Publisher: Packt Publishing
Distributed Computing with Go. Practical concurrency and parallelism for Go applications
V.N. Nikhil Anurag
Distributed Computing with Go gives developers with a good idea how basic Go development works the tools to fulfill the true potential of Golang development in a world of concurrent web and cloud applications. Nikhil starts out by setting up a professional Go development environment. Then you’ll learn the basic concepts and practices of Golang concurrent and parallel development. You’ll find out in the new few chapters how to balance resources and data with REST and standard web approaches while keeping concurrency in mind. Most Go applications these days will run in a data center or on the cloud, which is a condition upon which the next chapter depends. There, you’ll expand your skills considerably by writing a distributed document indexing system during the next two chapters. This system has to balance a large corpus of documents with considerable analytical demands. Another use case is the way in which a web application written in Go can be consciously redesigned to take distributed features into account. The chapter is rather interesting for Go developers who have to migrate existing Go applications to computationally and memory-intensive environments. The final chapter relates to the rather onerous task of testing parallel and distributed applications, something that is not usually taught in standard computer science curricula.
Rasheedh B, Francesco Pierfederici
CPU-intensive data processing tasks have become crucial considering the complexity of the various big data applications that are used today. Reducing the CPU utilization per process is very important to improve the overall speed of applications.This book will teach you how to perform parallel execution of computations by distributing them across multiple processors in a single machine, thus improving the overall performance of a big data processing task. We will cover synchronous and asynchronous models, shared memory and file systems, communication between various processes, synchronization, and more.
Distributed Data Systems with Azure Databricks. Create, deploy, and manage enterprise data pipelines
Alan Bernardo Palacio
Microsoft Azure Databricks helps you to harness the power of distributed computing and apply it to create robust data pipelines, along with training and deploying machine learning and deep learning models. Databricks' advanced features enable developers to process, transform, and explore data. Distributed Data Systems with Azure Databricks will help you to put your knowledge of Databricks to work to create big data pipelines. The book provides a hands-on approach to implementing Azure Databricks and its associated methodologies that will make you productive in no time. Complete with detailed explanations of essential concepts, practical examples, and self-assessment questions, you’ll begin with a quick introduction to Databricks core functionalities, before performing distributed model training and inference using TensorFlow and Spark MLlib. As you advance, you’ll explore MLflow Model Serving on Azure Databricks and implement distributed training pipelines using HorovodRunner in Databricks. Finally, you’ll discover how to transform, use, and obtain insights from massive amounts of data to train predictive models and create entire fully working data pipelines. By the end of this MS Azure book, you’ll have gained a solid understanding of how to work with Databricks to create and manage an entire big data pipeline.
Guanhua Wang
Reducing time cost in machine learning leads to a shorter waiting time for model training and a faster model updating cycle. Distributed machine learning enables machine learning practitioners to shorten model training and inference time by orders of magnitude. With the help of this practical guide, you'll be able to put your Python development knowledge to work to get up and running with the implementation of distributed machine learning, including multi-node machine learning systems, in no time. You'll begin by exploring how distributed systems work in the machine learning area and how distributed machine learning is applied to state-of-the-art deep learning models. As you advance, you'll see how to use distributed systems to enhance machine learning model training and serving speed. You'll also get to grips with applying data parallel and model parallel approaches before optimizing the in-parallel model training and serving pipeline in local clusters or cloud environments. By the end of this book, you'll have gained the knowledge and skills needed to build and deploy an efficient data processing pipeline for machine learning model training and inference in a distributed manner.
Bhupesh Guptha Muthiyalu, Suneel Kumar Kunani
Building distributed applications in this modern era can be a tedious task as customers expect high availability, high performance, and improved resilience. With the help of this book, you'll discover how you can harness the power of Microsoft Orleans to build impressive distributed applications.Distributed .NET with Microsoft Orleans will demonstrate how to leverage Orleans to build highly scalable distributed applications step by step in the least possible time and with minimum effort. You'll explore some of the key concepts of Microsoft Orleans, including the Orleans programming model, runtime, virtual actors, hosting, and deployment. As you advance, you'll become well-versed with important Orleans assets such as grains, silos, timers, and persistence. Throughout the book, you'll create a distributed application by adding key components to the application as you progress through each chapter and explore them in detail.By the end of this book, you'll have developed the confidence and skills required to build distributed applications using Microsoft Orleans and deploy them in Microsoft Azure.
Jeremiah Ginn, David H. Brown
The SASE concept was coined by Gartner after seeing a pattern emerge in cloud and SD-WAN projects where full security integration was needed. The market behavior lately has sparked something like a space race for all technology manufacturers and cloud service providers to offer a SASE solution. The current training available in the market is minimal and manufacturer-oriented, with new services being released every few weeks. Professional architects and engineers trying to implement SASE need to take a manufacturer-neutral approach.This guide provides a foundation for understanding SASE, but it also has a lasting impact because it not only addresses the problems that existed at the time of publication, but also provides a continual learning approach to successfully lead in a market that evolves every few weeks. Technology teams need a tool that provides a model to keep up with new information as it becomes available and stay ahead of market hype.With this book, you’ll learn about crucial models for SASE success in designing, building, deploying, and supporting operations to ensure the most positive user experience (UX). In addition to SASE, you’ll gain insight into SD-WAN design, DevOps, zero trust, and next-generation technical education methods.
Miguel Angel Garcia-Ruiz, Pedro Cesar Santana Mancilla
We live in a world surrounded by electronic devices, and microcontrollers are the brains of these devices. Microcontroller programming is an essential skill in the era of the Internet of Things (IoT), and this book helps you to get up to speed with it by working through projects for designing and developing embedded apps with microcontroller boards.DIY Microcontroller Projects for Hobbyists are filled with microcontroller programming C and C++ language constructs. You'll discover how to use the Blue Pill (containing a type of STM32 microcontroller) and Curiosity Nano (containing a type of PIC microcontroller) boards for executing your projects as PIC is a beginner-level board and STM-32 is an ARM Cortex-based board. Later, you'll explore the fundamentals of digital electronics and microcontroller board programming. The book uses examples such as measuring humidity and temperature in an environment to help you gain hands-on project experience. You'll build on your knowledge as you create IoT projects by applying more complex sensors. Finally, you'll find out how to plan for a microcontroller-based project and troubleshoot it.By the end of this book, you'll have developed a firm foundation in electronics and practical PIC and STM32 microcontroller programming and interfacing, adding valuable skills to your professional portfolio.
