composer VS pytorch-lightning

Mosaic's open source library is excellent: Composer https://github.com/mosaicml/composer.

* It gives you PyTorch DDP for free. Makes FSDP about as easy as can be, and provides best in class performance monitoring tools. https://docs.mosaicml.com/en/v0.12.1/notes/distributed_train...

Here's a nice intro to using Huggingface models: https://docs.mosaicml.com/en/v0.12.1/examples/finetune_huggi...

I'm just a huge fan of their developer experience. It's up there with Transformers and Datasets as the nicest tools to use.

You may consider using Composer Composer by MosaicML.

Which is why I'm excited to announce that we (MosaicML) just released an automatic way to avoid these errors. Namely, we just added automatic gradient accumulation to Composer, our open source library for faster + easier neural net training.

I would love to know your thoughts on PyTorch Lightning vs. other, even more lightweight libraries, if you have the time. PL strikes me as being less idiosyncratic than FastAI, but I'm still not sure whether it would be better in engineering work to go even more lightweight (when I'm not just writing the code myself) -- something that offers up just optimizations and a trainer, a la MosaicML's [Composer](https://github.com/mosaicml/composer) or Chris Hughes's [pytorch-accelerated](https://github.com/Chris-hughes10/pytorch-accelerated) .

This master's thesis sort of does it, but it doesn't have any fine-tuning yet so it completely wrecks the accuracy: https://github.com/joennlae/halutmatmul.

If someone worked on contributing this to Composer [1] I'd be down to help out. I can't justify building it all on my own right now since we're 100% focused on training speedup, but I could definitely meet and talk through it, help code tricky parts, review PRs, etc.

[1] https://github.com/mosaicml/composer

We're always looking for contributors for Composer. tl;dr it speeds up neural net training by a lot (e.g., 7x faster ResNet-50).

Looking at this: https://github.com/mosaicml/composer

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).