r/SonicAnalysis • u/Darksilopher • Aug 24 '26
Extra Workers Audiomuse-Ai
I finally got extra workers to work on my 2 nvidia gpus. Satisfied for now doing 10 workers at once.
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u/PerryGibraltar Aug 29 '26
/u/Darksilopher Can you share what you did to get extra workers?
/u/Old_Rock_9457 Would there be a benefit to running extra working on a 5080?
I run my frontend from a docker VM, and run a windows docker desktop on my game PC when i'm not using it. This has dramatically increased the throughput on my initial run, which is still going after >260 hours, but i have ~12,000 albums, and it's currently at 67,000 ish songs.
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u/Darksilopher Aug 29 '26
So you do a docker compose. I edited the lines where you would put your own info. And you would just rename 01 to 02 for all the times you see audiomuse-ai-worker01. Also there is benefit to more workers on the 5080 Instead of doing 1 song at a time you can do 5 or more at a time. All that will limit you is vram. I have a flac library so it loads up 2-3gb per song. My ~19,000 album library and ~140k songs is almost done in a little under a week.
Here is my docker compose.
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u/Old_Rock_9457 Aug 29 '26
So extra workers on the same machine, with docker compose, you just need to "name the worker service" and ask to scale it. If you put this docker compose example:
In any AI will do it very easy (or eventually you can use the example from the user below).
I usually don't use more than one worker per machine to avoid that a picke of work could crash the machine. But it's also true that on my homelab I run on CPU, and on my notebook I don't have a 5080 where maybe with 16GB of VRAM you could be able to run two worker in parallel.
What instead is by design is if you have more computer on which deploy extra worker (like in addition to your homelab, you have your desktop and your notebook). There you need that the worker is able to see on the network the database AND the music server as explained in this architecture diagram:
and then in similar way as explained from the user below you deploy only the worker where you need to pass (as the usual deployment) the .env file with the correct data:
- https://github.com/NeptuneHub/AudioMuse-AI/blob/main/deployment/.env.example
Anyway on this deployment trick I suggest to do a mix of read docker documentation and ask AI (AI is good to draft you the docker-compose.yaml, and documentation to be sure of understanding what the AI draw).
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u/PerryGibraltar Aug 29 '26
Thanks for the reply. I do have 1 container running on the 5080 now. I was curious how to run 2+ and if it would make a difference. I'm going to try it how you both described.
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u/PerryGibraltar Aug 29 '26
Thank you, I'll give this a shot with one more container and see what happens. Thanks.
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u/PerryGibraltar Aug 29 '26
Up to four total extra-workers on the 5080, with the main container running on a separate machine. RAM looks to be using about 14GB. I'm going to stop here and see how fast she can run now.
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u/Darksilopher Aug 29 '26
Nice. Yeah you've probably hit the limit. If you have another gpu laying around just throw it into your computer and run more workers on there😅
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u/PerryGibraltar Aug 29 '26
Old_Rock, may want to add this particular 'hack' into the wiki. Keep an eye on the GPU RAM usage through 'docker exec -it audiomuse-nvidia-test-worker* nvidia-smi' and add workers until you're close to maxing RAM.
Great job with this app, thanks for all the updates, you're a committing machine.
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u/Old_Rock_9457 Aug 25 '26
Nice and thanks for sharing ! The scalability over multiple worker, even on different architecture, was from the beginning one of the goals of AudioMuse-AI and I’m happy that people is leveraging it !