Usage#
Create models and datasets#
Similar to pytorch lightning, we use LightningModule to implement the model and train, val, and test loop, and use LightningDataModule to implement dataset and dataloaders, for details, see model doc and dataset doc
Config optimizers and lr schedulers#
pytorch lightning does not support multi optimizers and lr schedulers from cli, we add this feature, see doc for detail.
Cross-validation#
Set num_folds of the trainer to an int bigger than one to start cross-validation, for details, see doc.
Config files#
See config file structure, deep update, yaml with merge, and json file.
You can get some examples from project_template
CLI#
This project is based on the lightning CLI, so it supports all features from pytorch lightning and lightning CLI, you can get a brief introduction from cli doc.