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Boilerplate code for deep learning projects with PyTorch Lightning

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Boilerplate for Deep Learning with Pytorch Lightning

Zach Stoebner

Boilerplate code for building deep learning projects with PyTorch Lightning.

Setup

It is recommended to create a virtual environment for each project.

Requirements:

Usage

The main components are a dataset, a model, and a loss. Each module has abstract base classes to define those for a project. Basic classes are provided for each module, as well as an example config and a shell script to easily run configs.

Either clone the repo or use it as a submodule.

References

See inrlib for more example usage.

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Boilerplate code for deep learning projects with PyTorch Lightning

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