Smart Search - ML can not load images in PIL
Having an issue with Smart Search. I have tried switching models a few times with no luck. I can see that the new model is downloaded when I switch by looking in the logs, so I don't think thats the issue.
Errors happen when I start a Smart Search job. Seems like each time the server sends an image, ML can't interpret it.
Please let me know what other info would be helpful to send other than what I have in the attached files. Thanks so much!
Server Version v1.125.2
Immich via Docker on Ubuntu 24.10
Storage is two 12TB drives, ZFS RAID1
19 Replies
:wave: Hey @disorderlyconduct,
Thanks for reaching out to us. Please carefully read this message and follow the recommended actions. This will help us be more effective in our support effort and leave more time for building Immich :immich:.
References
- Container Logs:
docker compose logs docs
- Container Status: docker ps -a docs
- Reverse Proxy: https://immich.app/docs/administration/reverse-proxy
- Code Formatting https://support.discord.com/hc/en-us/articles/210298617-Markdown-Text-101-Chat-Formatting-Bold-Italic-Underline#h_01GY0DAKGXDEHE263BCAYEGFJAChecklist
I have...
1. :ballot_box_with_check: verified I'm on the latest release(note that mobile app releases may take some time).
2. :ballot_box_with_check: read applicable release notes.
3. :ballot_box_with_check: reviewed the FAQs for known issues.
4. :ballot_box_with_check: reviewed Github for known issues.
5. :ballot_box_with_check: tried accessing Immich via local ip (without a custom reverse proxy).
6. :ballot_box_with_check: uploaded the relevant information (see below).
7. :ballot_box_with_check: tried an incognito window, disabled extensions, cleared mobile app cache, logged out and back in, different browsers, etc. as applicable
(an item can be marked as "complete" by reacting with the appropriate number)
Information
In order to be able to effectively help you, we need you to provide clear information to show what the problem is. The exact details needed vary per case, but here is a list of things to consider:
- Your docker-compose.yml and .env files.
- Logs from all the containers and their status (see above).
- All the troubleshooting steps you've tried so far.
- Any recent changes you've made to Immich or your system.
- Details about your system (both software/OS and hardware).
- Details about your storage (filesystems, type of disks, output of commands like
fdisk -l and df -h).
- The version of the Immich server, mobile app, and other relevant pieces.
- Any other information that you think might be relevant.
Please paste files and logs with proper code formatting, and especially avoid blurry screenshots.
Without the right information we can't work out what the problem is. Help us help you ;)
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I will attach cleaner images of the errors
Is the ML container on the latest update as well?
yes it is
I just completely deleted the cache of CLIP in immich_machine_learning 's docker container. Then restarted the container and got it to redownload. Still having the same error.
I disabled hardware acceleration in docker-compose.yml and and am still getting the same issue
Here is the immich_server log for running without hardware acceleration
From the log, I don't understand what is going wrong. A bad literal??
Something strange I notice, is that under server stats, It thinks my files have no size?
The ML server not being able to decode the preview usually happens when the thumbnail job failed after succeeding once. It leaves behind a 0kb file that the machine learning service naturally can’t use
I nuked the whole container and started over from scratch. Working totally fine now
Hi, I'm having the same issue with the bad literal log lines, how can I reinstall the container as you did? I suppose that process doesn't affect the immich server, right? I cannot imagine doing it all again 🤣
Instead of hijacking someone else's thread you'd be better off creating your own #help-desk-support post
Okey, so sorry, I'm new on the server and I didn't knew what was prefearable, I though it would be more efficient if there was some context
My assumption is that I broke something because I was docker compose down / up a lot due to working on setting up other containers on my homelab