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.
That’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…
Well, you gotta start it somehow. You could rely on compose’es built-in service management which will restart containers upon system reboot if they were started with
-d
, and have the right restart policy. But you still have to start those at least once. How’d you do that? Unless you plan to start it manually, you have to use some service startup mechanism. That leads us to systemd unit. I have to write a systemd unit to dodocker compose up -d
. But then I’m splitting the service lifecycle management to two systems. If I want to stop it, I no longer can do that via systemd. I have to go find where the compose file is and issuedocker compose down
. Not great. Instead I’d write a stop line in my systemd unit so I can start/stop from a single place. But wait 🫷 that’s kinda what I’m doing isn’t it? Except if I start it withdocker compose up
without-d
, I don’t need a separate stop line and systemd can directly monitor the process. As a result I get logs injournald
too, and I can use systemd’s restart policies. Having the service managed by systemd also means I can use aystemd dependencies such as fs mounts, network availability, you name it. It’s way more powerful than compose’s restart policy. Finally, I like to clean up any data I haven’t explicitly intended to persist across service restarts so that I don’t end up in a situation where I’m debugging an issue that manifests itself because of some persisted piece of data I’m completely unaware of.Interesting, waiting on network mounts could be useful!
I deploy everything through Komodo so it’s handling the initial start of the stack, updates, logs, etc…