Model checkpointing is a technique used during the training of neural networks to save the model's state at specific points, usually at the end of each epoch or after a certain amount of iterations. This allows the model to be restored from the saved state in case training is interrupted or to resume training with the best performing model.
For example, in cases of system failure or time limitations, checkpointing ensures that the model does not need to start training from scratch. Additionally, it is useful to keep the best version of the model, based on validation performance, for later evaluation or deployment.
Frameworks like TensorFlow and PyTorch offer built-in methods to save checkpoints during training, making it easier to implement this technique.