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 files

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.

See config file structure, deep update, yaml with merge, and json file for more details.

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.

Wandb Logger

We support logging your code with WandbNamedLogger to control the version of your codes for every experiment. To use Wandb, you have to create an account on their site and login following their doc.