解决方案：
将：image: ghcr.io/immich-app/immich-server:${IMMICH_VERSION:-release}
改为：image: ghcr.nju.edu.cn/immich-app/immich-server:${IMMICH_VERSION:-release}
使用镜像下载，给出修改后的文件，直接复制然后就可以运行。
点击查看代码
#
# 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.nju.edu.cn/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
- ${UPLOAD_LOCATION}:/usr/src/app/upload
- /etc/localtime:/etc/localtime:ro
env_file:
- .env
ports:
- 2283:3001
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: ${IMMICH_VERSION:-release}-cuda
image: ghcr.nju.edu.cn/immich-app/immich-machine-learning:${IMMICH_VERSION:-release}
# extends: # uncomment this section for hardware acceleration - see https://immich.app/docs/features/ml-hardware-acceleration
# file: hwaccel.ml.yml
# service: cpu # set to one of [armnn, cuda, openvino, openvino-wsl] 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/redis:6.2-alpine@sha256:2d1463258f2764328496376f5d965f20c6a67f66ea2b06dc42af351f75248792
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
- ${DB_DATA_LOCATION}:/var/lib/postgresql/data
healthcheck:
test: pg_isready --dbname='${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:
分析过程：
失败方法一：添加镜像
下载时很慢。
使用镜像也很慢，
这里我将https://ghcr.nju.edu.cn添加到镜像源也不行。
其实这里如果添加了镜像直接使用命令拉就可以了，会选择最优镜像。但是docker-compose.yml里面直接把路径写死了。
{
"registry-mirrors": ["https://wheurbwj.mirror.aliyuncs.com",
"https://dockerproxy.com",
"https://mirror.baidubce.com",
"https://docker.m.daocloud.io",
"https://docker.nju.edu.cn",
"https://docker.mirrors.sjtug.sjtu.edu.cn",
"https://ghcr.nju.edu.cn"
]
}
最后的效果：下载的很快，基本上跑慢了带宽。