LLaMA-Factory VS kamal

Depends what model you want to train, and how well you want your computer to keep working while you're doing it.

If you're interested in large language models there's a table of vram requirements for fine-tuning at [1] which says you could do the most basic type of fine-tuning on a 7B parameter model with 8GB VRAM.

You'll find that training takes quite a long time, and as a lot of the GPU power is going on training, your computer's responsiveness will suffer - even basic things like scrolling in your web browser or changing tabs uses the GPU, after all.

Spend a bit more and you'll probably have a better time.

[1] https://github.com/hiyouga/LLaMA-Factory?tab=readme-ov-file#...

Guessing you're talking about https://kamal-deploy.org/ which looks interesting, though I tend to like reconciliation logic based systems ... but often only fired off imperatively with a plan/apply separation. So I shall be having a poke around anyway :)

Navigating the deployment landscape can be a daunting task for any developer, particularly those going it alone. PopaDex uses Kamal as its deployment solution, due to its straightforward and efficient nature. This tool is designed to facilitate the quick deployment of Rails applications across diverse environments, streamlining what can often be a complicated process. Kamal's appeal lies in its ability to demystify deployment, removing the hurdles commonly associated with Docker and app deployment in general. A couple of the main benefits of Kamal:

Or you can use [https://kamal-deploy.org/] and GitHub actions for automating at least deploys, but there are still will be issues that you will need to solve.

For homelab, Docker compose should be enough

For something more production oriented https://github.com/basecamp/kamal

Kamal is a wonderfully simple way to deploy your applications anywhere. It will also be included by default in Rails 8. Kamal is trivial, but I don’t recommend using it on your development machine.