Using callbacks – EarlyStopping
Callbacks in PyTorch Lightning are reusable components that allow you to inject custom behavior into various stages of the training, validation, and testing loops. They offer a way to encapsulate functionalities separate from the main training logic, providing a modular and extensible approach to manage auxiliary tasks such as logging metrics, saving checkpoints, early stopping, and more.
By defining a custom class that inherits from PyTorch Lightning’s base Callback class, you can override specific methods corresponding to different points in the training process, such as on_epoch_start or on_batch_end. When a trainer is initialized with one or more of these callback objects, the defined behavior is automatically executed at the corresponding stage of the training process. This makes callbacks powerful tools for organizing the training pipeline, adding flexibility without cluttering the main training code.
Getting ready
After...
