A PyTorch Lightning project template for structuring deep learning research code to ensure reproducibility and extensibility.
Deep Learning Project Template is a structured seed project for starting new deep learning and machine learning research projects using PyTorch Lightning. It provides a standardized codebase with built-in setup, examples, and tools to ensure work is easily replicable and extendable, addressing the common problem of inconsistent code organization in academic research.
Machine learning researchers, data scientists, and students who are publishing papers or collaborating on deep learning projects and need a consistent starting point for their code.
Developers choose this template because it saves time on project setup, enforces best practices for code organization, and specifically targets the needs of academic research with features like BibTeX support and conference badge integration.
Pytorch Lightning code guideline for conferences
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Includes setup.py and requirements files for quick installation and dependency management, as shown in the straightforward 'How to run' instructions.
Enforces a consistent project layout with an MNIST example, making it easy to share, extend, and replicate work for research collaboration, aligning with its reproducibility goals.
Integrates badges and BibTeX citation support directly in the README, facilitating paper submissions and proper attribution in conferences like NeurIPS or ICLR.
Configured as a Python package, allowing easy imports between modules (e.g., from project.datasets.mnist import mnist), which streamlines development and testing.
Locks users into the PyTorch Lightning ecosystem; projects requiring other frameworks like TensorFlow or vanilla PyTorch without Lightning would need significant modifications.
The standardized structure can be excessive for small scripts or quick experiments, adding complexity where a minimal setup might suffice.
Focuses on basic setup and examples, lacking built-in features for hyperparameter tuning, experiment tracking, or deployment, which researchers often need to add separately.