{"id":15028664,"url":"https://github.com/lightning-ai/deep-learning-project-template","last_synced_at":"2025-05-16T03:05:30.630Z","repository":{"id":39538536,"uuid":"204037017","full_name":"Lightning-AI/deep-learning-project-template","owner":"Lightning-AI","description":"Pytorch Lightning code guideline for 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Deep learning project seed\nUse this seed to start new deep learning / ML projects.\n\n- Built in setup.py\n- Built in requirements\n- Examples with MNIST\n- Badges\n- Bibtex\n\n#### Goals  \nThe goal of this seed is to structure ML paper-code the same so that work can easily be extended and replicated.   \n\n### DELETE EVERYTHING ABOVE FOR YOUR PROJECT  \n \n---\n\n\u003cdiv align=\"center\"\u003e    \n \n# Your Project Name     \n\n[![Paper](http://img.shields.io/badge/paper-arxiv.1001.2234-B31B1B.svg)](https://www.nature.com/articles/nature14539)\n[![Conference](http://img.shields.io/badge/NeurIPS-2019-4b44ce.svg)](https://papers.nips.cc/book/advances-in-neural-information-processing-systems-31-2018)\n[![Conference](http://img.shields.io/badge/ICLR-2019-4b44ce.svg)](https://papers.nips.cc/book/advances-in-neural-information-processing-systems-31-2018)\n[![Conference](http://img.shields.io/badge/AnyConference-year-4b44ce.svg)](https://papers.nips.cc/book/advances-in-neural-information-processing-systems-31-2018)  \n\u003c!--\nARXIV   \n[![Paper](http://img.shields.io/badge/arxiv-math.co:1480.1111-B31B1B.svg)](https://www.nature.com/articles/nature14539)\n--\u003e\n![CI testing](https://github.com/PyTorchLightning/deep-learning-project-template/workflows/CI%20testing/badge.svg?branch=master\u0026event=push)\n\n\n\u003c!--  \nConference   \n--\u003e   \n\u003c/div\u003e\n \n## Description   \nWhat it does   \n\n## How to run   \nFirst, install dependencies   \n```bash\n# clone project   \ngit clone https://github.com/YourGithubName/deep-learning-project-template\n\n# install project   \ncd deep-learning-project-template \npip install -e .   \npip install -r requirements.txt\n ```   \n Next, navigate to any file and run it.   \n ```bash\n# module folder\ncd project\n\n# run module (example: mnist as your main contribution)   \npython lit_classifier_main.py    \n```\n\n## Imports\nThis project is setup as a package which means you can now easily import any file into any other file like so:\n```python\nfrom project.datasets.mnist import mnist\nfrom project.lit_classifier_main import LitClassifier\nfrom pytorch_lightning import Trainer\n\n# model\nmodel = LitClassifier()\n\n# data\ntrain, val, test = mnist()\n\n# train\ntrainer = Trainer()\ntrainer.fit(model, train, val)\n\n# test using the best model!\ntrainer.test(test_dataloaders=test)\n```\n\n### Citation   \n```\n@article{YourName,\n  title={Your Title},\n  author={Your team},\n  journal={Location},\n  year={Year}\n}\n```   \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flightning-ai%2Fdeep-learning-project-template","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flightning-ai%2Fdeep-learning-project-template","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flightning-ai%2Fdeep-learning-project-template/lists"}