PSA: If the first Smart Search in Immich takes a while
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wrote last edited by [email protected]
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:/cacheTo that:
immich-machine-learning: ... volumes: - ./cache:/cacheI 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.
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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:/cacheTo that:
immich-machine-learning: ... volumes: - ./cache:/cacheI 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.
Ok I should know this by now, but what actually is the current “./“ directory when you use that? Is it the docker daemons start dir like /var/docker ?
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Ok I should know this by now, but what actually is the current “./“ directory when you use that? Is it the docker daemons start dir like /var/docker ?
It usually is the directory where you execute the docker compose command.
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Ok I should know this by now, but what actually is the current “./“ directory when you use that? Is it the docker daemons start dir like /var/docker ?
./will be the directory you run your compose from -
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:/cacheTo that:
immich-machine-learning: ... volumes: - ./cache:/cacheI 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.
wrote last edited by [email protected]Oh, and if you haven't changed from the default ML model, please do. The results are phenomenal. The default is nice but only really needed on really low power hardware. If you have a notebook/desktop class CPU and/or GPU with 6GB+ of RAM, you should try a larger model. I used the best model they have and it consumes around 4GB VRAM.
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Oh, and if you haven't changed from the default ML model, please do. The results are phenomenal. The default is nice but only really needed on really low power hardware. If you have a notebook/desktop class CPU and/or GPU with 6GB+ of RAM, you should try a larger model. I used the best model they have and it consumes around 4GB VRAM.
Which model would you recommend? I just switched from ViT-B/32 to ViT-SO400M-16-SigLIP2-384__webli since it seemed to be the most popular.
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Which model would you recommend? I just switched from ViT-B/32 to ViT-SO400M-16-SigLIP2-384__webli since it seemed to be the most popular.
I switched to the same model. It's absolutely spectacular. The only extra thing I did was to increase the concurrent job count for Smart Search and to give the model access to my GPU which sped up the initial scan at least an order of magnitude.
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Oh, and if you haven't changed from the default ML model, please do. The results are phenomenal. The default is nice but only really needed on really low power hardware. If you have a notebook/desktop class CPU and/or GPU with 6GB+ of RAM, you should try a larger model. I used the best model they have and it consumes around 4GB VRAM.
Is this something that would be recommended if self-hosting off a Synology 920+ NAS?
My NAS does have extra ram to spare because I upgraded it, and has NVME cache
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./will be the directory you run your compose fromThat seems like a bad idea
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That seems like a bad idea
Its convenient because your data is stored in the same folder that your docker-compose.yaml file is in, making backups or migrations simpler.
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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:/cacheTo that:
immich-machine-learning: ... volumes: - ./cache:/cacheI 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.
It's not normal for
- model-cache:/cacheto be deleted on restart or even upgrade. You shouldn't need to do this. -
It's not normal for
- model-cache:/cacheto be deleted on restart or even upgrade. You shouldn't need to do this.wrote last edited by [email protected]Yes, it depends on how you're managing the service. If you're using one of the common patterns via systemd, you may be cleaning up everything, including old volumes, like I do.
E: Also if you have any sort of lazy prune op running on a timer, it could blow it up at some point.
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Is this something that would be recommended if self-hosting off a Synology 920+ NAS?
My NAS does have extra ram to spare because I upgraded it, and has NVME cache
wrote last edited by [email protected]That's a Celeron right? I'd try a better AI model. Check this page for the list. You could try the heaviest one. It'll take a long time to process your library but inference is faster. I don't know how much faster it is. Maybe it would be fast enough to be usable. If not usable, choose a lighter model. There's execution times in the table that I assume tell us how heavy the models are. Once you change a model, you have to let it rescan the library.
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That's a Celeron right? I'd try a better AI model. Check this page for the list. You could try the heaviest one. It'll take a long time to process your library but inference is faster. I don't know how much faster it is. Maybe it would be fast enough to be usable. If not usable, choose a lighter model. There's execution times in the table that I assume tell us how heavy the models are. Once you change a model, you have to let it rescan the library.
That’s a Celeron right?
Yup, the Intel J4125 Celeron 4-Core CPU, 2.0-2.7Ghz.
I switched to the ViT-SO400M-16-SigLIP2-384__webli model, same as what you use. I don't worry about processing time, but it looks like a more capable model, and I really only use immich for contextual search anyway, so that might be a nice upgrade.
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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:/cacheTo that:
immich-machine-learning: ... volumes: - ./cache:/cacheI 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.
wrote last edited by [email protected]Doing a volume like the default Immich docker-compose uses should work fine, even through restarts. I'm not sure why your setup is blowing up the volume.
Normally volumes are only removed if there is no running container associated with it, and you manually run
docker volume prune -
That’s a Celeron right?
Yup, the Intel J4125 Celeron 4-Core CPU, 2.0-2.7Ghz.
I switched to the ViT-SO400M-16-SigLIP2-384__webli model, same as what you use. I don't worry about processing time, but it looks like a more capable model, and I really only use immich for contextual search anyway, so that might be a nice upgrade.
Did you run the Smart Search job?
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That seems like a bad idea
It's not.
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Doing a volume like the default Immich docker-compose uses should work fine, even through restarts. I'm not sure why your setup is blowing up the volume.
Normally volumes are only removed if there is no running container associated with it, and you manually run
docker volume pruneBecause 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.target -
Its convenient because your data is stored in the same folder that your docker-compose.yaml file is in, making backups or migrations simpler.
Yup. Everything is in one place and there's no hardcoded paths outside of the work dir making it trivial to move across storage or even machines.
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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.targetBut why?
why not just down up normally and have a cleanup job on a schedule to get rid of any orphans?
