Your ML model cache volume is getting blown up during restart and the model is being re-downloaded during the first search post-restart. Either set it to a path somewhere on your storage, or ensure you’re not blowing up the dynamic volume upon restart.
In my case I changed this:
immich-machine-learning:
...
volumes:
- model-cache:/cache
To that:
immich-machine-learning:
...
volumes:
- ./cache:/cache
I no longer have to wait uncomfortably long when I’m trying to show off Smart Search to a friend, or just need a meme pronto.
That’ll be all.
Because I clean everything up that’s not explicitly on disk on restart:
[Unit] Description=Immich in Docker After=docker.service Requires=docker.service [Service] TimeoutStartSec=0 WorkingDirectory=/opt/immich-docker ExecStartPre=-/usr/bin/docker compose kill --remove-orphans ExecStartPre=-/usr/bin/docker compose down --remove-orphans ExecStartPre=-/usr/bin/docker compose rm -f -s -v ExecStartPre=-/usr/bin/docker compose pull ExecStart=/usr/bin/docker compose up Restart=always RestartSec=30 [Install] WantedBy=multi-user.targetThat’s wild! What advantage do you get from it, or is it just because you can for fun?
Also I’ve never seen a service created for each docker stack like that before…
Wow, you pull new images every time you boot up? Coming from a mindset of having rock solid stability, this scares me. You’re living your life on the edge my friend. I wish I could do that.
I use a fixed tag. 😂 It’s more a simple way to update. Change the tag in SaltStack, apply config, service is restarted, new tag is pulled. If the tag doesn’t change, the pull is a noop.
Ahh, calmed me down. Never thought of doing anything like you’re doing it here, but I do like it.
But why?
why not just down up normally and have a cleanup job on a schedule to get rid of any orphans?
I a world where we can’t really be sure what’s in an upgrade, a super-clean start that burns any ephemeral data is about the best way to ensure a consistent start.
And consistency gives reliability, as much as we can get without validation (validation is “compare to what’s correct”, but consistency is “try to repeat whatever it was”).