Recap: Scalable AI Models with PyTorch Lightning

1. Recap: Scalable AI Models with PyTorch Lightning

Well done, you've completed "Scalable AI Models with PyTorch Lightning"! You've successfully navigated essential methods and best practices in scalable AI, significantly enhancing your ability to build efficient models with PyTorch Lightning.

2. Building scalable models with PyTorch Lightning

Throughout this chapter, you gained expertise in architecting structured neural networks, developed a clear understanding of training procedures, and effectively used LightningModule for creating streamlined AI solutions.

3. Advanced techniques in PyTorch Lightning

You delved deeper into sophisticated data handling strategies, learned vital methods for assessing model performance through validation and testing, and refined your models using powerful Lightning callbacks.

4. Optimizing models for scalability

You explored critical optimization strategies including quantizing models for faster inference, pruning to reduce complexity, and exporting your models efficiently using TorchScript, ensuring readiness for real-world applications.

5. Congratulations!

Congratulations again on reaching this milestone! You now have a solid foundation in scalable AI design, ready to tackle future challenges. Keep pushing forward, applying these skills, and never stop exploring and innovating!

This exercise is part of the course

Scalable AI Models with PyTorch Lightning

IntermediateSkill Level

5.0+

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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 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

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