grok-ai/nn-template

Generic template to bootstrap your PyTorch project.

nn-template's Issues

Curious if you checked out DAGsHub

Hi @lucmos, this looks like an awesome repo. I stumbled on it while doing some research on project templates for ML projects. I'm one of the creators of DAGsHub which is a platform built on Git, DVC, and MLflow. It integrates with GitHub and provides a free DVC remote and MLflow server so that you can track experiments and share your data & models in one UI.

Here's an example project to showcase the abilities:
https://dagshub.com/OperationSavta/SavtaDepth

It seems really in line with what you're creating here, and I would love to hear your thoughts about it.

Streamlit UI - Weights and Biases login

The template is really awesome.

I had a small issue. When I run the Streamlit UI without being logged in in Weights and Biases. The UI just hanged with the loading status without giving me any feedback about what was happening, so that I have to log in first in wandb.
I had to login manually from the console. Is there any way to solve this issue? For example to have feedback from the UI if I'm not logged in.

Thanks!

Maybe not so friendly with Windows

See for example https://github.com/txie-93/cdvae and txie-93/cdvae#17, though I haven't tried directly with nn-template.

Do you collaborate with lightning-hydra-template?

Hi, it is an awesome template!

For me it looked like you have similar principles as in Lightning-Hydra-Template. I am wondering if you collaborate?

Update LightningDataModule example usage

I believe L217 in datamodule.py should be changed from

_: pl.LightningDataModule = hydra.utils.instantiate(cfg.data.datamodule, _recursive_=False)

to

_: pl.LightningDataModule = hydra.utils.instantiate(cfg.nn.data, _recursive_=False)

Thanks for the great project, keep up with the good work! @lucmos

Switch from pytorch_lightning to lightning

We should change all the imports from pytorch_lightning to lightning

However, naively substituting all the occurrences does not work -- unknown reasons atm.

Resuming from a specific run

Thank you very much for this template, how can we restore the weights of a specific run using wandb? thanks

load_model

Hi,
This issue concerns the function from nn_core.serialization import load_model
Suppose i train a pytorch model with class MyLightningModule, and that I saved the checkpoint in model_path.
Suppose now that the class MyLightningModule has received some minor changes, like a new class variable has been added.
Let's call this version MyLightningModuleV2.
When I load a model using this function, like:

self.model = load_model(module_class=MyLightningModuleOld, checkpoint_path=Path(model_path), map_location=self.device).to(self.device).eval()

I get an error because the chekpoint refers to the model of class MyLightningModule and therefore the new variable is (obviously) missing.
To make it work, i need to load the model with the old version of the class, that is, MyLightningModule, and then manually setting "model.new_variable" to the value i want to get, like the following:

self.model = load_model(module_class=MyLightningModuleOld, checkpoint_path=Path(model_path), map_location=self.device).to(self.device).eval()
self.model.new_variable = False

It would be nice to have this option in the load_model function to avoid creating multiple versions of the same class.

PyTorch Lightning EcoCI integration to check for compatibility with latest & upcoming releases

Hey Valentino & Luca,

I am just catching up with some bookmarks and remembered your repo here :). As someone who constantly fuzzes about the ideal project structure, that's actually pretty cool. I have been using an adapted version of the data science cookiecutter for generic ML projects, but nothing sophisticated like this here with code stubs.

Haven't thoroughly played with it yet, though, besides creating an example folder and looking at the pl_module.py and datamodule.py files, which look good to me!

In any case, long story short, I was wondering if you'd be interested in the PyTorch Lightning's ecosystem CI to make sure that it stays fresh and relevant wrt to upcoming version releases (comes with free CPU and multi-GPU CI tests): https://devblog.pytorchlightning.ai/stay-ahead-of-breaking-changes-with-the-new-lightning-ecosystem-ci-b7e1cf78a6c7

If you are interested in that, I am sure my colleague @Borda would be happy to assist with questions & technical details -- he built this thing, so he probably knows best :)