remove production_mnist as it the same as research_mnist just using a deprecated dictionary on the __step_end

some next steps/PRs

• add testing
• generating docs

pretty much adding CI/docs tools as we have in Sandbox

should we promote from here to provide minimal unitests ?

it's the first thing I check in research repos and usually none of them provide minimum testing, which makes me doubt about the reliability of showed results in the paper.

In my own project using this seed, I updated to 1.0.0rc2 to discover that TrainResult and EvalResult are deprecated.

For example, I got the error: AttributeError: module 'pytorch_lightning' has no attribute 'EvalResult'

It would be great if this repo can be used with the upcoming 1.0.0 release.

@williamFalcon have you considered making this a GitHub Template?

"After you make your repository a template, anyone with access to the repository can generate a new repository with the same directory structure and files."

Recently I've been trying to develop a very general PyTorch Lightning + Hydra template.

The idea is to extend deep-learning-project-template, by just adding some Hydra config structure and simple example, which initializes modules from config parts in the most generic and obvious way.

I've been wondering if it would be a good idea to develop an official template of this sort and add it to Lightning ecosystem as an alternative for deep-learning-project-template? Currently it seems like Hydra is still a little rough around the edges and it's not obvious how config should be structured for Lightning, but still such a template could be a useful addition in the future.

Here is a link to my version: https://github.com/hobogalaxy/lightning-hydra-template

clone project

git clone https://github.com/YourGithubName/deep-learning-project-template

install project

cd deep-learning-project-template pip install -e .
pip install -r requirements.txt Next, navigate to any file and run it.

module folder

cd project

run module (example: mnist as your main contribution)

python lit_classifier_main.py
Imports

This project is setup as a package which means you can now easily import any file into any other file like so:

from project.datasets.mnist import mnist from project.lit_classifier_main import LitClassifier from pytorch_lightning import Trainer

model

model = LitClassifier()

data

train, val, test = mnist()

train

trainer = Trainer() trainer.fit(model, train, val)

test using the best model!

trainer.test(test_dataloaders=test) Citation

@article{YourName, title={Your Title}, author={Your team}, journal={Location}, year={Year} great={vocal} Asp.2n={kesnting}

I think it would be more than beneficial to provide an example that would use all the hooks from the LightningModule and LightningDataModule so that the end user has a clear overview about customisation and use.

Hey,

I was just quickly browsing through the templates when I noticed that for the Autoencoder project the argparse parameter 'hidden_dim' is unused:

https://github.com/PyTorchLightning/deep-learning-project-template/blob/faef5bbf4f3c392b9e4d71e606edaab5ce4d0aaf/project/lit_autoencoder.py#L54

I suspect this should be used by the model to define the number of hidden dimensions. The other argparse parameter is probably accounted for in the Trainer (me: new to lightning). Thanks for this useful project.

Add torch.hub support like so:

import torch
model = torch.hub.load(
    "pytorchlightning/deep-learning-project-template",
    "lit_classifier",
)