Introducing the LightningModule

Get ready to build your first LightningModule! In this hands-on exercise, you'll set up the core structure of a classification workflow. You'll define a linear layer, pass data through it in the forward method, and compute the loss in the training step. This clean structure gives you a solid foundation to start experimenting with your models.

The torch and lightning.pytorch, imported as pl, have been preloaded for you.

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Scalable AI Models with PyTorch Lightning

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Oefeninstructies

Define a class LightModel that inherits from pl.LightningModule.
Define a linear layer to transform your input, assuming the input features are 16 and there are 10 output classes.

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# Define the model class
class LightModel(____):
  	# Define a linear layer to transform your input
    def __init__(self):
        super().__init__()
        self.layer = ____
    def forward(self, x):
        return self.layer(x)
    def training_step(self, batch, batch_idx):
        x, y = batch
        logits = self(x)
        loss = torch.nn.functional.cross_entropy(logits, y)
        return loss

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Scalable AI Models with PyTorch Lightning

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In this chapter, we'll explore how PyTorch Lightning simplifies the development and deployment of scalable AI models. Starting with foundational concepts, we'll go through the core structure of a PyTorch Lightning project, including essential components like the LightningModule and Trainer, to set a strong foundation for more advanced AI solutions.

Exercise 1: Introduction to PyTorch Lightning Exercise 2: Introducing the LightningModule

Huidige oefening

Exercise 3: Running the Lightning Trainer Exercise 4: Defining models with LightningModule Exercise 5: Usage of the LightningModule Exercise 6: Mastering the init method Exercise 7: Perfecting the forward method Exercise 8: Implementing training logic Exercise 9: Implementing the training step Exercise 10: Configuring the optimizer Exercise 11: Training and evaluating

We'll dive deeper into PyTorch Lightning to efficiently manage data and refine model training in this chapter. We'll learn how to create modular and reusable data workflows with LightningDataModule, evaluate your models accurately through validation and testing, and enhance training processes using Lightning Callbacks to automate model improvement and avoid overfitting.

Exercise 1: Managing data with LightningDataModule Exercise 2: Splitting data with LightningDataModule Exercise 3: Creating a train DataLoader Exercise 4: Incorporating validation and testing Exercise 5: Implementing the validation step Exercise 6: Evaluate model accuracy using Torchmetrics Exercise 7: Enhancing training with Lightning callbacks Exercise 8: Classifying Lightning callbacks Exercise 9: Optimizing model training with Lightning

Learn to prepare deep learning models for real-world deployment by making them leaner and faster. This chapter introduces techniques such as dynamic quantization, pruning, and TorchScript conversion, helping you reduce model size and latency without sacrificing accuracy

Exercise 1: Applying dynamic quantization Exercise 2: Apply dynamic quantization Exercise 3: Comparing quantized model performance Exercise 4: Implementing model pruning techniques Exercise 5: Apply pruning to a linear layer Exercise 6: Finalize pruning by removing the mask Exercise 7: Exporting models with TorchScript Exercise 8: Choosing the right conversion method Exercise 9: Optimizing models for scalability Exercise 10: Recap: Scalable AI Models with PyTorch Lightning