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.

Introduction to PyTorch Lightning

Introducing the  LightningModule

Running the Lightning Trainer

Defining models with LightningModule

Usage of the LightningModule

Mastering the init method

Perfecting the forward method

Implementing training logic

Implementing the training step

Configuring the optimizer

Training and evaluating

Building Scalable Models with PyTorch Lightning

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.

Managing data with LightningDataModule

Splitting data with LightningDataModule

Creating a train DataLoader

Incorporating validation and testing

Implementing the validation step

Evaluate model accuracy using Torchmetrics

Enhancing training with Lightning callbacks

Options

ModelCheckpoint

EarlyStopping

Classifying Lightning callbacks

Optimizing model training with Lightning

Advanced Techniques in PyTorch 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

Applying dynamic quantization

Apply dynamic quantization

Comparing quantized model performance

Implementing model pruning techniques

Apply pruning to a linear layer

Finalize pruning by removing the mask

Exporting models with TorchScript

Choosing the right conversion method

Optimizing models for scalability

Recap: Scalable AI Models with PyTorch Lightning

Optimizing Models for Scalability

In this course, you'll explore the principles, tools, and techniques for building scalable AI systems. Using PyTorch Lightning as the primary framework, you'll get hands-on experience in developing and optimizing AI models by building modular neural network components, managing data pipelines, and applying advanced optimization techniques. By the end of the course, you will be equipped with the skills needed to build, scale, and deploy robust AI solutions.

<h2>可扩展 AI 基础</h2> 
这门课程将带你踏上可扩展 AI 基础知识之旅。 你将首先学习 PyTorch Lightning 如何通过减少样板代码来简化模型开发生命周期。 通过引导式示例，你将了解如何将复杂的神经网络拆分为可复用的组件，从而即使项目规模不断扩大，也能保持代码质量。
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<h2>高级优化技术</h2> 
你还将掌握优化技术，例如自适应优化器、模型剪枝和量化。 您将亲眼看到训练策略中的微小变化如何带来速度和准确率的显著提升，并学习如何优化训练循环以消除瓶颈。
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<h2>生产就绪部署</h2> 
到课程结束时，您将掌握将原型一路推进到生产环境的技能，并拥有一套模块化、经过优化且可部署的 AI 解决方案作品集，随时准备应对现实世界的挑战。







Intermediate Deep Learning with PyTorch

通过构建模块化模型并掌握PyTorch Lightning的高级优化，简化您的AI项目！

Scalable AI Models with PyTorch Lightning

用 PyTorch Lightning 构建模块化模型并掌握高级优化，简化你的 AI 项目！

开发大型语言模型

Saves model weights to file when a new best score is achieved.

Optionally keeps the top K checkpoints and discards the rest.

Stops training to avoid overfitting when improvements stall.

Uses a patience parameter to allow a few more epochs of training after the last improvement.

Classifying Lightning callbacks

Scalable AI Models with PyTorch Lightning

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