1. Implementing training logic
So far, we've built our model using LightningModule. In this video, we'll bring it all together by training our classifier.
2. Defining the training step
We'll start with the training step. The model unpacks a batch, runs a forward pass to get predictions, and uses PyTorch’s cross_entropy to calculate how well predictions match labels. Finally, we log the loss for performance tracking.
This method will run over batches in the training loop, updating the model to improve performance.
The log method, inherited from LightningModule, enables metric tracking using Lightning’s built-in logging.
3. Configuring optimizers
Next, we configure the optimizer. We select an optimizer—in our example, Adam—ensuring it is set up to update the model's parameters. We specify a learning rate to control the update magnitude, and return the optimizer object.
4. Training with Lightning Trainer
Let's examine how the Lightning Trainer executes the training process. The Trainer handles training across epochs and logs metrics automatically. The diagram shows this workflow: loading data, computing loss, updating weights, and logging metrics. Think of it as a choreographer guiding each step precisely.
5. Using trainer.fit and trainer.validate
Let's briefly cover the essential methods for executing training and validation. Using trainer.fit, we start the training loop with our model and training data loader, and then trainer.validate runs the validation loop to evaluate performance on the validation data. This automation simplifies the process, ensuring that both training and validation metrics are monitored in real time.
6. Complete training logic example
Let's review a full example that brings everything together in a practical way. Imagine you're setting up a simple classifier—here, you define a custom LightningModule, craft a training_step to compute and log the loss, and configure the optimizer to update the model parameters. The code snippet shows how these elements work in unison with the Lightning Trainer, creating a smooth and efficient training pipeline. This practical blueprint allows you to experiment and extend, paving the way for more advanced PyTorch Lightning projects.
It’s important to note that method names like forward, training_step, and configure_optimizers must be named exactly as shown—these are special hooks that PyTorch Lightning’s Trainer recognizes and calls automatically during training and validation.
Enjoy customizing your models!
7. Industry applications
In this video we explored the training step, and how efficient loss tracking and optimized training pipelines ensure models meet high-quality standards. It has real-world implementation possibilities, for example: In healthcare, they enhance diagnostic imaging;
8. Industry applications
in finance, they enable real-time fraud detection. Well-optimized pipelines boost model accuracy and reliability, supporting the shift from experimental to production-grade solutions.
9. Let's practice!
It's time to put theory into practice. Happy coding and see you next time!