modular-monolith-with-ddd VS pytorch-lightning

Link to PR here: https://github.com/kgrzybek/modular-monolith-with-ddd/pull/286

- https://github.com/kgrzybek/modular-monolith-with-ddd (a really well documented example)

You can see there is a module called building_blocks. Inside it, I keep all the utilities needed in the project, like a logger, serializers, and so on. I did not make up this name, I borrowed it from this repo.

9. Modular Monolith with TDD

This covers far more than just DDD, however, I find the links and README super helpful for a wide range of subjects: https://github.com/kgrzybek/modular-monolith-with-ddd

Modular monolith. Here is one of the best production-ready examples https://github.com/kgrzybek/modular-monolith-with-ddd

Am wondering what react community thinks of DDD.

I've been reading "blue" DDD book (by Eric Evans) and "red" book (by Vaugh Vernon) and that was a completely "my whole life was a lie" type of experience and relief at the same time. It's just so great to have the principles of who to structure the code. It, by definition makes, your codebase structure meaningful. Because it's structured according to some common knowledge, not your random thoughts at the time you were writing code.

I was surprised to find so little DDD react sample codebases. Let's say for backend there is huge amount of samples, i.e. https://github.com/kgrzybek/modular-monolith-with-ddd . For react/frontend I have bookmarked only https://github.com/talyssonoc/react-redux-ddd/tree/master/sr... and few more, but those others does not meet the optional criteria i like really much - at the highest (or at app) level all codebase need to have folders app, domain, infra and ui. Simple rule, but simplifies life a lot.

So my question is - is DDD for some reasons not very applicable for app frontend development. Or it just never became popular. Or maybe DDD is popular amongst react developers, just I am not aware of this.

Many thanks for any ideas and comments!

It's very easy to get started, right in your Terminal, no fees! No credit card at all.

And there are cloud providers like https://replicate.com/ and https://lightning.ai/ that will let you use your LLM via an API key just like you did with OpenAI if you need that.

You don't need OpenAI - nobody does.

https://see.stanford.edu/Course/CS229 https://lightning.ai/ https://www.youtube.com/watch?v=00s9ireCnCw&t=57s https://towardsdatascience.com/

I've been wondering on what is the recommended method of saving logits/activations using PyTorch Lightning. I've looked at Callbacks, Loggers and ModelHooks but none of the use-cases seem to be for this kind of activity (even if I were to create my own custom variants of each utility). The ModelCheckpoint Callback in its utility makes me feel like custom Callbacks would be the way to go but I'm not quite sure. This closed GitHub issue does address my issue to some extent.

Our codebase for the diffusion models builds heavily on OpenAI's ADM codebase , lucidrains, Stable Diffusion, Lightning and Hugging Face. Thanks for open-sourcing!

For lightweight use, pytorch-lightning is too heavy, and its source code will be very difficult for beginners to read, at least for me.

For the A100 GPU, theoretical performance is the same for FP16/BF16 and both rely on the same number of bits, meaning memory should be the same. However since it's quite newly added to PyTorch, performance seems to still be dependent on underlying operators used (pytorch lightning debugging in progress here).