Deep Learning with PyTorch Lightning: Swiftly build high-performance Artificial Intelligence (AI) models using Python

Sawarkar, Kunal



Build, train, deploy, and scale deep learning models quickly and accurately, improving your productivity using the lightweight PyTorch Wrapper

Key Features:

  • Become well-versed with PyTorch Lightning architecture and learn how it can be implemented in various industry domains
  • Speed up your research using PyTorch Lightning by creating new loss functions, networks, and architectures
  • Train and build new algorithms for massive data using distributed training

Book Description:

PyTorch Lightning lets researchers build their own Deep Learning (DL) models without having to worry about the boilerplate. With the help of this book, you'll be able to maximize productivity for DL projects while ensuring full flexibility from model formulation through to implementation. You'll take a hands-on approach to implementing PyTorch Lightning models to get up to speed in no time.

You'll start by learning how to configure PyTorch Lightning on a cloud platform, understand the architectural components, and explore how they are configured to build various industry solutions. Next, you'll build a network and application from scratch and see how you can expand it based on your specific needs, beyond what the framework can provide. The book also demonstrates how to implement out-of-box capabilities to build and train Self-Supervised Learning, semi-supervised learning, and time series models using PyTorch Lightning. As you advance, you'll discover how generative adversarial networks (GANs) work. Finally, you'll work with deployment-ready applications, focusing on faster performance and scaling, model scoring on massive volumes of data, and model debugging.

By the end of this PyTorch book, you'll have developed the knowledge and skills necessary to build and deploy your own scalable DL applications using PyTorch Lightning.

What You Will Learn:

  • Customize models that are built for different datasets, model architectures, and optimizers
  • Understand how a variety of Deep Learning models from image recognition and time series to GANs, semi-supervised and self-supervised models can be built
  • Use out-of-the-box model architectures and pre-trained models using transfer learning
  • Run and tune DL models in a multi-GPU environment using mixed-mode precisions
  • Explore techniques for model scoring on massive workloads
  • Discover troubleshooting techniques while debugging DL models

Who this book is for:

This deep learning book is for citizen data scientists and expert data scientists transitioning from other frameworks to PyTorch Lightning. This book will also be useful for deep learning researchers who are just getting started with coding for deep learning models using PyTorch Lightning. Working knowledge of Python programming and an intermediate-level understanding of statistics and deep learning fundamentals is expected.


建立、訓練、部署和擴展深度學習模型,快速且準確地提高生產力,使用輕量級的PyTorch Wrapper。

- 熟悉PyTorch Lightning架構,並學習如何在各種行業領域中實施
- 使用PyTorch Lightning加速研究,創建新的損失函數、網絡和架構
- 使用分佈式訓練對大量數據進行訓練和構建新的算法

PyTorch Lightning讓研究人員能夠建立自己的深度學習模型,而不必擔心繁瑣的工作。通過本書的幫助,您將能夠在DL項目中提高生產力,同時確保從模型制定到實施的完全靈活性。您將通過實踐來實現PyTorch Lightning模型,並迅速上手。

您將首先學習如何在雲平台上配置PyTorch Lightning,了解其架構組件,並探索如何配置它們以構建各種行業解決方案。接下來,您將從頭開始構建網絡和應用程序,並了解如何根據特定需求擴展它們,超越框架所能提供的功能。本書還演示了如何使用PyTorch Lightning實現開箱即用的功能,以構建和訓練自監督學習、半監督學習和時間序列模型。隨著您的進一步學習,您將了解生成對抗網絡(GAN)的工作原理。最後,您將使用部署就緒的應用程序,重點放在更快的性能和擴展性上,對大量數據進行模型評分和調試。

通過閱讀本書,您將掌握構建和部署自己的可擴展DL應用程序所需的知識和技能,使用PyTorch Lightning。

- 自定義針對不同數據集、模型架構和優化器的模型
- 了解如何構建各種深度學習模型,從圖像識別和時間序列到GAN、半監督和自監督模型
- 使用開箱即用的模型架構和預訓練模型,使用遷移學習
- 在多GPU環境中運行和調整DL模型,使用混合模式精度
- 探索在大量工作負載上進行模型評分的技術
- 在調試DL模型時發現故障排除技巧

本書適合公民數據科學家和從其他框架轉換到PyTorch Lightning的專業數據科學家。對Python編程有一定的工作知識,並對統計學和深度學習基礎有中級水平的理解。