Training Patterns

Structure a PyTorch Lightning module by defining the model architecture within a LightningModule, then configure logging and optimizers in dedicated methods. This pattern ensures a clean, maintainable training loop with proper gradient handling and checkpointing.

Set up HuggingFace Trainer by configuring custom metric computation callbacks and early stopping parameters. This approach provides a production-ready training loop that monitors validation metrics and halts training when performance stops improving.

Find the optimal learning rate using an LR finder tool, then implement a warmup schedule that gradually increases the rate before decay. This technique stabilizes early training phases and prevents loss divergence in deep learning models.

Monitor gradient norms by tracking them during the training step and apply gradient clipping to limit their maximum value. This strategy effectively prevents exploding gradients and stabilizes the training of deep transformer models.

Resume model training from a checkpoint by loading the saved model state, optimizer configuration, and step count. This reliable method ensures training continues seamlessly from interruptions without restarting the learning rate schedule.

No specific dependencies are required to access the training patterns. The Skill provides reference implementations for PyTorch Lightning and HuggingFace Trainer workflows, which you apply within your existing deep learning environment.