lightning-hydra-template VS lightning-transformers

I've been trying to learn PyTorch Lightning and Hydra in order to use/create my own custom deep learning template (e.g. like this) as it would greatly help with my research workflow. A lot of the work I do requires me to analyse metrics based on the logits/activations of the model.

I can't say for sure whether it is the best combination for research in the long run, but if you do go down that route I have found this template very useful

lightning-hydra-template

This lightning + hydra template is quite complete. Great for learning best practices.

A good example is this project template https://github.com/ashleve/lightning-hydra-template. It uses a lot of cool things such as

and many more! (checkout the #Your Superpowers section of the readme)

I write a LightningDatamodule. I found it to be an intuitive way to encapsulate any dataset. LightningDatamodule is a simple abstraction providing methods for data download, split, transforms and exposing dataloaders. Would love to see more researchers try out this concept, even in projects which don't use pytorch lightning. Reading LightningDatamodule makes me immedietely see how the dataset is prepared, while it seems like most data science projects throw around data logic across different parts of the pipeline, making it hard to understand what's going on. You can see example of such datamodule here

I feel like most ML people don't use those tools because they simply don't realize all the advantages (especially Hydra seems like a very useful addition to any deep learning project). I focused on structuring the readme in a way, which (I hope) will give you a quick overview - my hope is it can help to spread the word about those frameworks in a broaded community. It incorporates best practices and tricks I gathered over the last couple of months of playing around with it.

Lightning Transformers is for users who want to train, evaluate and predict using HuggingFace models and datasets with PyTorch Lightning. Full customizability of the code using the LightningModule and Trainer, with Hydra config composition for quick and easy experimentation. No boilerplate code is required; easily swap out models, optimizers, schedulers, and more without touching the code. Check out the blog post: Training Transformers at Scale with PyTorch Lightning for more information or the documentation.