https://github.com/lightning-ai/deep-learning-project-template
Pytorch Lightning code guideline for conferences
https://github.com/lightning-ai/deep-learning-project-template
Last synced: 12 months ago
JSON representation
Pytorch Lightning code guideline for conferences
- Host: GitHub
- URL: https://github.com/lightning-ai/deep-learning-project-template
- Owner: Lightning-AI
- License: apache-2.0
- Created: 2019-08-23T16:50:05.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-09-20T12:05:43.000Z (over 2 years ago)
- Last Synced: 2025-05-16T03:05:24.813Z (12 months ago)
- Language: Python
- Homepage:
- Size: 78.1 KB
- Stars: 1,264
- Watchers: 18
- Forks: 277
- Open Issues: 14
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
### Deep learning project seed
Use this seed to start new deep learning / ML projects.
- Built in setup.py
- Built in requirements
- Examples with MNIST
- Badges
- Bibtex
#### Goals
The goal of this seed is to structure ML paper-code the same so that work can easily be extended and replicated.
### DELETE EVERYTHING ABOVE FOR YOUR PROJECT
---
# Your Project Name
[](https://www.nature.com/articles/nature14539)
[](https://papers.nips.cc/book/advances-in-neural-information-processing-systems-31-2018)
[](https://papers.nips.cc/book/advances-in-neural-information-processing-systems-31-2018)
[](https://papers.nips.cc/book/advances-in-neural-information-processing-systems-31-2018)

## Description
What it does
## How to run
First, install dependencies
```bash
# 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.
```bash
# 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:
```python
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}
}
```