Perfecting the forward method

After setting up layers in the __init__ method, the forward method dictates how data flows through them. In PyTorch Lightning, this separation keeps your code clean and easy to maintain. You've already seen how to structure the constructor-now it's time to focus on the forward pass, ensuring your classification logic is clear and optimized for training. Here, the layers in __init__ are already defined for you, so you can concentrate purely on the forward flow.

The lightning.pytorch and torch.nn have already been imported as pl and nn.

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Scalable AI Models with PyTorch Lightning

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Istruzioni dell'esercizio

Implement the forward method inside ClassifierModel.
Apply a ReLU activation after the hidden layer.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

class ClassifierModel(pl.LightningModule):
  
    def __init__(self, input_dim, hidden_dim, output_dim):
        super().__init__()
        self.hidden = nn.Linear(input_dim, hidden_dim)
        self.output = nn.Linear(hidden_dim, output_dim)
        
    # Define forward method
    def ____(self, ____):
        # Complete the forward pass
        x = self.hidden(x)
        x = ____(x)
        x = self.output(x)
        return x

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Questo esercizio fa parte del corso

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 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

Esercizio in corso

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