This book is a comprehensive guide to understanding and utilizing PyTorch 2.0 for deep learning applications. It starts with an introduction to PyTorch, its various advantages over other deep learning frameworks, and its blend with CUDA for GPU acceleration. We delve into the heart of PyTorch – tensors, learning their different types, properties, and operations. Through step-by-step examples, the reader learns to perform basic arithmetic operations on tensors, manipulate them, and understand errors related to tensor shapes.

A substantial portion of the book is dedicated to illustrating how to build simple PyTorch models. This includes uploading and preparing datasets, defining the architecture, training, and predicting. It provides hands-on exercises with a real-world dataset. The book then dives into exploring PyTorch's nn module and gives a detailed comparison of different types of networks like Feedforward, RNN, GRU, CNN, and their combination.

Further, the book delves into understanding the training process and PyTorch's optim module. It explores the overview of optimization algorithms like Gradient Descent, SGD, Mini-batch Gradient Descent, Momentum, Adagrad, and Adam. A separate chapter focuses on advanced concepts in PyTorch 2.0, like model serialization, optimization, distributed training, and PyTorch Quantization API.

In the final chapters, the book discusses the differences between TensorFlow 2.0 and PyTorch 2.0 and the step-by-step process of migrating a TensorFlow model to PyTorch 2.0 using ONNX. It provides an overview of common issues encountered during this process and how to resolve them.

Key Learnings

• A comprehensive introduction to PyTorch and CUDA for deep learning.
• Detailed understanding and operations on PyTorch tensors.
• Step-by-step guide to building simple PyTorch models.
• Insight into PyTorch's nn module and comparison of various network types.
• Overview of the training process and exploration of PyTorch's optim module.
• Understanding advanced concepts in PyTorch like model serialization and optimization.
• Knowledge of distributed training in PyTorch.
• Practical guide to using PyTorch's Quantization API.
• Differences between TensorFlow 2.0 and PyTorch 2.0.
• Guidance on migrating TensorFlow models to PyTorch using ONNX.

Table of Content

1. Introduction to Pytorch 2.0 and CUDA 11.8
2. Getting Started with Tensors
3. Advanced Tensors Operations
4. Building Neural Networks with PyTorch 2.0
5. Training Neural Networks in PyTorch 2.0
6. PyTorch 2.0 Advanced
7. Migrating from TensorFlow to PyTorch 2.0
8. End-to-End PyTorch Regression Model

Audience

A perfect and skillful book for every machine learning engineer, data scientist, AI engineer and data researcher who are passionately looking towards drawing actionable intelligence using PyTorch 2.0. Knowing Python and the basics of deep learning is all you need to sail through this book.

"Hands-On Multi-Cloud Kubernetes" is an essential guide for anyone looking to understand Kubernetes and how it can be used to manage multi-cloud infrastructure. With eight comprehensive chapters, this book provides hands-on experience in setting up Kubernetes clusters, administering deployments and updates, and working with AWS and GCP tools.

Readers will learn to work with various powerful tools, including Helm, FluxCD, Virtual Kubelet, Submariner, and KubeFed. With GitOps principles and workflows, they will practice continuous delivery and learn to manage secrets and config maps. They will build and deploy serverless clusters using Virtual Kubelet and learn to scale them across multiple cloud environments. They will even be introduced to cross-cluster networking with Submariner, where they will learn to perform service discovery, load balancing, and monitor networking metrics.

Managing multi-cluster Kubernetes can be a daunting task, but with KubeFed, readers will gain the skills necessary to set up and deploy multicluster federations, making it easier than ever to administer their own infrastructure. And with multi-cloud CI/CD pipelines using Jenkins, they will perform end-to-end multi-cloud operations, ensuring their code is delivered quickly and efficiently.

Finally, the book covers security in Kubernetes, giving readers the tools and knowledge to configure RBAC, Kubernetes network policies, and secure data over Kubernetes clusters. They will even learn to use Open Policy Agent to manage compliance, ensuring that their infrastructure is powerful and secure.

• Learn Multi-cloud Kubernetes from fundamentals to advanced concepts and tools
• Setting up and managing Kubernetes clusters on multi-cloud infrastructure
• Working with powerful tools like Helm, FluxCD, and Virtual Kubelet
• Utilize Submariner for cross-cluster networking, service discovery, and load balancing
• CI/CD pipelines with Jenkins for end-to-end multi-cloud operations
• Practice GitOps principles and workflows for continuous delivery
• Building and deploying serverless clusters using Virtual Kubelet
• Managing multiple Kubernetes clusters as a single entity with KubeFed
• Security in Kubernetes with RBAC, network policies, and Open Policy Agent

1. Introduction to Multi-cloud Kubernetes
2. Kubernetes Cluster Management and Deployment
3. Using FluxCD
4. Virtual Kubelet and Serverless Clusters
5. Networking with Submariner
6. MultiCluster Management and Federation
7. Multi-cloud CI/CD Pipelines
8. Security in Multi-cloud Kubernetes

This book is ideal for cloud professionals, the DevOps team, Kubernetes developers, and networking professionals to explore multi-cloud networking, working with multi clusters, deploying Kubernetes, and getting skilled with various innovative Kubernetes tools. Knowing cloud networking or Kubernetes is sufficient to begin with the book.