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  1. Any chance something in your memory allocation changed? Not enough memory allocated for your camera/frame buffer size is a common pitfall (granted you updated from working config), zmMemAttach sounds like may indicated a memory issue. Memory has certainly bit me enough times...
  2. Small PSA on mocord and zmninja and notifications. If you run Mocord, by default you get recordings every 10 minutes. zmninja/eventserver ties notifications to the event ID, so that if multiple motion events trigger within the same 10 minute window, you will only receive the first notification and in the timeline you will only see the 10 minute chunks. The workaround I found here: You can find the aforementioned setting under CONFIG and the help section
  3. Unfortunately, this may be unifi controller type situation all over again, where 'a solution' (but not easy, fun, or making @dlandon's life any easier) is versioning so people can lock in a 'last working version', but that's an ugly matrix of versions, ZM ver + ES Ver + ?opencv? = exponential growth.
  4. You need to go into your camera config and change how it records, read the ZM documentation for more details on what does what: Yah, there's a lot going on, I don't pretend to have found it all, mlapi isn't designed to be completely on a remote device just yet, reading between the lines it seems one reason for making the api was so that the ML models could stay in memory so they don't have to be loaded each time, which apparently they have to be loaded on each 'image check' for one reason or another? I'm not a code monkey to catch evverything, only know enough to be dange
  5. Unfortunately I spoke too soon. If I had read the FAQ with more comprehension, the current mlapi implementation still requires most if not all of the ML libraries be installed on the ZM install, so that isn't a solution just yet. The mlapi docker hasn't been updated in 7 months but you will probably have to setup/build the docker yourself because of the compile requirements of opencv so I don't think one click install app version of a ML docker is in the cards for most unraid users. A VM following the instructions would therefore be 'easier' for most users. And to continue to ra
  6. If it 'went back down' it would seem that it was temp files, perhaps package updates or something. Your database should be stashed in appdata so that shouldn't grow the image unless it's mirrored over for some reason. It does seem odd that your 1 day change originally was 'exactly' doubling in size, from 3.31GB to 6.62GB which seems more than coincidental. For reference I'm running 3.06GiB image for my 222GiB of video at the moment.
  7. If it's still growing, My next best guess is that ZM config may have an error in it. Have you changed this? Does the rate of growth of the container match your expected rate of recorded video? Your config and run command looks correct to me. /mnt/disks/cctv/zoneminder has files in it, but does it have the LATEST files and ALL the files? If the cctv disk wasn't mounted at one point when the docker started some files might go into the image instead, I'm pretty sure I goofed and broke things that way one time.
  8. Remind me again what's giving you this image/readout? I'm struggling today. EDIT: Oh FFS, 2 seconds after I post of course it comes to me, (Container Sizes) button on Docker page...(grumble grumble)
  9. This is all the same problem. /EDIT (this sentence didn't leave my head) your ZM storage location needs to be corrected. /EDIT. /appdata/zoneminder/data is the default storage location for video/images that zoneminder stores. ZM will delete when (default 95%?) full, go to the filters tab in ZM and you can adjust. I highly recommend setting up a quota system to limit ZM's storage area, and recommend even more setting up an unassigned drive to be your video only storage. Unraid doesn't do quota's in the normal sense (this share shall be XXXGiB in size) so you'll have to do your ow
  10. Are you trying to limit the RAM the container needs or the storage of video files on your array?
  11. ES does support remote ML server as described here. Which if you go down the rabit hole, you get pliablepixels mlapi software, which does have a containerized version someone has made (and may have GPU support?). It may be possible even now to glue this all together. Obviously experimentation must ensue. The more I dig through stuff the more I tend to agree with dlandon that this container is doing a lot of 'init install' stuff that is more OS/VM behavior than docker pull/start behavior and I don't fault wanting to kick that to the curb. Having
  12. I think I may have figured it out for some (most?) people having these problems, maybe? A) dlandon is right, you really need to go into the configs (specifically objectconfig.ini) and understand (at least somewhat) what is going on in there. Everyone's config is a little different (Yolo v3 or v4? tiny or not? cpu, gpu, or tpu?, etc) and will need to be setup for what hardware/software you're using. B) the default [object] section looks like this: [object] # If you are using legacy format (use_sequence=no) then these parameters will # be used during ML inferencing
  13. Ah, good to know, thanks for that, completely missed it. Then yes it's probably good it's gone as it frees up space a reduces confusion in the extra parameters field.
  14. All of them are still applicable, they're docker parameters (you can have a google about them if you like). Unless I completely forgot that I added them myself, I thought that they were originally in the template but had fallen off for whatever reason.
  15. Just an FYI I noticed that the log file limitations that were in place have gone away from the template. Extra Parameters: --log-opt max-file=1 --log-out max-size=50m I know it probably junked up/confused the field that users would be adjusting the shared mem, but zoneminder will probably make some huge log files given the chance. Also a PSA, I recommend users add a cpu usage limit to the container, specifically when using object detect because of the opencv compile, which can lock up your server when it uses 100% CPU to compile. I'd recommend N-2 at most, where N is your c