
  4. 修改部署文件配置
  5. 部署
  6. 部署完配置

1.开启远程调试

这个步骤很简单，如下图，下面的验证码就是ssh密码，通过ssh工具连接登录绿联nas，用户root,端口922

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2.下载Docker-compose

已经下载过且可用的跳过此步骤。

可以搜索docker-compose二进制可执行文件直接下载到nas，拷贝到/usr/bin/目录下，加执行权限就行。

下面是我分享的文件，链接有时效，失效可以从网上下载。

https://web.ugreen.cloud/web/#/share/9da7a03728c24a95965242a749d65abb 提取码：YB3F

赋权操作：

~#chmod +x /usr/bin/docker-compose
~# ls -hl /usr/bin/docker-compose
-rwxr-xr-x    1 root     root       60.1M May 24 17:09 /usr/bin/docker-compose

然后输入docker-compose就会出现介绍和使用说明。这时候docekr-compose就已经可用了

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3.下载immich docker-compose部署文件

简介:

官方提供的docker-compose文件共有4个:

.env是环境变量文件，定义了通过immich上传图片的路径，外部库路径，即你之前就已经存在的图片要被immich扫描到需要添加这个外部库路径。

docekr-compose.yml是部署文件，不开硬件加速的话这个文件可以不用改。

另外两个是硬件加速（实验性功能）相关配置的部署文件hwaccel.ml和hwaccel.transcoding。实际测试硬件加速开启的情况下智能搜索会失效，看日志在使用intel核心显卡来跑ai模型的时候会报错，导致人脸识别和图像语义搜索功能失效。

实际只需要.env文件和docker-compose文件即可完成部署

环境变量介绍：

其中UPLOAD_LOCATION是immich上传的图片路径

EXTERNAL_PATH是外部库的路径。找到你之前存在的照片路径

DB_DATA_LOCATION是immich运行产生的数据，这些软件运行的文件如果条件允许推荐放到固态硬盘路径上

CACHE_LOCATION是immich缓存目录，也一样，推荐固态路径

配置文件中的路径需要集合自己的图片路径来进行相应更改

我是在固态硬盘上建立一个immich的文件夹，用来放部署文件和immich的运行数据和缓存

即/mnt/dm-0/.ugreen_nas/138716/Docker/immich

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下面是.env文件

# You can find documentation for all the supported env variables at https://immich.app/docs/install/environment-variables

# The location where your uploaded files are stored
UPLOAD_LOCATION=/mnt/dm-5/.ugreen_nas/138716/DSM/homes/immich
EXTERNAL_PATH=/mnt/dm-5/.ugreen_nas/138716

# The location where your database files are stored
DB_DATA_LOCATION=/mnt/dm-0/.ugreen_nas/138716/Docker/immich/data
CACHE_LOCATION=/mnt/dm-0/.ugreen_nas/138716/Docker/immich/cache
# To set a timezone, uncomment the next line and change Etc/UTC to a TZ identifier from this list: https://en.wikipedia.org/wiki/List_of_tz_database_time_zones#List
TZ=Asia/Shanghai

# The Immich version to use. You can pin this to a specific version like "v1.71.0"
IMMICH_VERSION=release

# Connection secret for postgres. You should change it to a random password
DB_PASSWORD=postgres

# The values below this line do not need to be changed
DB_USERNAME=postgres
DB_DATABASE_NAME=immich
HF_ENDPOINT=https://hf-mirror.com

下面是docker-compose.yml文件

#
# WARNING: 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.io/immich-app/immich-server:${IMMICH_VERSION:-release}
#    extends:
#      file: hwaccel.transcoding.yml
#      service: quicksync # set to one of [nvenc, quicksync, rkmpp, vaapi, vaapi-wsl] for accelerated transcoding
#   devices:
#     - /dev/dri:/dev/dri
    volumes:
      - ${UPLOAD_LOCATION}:/usr/src/app/upload
      - ${EXTERNAL_PATH}:/usr/src/app/external
      - /etc/localtime:/etc/localtime:ro
    env_file:
      - .env
    ports:
      - 2283:2283
    depends_on:
      - redis
      - database
    restart: always

  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}-openvino
#    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 `-wsl` version for WSL2 where applicable
#   devices:
#     - /dev/dri:/dev/dri
    volumes:
      - ${CACHE_LOCATION}:/cache
    env_file:
      - .env
    restart: always

  redis:
    container_name: immich_redis
    image: docker.io/redis:6.2-alpine@sha256:328fe6a5822256d065debb36617a8169dbfbd77b797c525288e465f56c1d392b
    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:
      - ${DB_DATA_LOCATION}:/var/lib/postgresql/data
    healthcheck:
      test: pg_isready --dbname='${DB_DATABASE_NAME}' --username='${DB_USERNAME}' || exit 1; Chksum="$$(psql --dbname='${DB_DATABASE_NAME}' --username='${DB_USERNAME}' --tuples-only --no-align --command='SELECT COALESCE(SUM(checksum_failures), 0) FROM pg_stat_database')"; echo "checksum failure count is $$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:

下面是hwaccel.ml.yml，排除硬件加速可以不用此文件

# 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

下面是hwaccel.transcoding.yml，排除硬件加速可以不用此文件

# 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:
      - LD_LIBRARY_PATH=/usr/lib/wsl/lib
      - LIBVA_DRIVER_NAME=d3d12

4.修改部署文件配置

不开启硬件加速，只需要拷贝.env文件和docker-compose文件到nas上即可

修改.env文件中的路径配置,

UPLOAD_LOCATION=/mnt/dm-5/.ugreen_nas/138716/DSM/homes/immich #此路径改为你实际上传路径
EXTERNAL_PATH=/mnt/dm-5/.ugreen_nas/138716					#此路径改为你已经存在的照片路径

# The location where your database files are stored
DB_DATA_LOCATION=/mnt/dm-0/.ugreen_nas/138716/Docker/immich/data #改为实际数据放置路径，新建
CACHE_LOCATION=/mnt/dm-0/.ugreen_nas/138716/Docker/immich/cache #改为实际缓存路径，新建

文件一览

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5 部署

在docker-compose文件所在路径下执行docker-compose up -d 即可

会拉取镜像创建容器，我这里已经拉取过，所以是直接重建。至于目前docker的问题，目前我使用1panel的镜像源:https://docker.1panel.live

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部署完，等所有immich容器都起来后，输入http://ip:2283即可访问immich控制台

6 部署完配置

登录
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主要说一点机器学习的ai模型，有点大，几个G，一般网络下载慢或者下不到模型，可以下载离线包，这里使用的智能模型为

XLM-Roberta-Large-Vit-B-16Plus，选完保存后会从官方库里下载模型，如果是离线包，下载好解压放置到cache缓存目录覆盖即可。

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离线包链接：https://www.123pan.com/s/WXqA-EGL6d.html

作者：大志若勇 https://www.bilibili.com/read/cv33865669/ 出处：bilibili

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下载离线包，解压后会得到如下文件夹,将这三个拷贝到缓存路径下即可。

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这样就可以加载ai模型。进行人脸识别和图片语义搜索。

posted @   红河1994  阅读(500)  评论(0)  编辑  收藏  举报
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