Download or read book Automating Workflows with GitHub Actions written by Priscila Heller and published by Packt Publishing Ltd. This book was released on 2021-11-11 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build, test, and deploy code right from your GitHub repository by automating, customizing, and executing software development workflows with GitHub Actions Key FeaturesEnhance your CI/CD and DevOps workflows using GitHub ActionsDiscover how to create custom GitHub Actions using Docker and JavaScriptGet up and running with building a CI/CD pipeline effectivelyBook Description GitHub Actions is one of the most popular products that enables you to automate development tasks and improve your software development workflow. Automating Workflows with GitHub Actions uses real-world examples to help you automate everyday tasks and use your resources efficiently. This book takes a practical approach to helping you develop the skills needed to create complex YAML files to automate your daily tasks. You'll learn how to find and use existing workflows, allowing you to get started with GitHub Actions right away. Moving on, you'll discover complex concepts and practices such as self-hosted runners and writing workflow files that leverage other platforms such as Docker as well as programming languages such as Java and JavaScript. As you advance, you'll be able to write your own JavaScript, Docker, and composite run steps actions, and publish them in GitHub Marketplace! You'll also find instructions to migrate your existing CI/CD workflows into GitHub Actions from platforms like Travis CI and GitLab. Finally, you'll explore tools that'll help you stay informed of additions to GitHub Actions along with finding technical support and staying engaged with the community. By the end of this GitHub book, you'll have developed the skills and experience needed to build and maintain your own CI/CD pipeline using GitHub Actions. What you will learnGet to grips with the basics of GitHub and the YAML syntaxUnderstand key concepts of GitHub ActionsFind out how to write actions for JavaScript and Docker environmentsDiscover how to create a self-hosted runnerMigrate from other continuous integration and continuous delivery (CI/CD) platforms to GitHub ActionsCollaborate with the GitHub Actions community and find technical help to navigate technical difficultiesPublish your workflows in GitHub MarketplaceWho this book is for This book is for anyone involved in the software development life cycle, for those looking to learn about GitHub Actions and what can be accomplished, and for those who want to develop a new skill to help them advance their software development career. If you are new to GitHub and GitHub Actions in general, then this book is for you. Basic knowledge of GitHub as a platform will help you to get the most out of this book.

Download or read book Learning GitHub Actions written by Brent Laster and published by "O'Reilly Media, Inc.". This book was released on 2023-08-17 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automate your software development processes with GitHub Actions, the continuous integration and continuous delivery platform that integrates seamlessly with GitHub. With this practical book, open source author, trainer, and DevOps director Brent Laster explains everything you need to know about using and getting value from GitHub Actions. You'll learn what actions and workflows are and how they can be used, created, and incorporated into your processes to simplify, standardize, and automate your work in GitHub. This book explains the platform, components, use cases, implementation, and integration points of actions, so you can leverage them to provide the functionality and features needed in today's complex pipelines and software development processes. You'll learn how to design and implement automated workflows that respond to common events like pushes, pull requests, and review updates. You'll understand how to use the components of the GitHub Actions platform to gain maximum automation and benefit. With this book, you will: Learn what GitHub Actions are, the various use cases for them, and how to incorporate them into your processes Understand GitHub Actions' structure, syntax, and semantics Automate processes and implement functionality Create your own custom actions with Docker, JavaScript, or shell approachesTroubleshoot and debug workflows that use actions Combine actions with GitHub APIs and other integration options Identify ways to securely implement workflows with GitHub Actions Understand how GitHub Actions compares to other options

Download or read book Machine Learning in Action written by Peter Harrington and published by Simon and Schuster. This book was released on 2012-04-03 with total page 558 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About the Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interestingor useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos Table of Contents PART 1 CLASSIFICATION Machine learning basics Classifying with k-Nearest Neighbors Splitting datasets one feature at a time: decision trees Classifying with probability theory: naïve Bayes Logistic regression Support vector machines Improving classification with the AdaBoost meta algorithm PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION Predicting numeric values: regression Tree-based regression PART 3 UNSUPERVISED LEARNING Grouping unlabeled items using k-means clustering Association analysis with the Apriori algorithm Efficiently finding frequent itemsets with FP-growth PART 4 ADDITIONAL TOOLS Using principal component analysis to simplify data Simplifying data with the singular value decomposition Big data and MapReduce

Download or read book Learning GitHub Actions written by Brent Laster and published by O'Reilly Media. This book was released on 2023-09-29 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automate your software development processes with GitHub Actions, the continuous integration and continuous delivery platform that integrates seamlessly with GitHub. With this practical book, open source author, trainer, and DevOps director Brent Laster explains everything you need to know about using and getting value from GitHub Actions. You'll learn what actions and workflows are and how they can be used, created, and incorporated into your processes to simplify, standardize, and automate your work in GitHub. This book explains the platform, components, use cases, implementation, and integration points of actions, so you can leverage them to provide the functionality and features needed in today's complex pipelines and software development processes. You'll learn how to design and implement automated workflows that respond to common events like pushes, pull requests, and review updates. You'll understand how to use the components of the GitHub Actions platform to gain maximum automation and benefit. With this book, you will: Learn what GitHub Actions are, the various use cases for them, and how to incorporate them into your processes Understand GitHub Actions' structure, syntax, and semantics Automate processes and implement functionality Create your own custom actions with Docker, JavaScript, or shell approaches Troubleshoot and debug workflows that use actions Combine actions with GitHub APIs and other integration options Identify ways to securely implement workflows with GitHub Actions Understand how GitHub Actions compares to other options

Download or read book Deep Reinforcement Learning in Action written by Alexander Zai and published by Manning Publications. This book was released on 2020-04-28 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Deep reinforcement learning AI systems rapidly adapt to new environments, a vast improvement over standard neural networks. A DRL agent learns like people do, taking in raw data such as sensor input and refining its responses and predictions through trial and error. About the book Deep Reinforcement Learning in Action teaches you how to program AI agents that adapt and improve based on direct feedback from their environment. In this example-rich tutorial, you’ll master foundational and advanced DRL techniques by taking on interesting challenges like navigating a maze and playing video games. Along the way, you’ll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym. What's inside Building and training DRL networks The most popular DRL algorithms for learning and problem solving Evolutionary algorithms for curiosity and multi-agent learning All examples available as Jupyter Notebooks About the reader For readers with intermediate skills in Python and deep learning. About the author Alexander Zai is a machine learning engineer at Amazon AI. Brandon Brown is a machine learning and data analysis blogger. Table of Contents PART 1 - FOUNDATIONS 1. What is reinforcement learning? 2. Modeling reinforcement learning problems: Markov decision processes 3. Predicting the best states and actions: Deep Q-networks 4. Learning to pick the best policy: Policy gradient methods 5. Tackling more complex problems with actor-critic methods PART 2 - ABOVE AND BEYOND 6. Alternative optimization methods: Evolutionary algorithms 7. Distributional DQN: Getting the full story 8.Curiosity-driven exploration 9. Multi-agent reinforcement learning 10. Interpretable reinforcement learning: Attention and relational models 11. In conclusion: A review and roadmap

Download or read book Learning Processing written by Daniel Shiffman and published by Newnes. This book was released on 2015-09-09 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning Processing, Second Edition, is a friendly start-up guide to Processing, a free, open-source alternative to expensive software and daunting programming languages. Requiring no previous experience, this book is for the true programming beginner. It teaches the basic building blocks of programming needed to create cutting-edge graphics applications including interactive art, live video processing, and data visualization. Step-by-step examples, thorough explanations, hands-on exercises, and sample code, supports your learning curve. A unique lab-style manual, the book gives graphic and web designers, artists, and illustrators of all stripes a jumpstart on working with the Processing programming environment by providing instruction on the basic principles of the language, followed by careful explanations of select advanced techniques. The book has been developed with a supportive learning experience at its core. From algorithms and data mining to rendering and debugging, it teaches object-oriented programming from the ground up within the fascinating context of interactive visual media. This book is ideal for graphic designers and visual artists without programming background who want to learn programming. It will also appeal to students taking college and graduate courses in interactive media or visual computing, and for self-study. A friendly start-up guide to Processing, a free, open-source alternative to expensive software and daunting programming languages No previous experience required—this book is for the true programming beginner! Step-by-step examples, thorough explanations, hands-on exercises, and sample code supports your learning curve

Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Download or read book GitHub For Dummies written by Sarah Guthals and published by John Wiley & Sons. This book was released on 2023-01-27 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get more out of your coding with GitHub For today’s coders, GitHub is a must. The world’s largest software development platform, GitHub helps developers store, track, and collaborate on software projects. In this easy-to-follow Dummies guide, you’ll find insight into creating repositories, establishing projects, collaborating, incorporating open-source resources, and establishing yourself as a valued member of the GitHub community. With a working knowledge of GitHub, you’ll be a better, more employable programmer. The simple instructions and interactive examples in this book will get you there quickly. Get the instructions you need for using GitHub to collaborate on software projects Become more attractive to employers with knowledge and experience in the largest development platform Set up GitHub Desktop, create a repository, and launch your first project Use GitHub Skills courses to learn new tricks, for beginners to pros You’ve learned how to write a little code—now learn how to share it with GitHub.

Download or read book Deep Learning and the Game of Go written by Kevin Ferguson and published by Simon and Schuster. This book was released on 2019-01-06 with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game. Foreword by Thore Graepel, DeepMind Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The ancient strategy game of Go is an incredible case study for AI. In 2016, a deep learning-based system shocked the Go world by defeating a world champion. Shortly after that, the upgraded AlphaGo Zero crushed the original bot by using deep reinforcement learning to master the game. Now, you can learn those same deep learning techniques by building your own Go bot! About the Book Deep Learning and the Game of Go introduces deep learning by teaching you to build a Go-winning bot. As you progress, you'll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You'll enjoy watching your bot master the game of Go, and along the way, you'll discover how to apply your new deep learning skills to a wide range of other scenarios! What's inside Build and teach a self-improving game AI Enhance classical game AI systems with deep learning Implement neural networks for deep learning About the Reader All you need are basic Python skills and high school-level math. No deep learning experience required. About the Author Max Pumperla and Kevin Ferguson are experienced deep learning specialists skilled in distributed systems and data science. Together, Max and Kevin built the open source bot BetaGo. Table of Contents PART 1 - FOUNDATIONS Toward deep learning: a machine-learning introduction Go as a machine-learning problem Implementing your first Go bot PART 2 - MACHINE LEARNING AND GAME AI Playing games with tree search Getting started with neural networks Designing a neural network for Go data Learning from data: a deep-learning bot Deploying bots in the wild Learning by practice: reinforcement learning Reinforcement learning with policy gradients Reinforcement learning with value methods Reinforcement learning with actor-critic methods PART 3 - GREATER THAN THE SUM OF ITS PARTS AlphaGo: Bringing it all together AlphaGo Zero: Integrating tree search with reinforcement learning

Download or read book Reinforcement Learning second edition written by Richard S. Sutton and published by MIT Press. This book was released on 2018-11-13 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

Download or read book Pragmatic AI written by Noah Gift and published by Addison-Wesley Professional. This book was released on 2018-07-12 with total page 720 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value. Building on these fundamentals, you’ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you’re a business professional, decision-maker, student, or programmer, Gift’s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment. Get and configure all the tools you’ll need Quickly review all the Python you need to start building machine learning applications Master the AI and ML toolchain and project lifecycle Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more Work with Microsoft Azure AI APIs Walk through building six real-world AI applications, from start to finish Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Download or read book GitHub Actions Cookbook written by Michael Kaufmann and published by Packt Publishing Ltd. This book was released on 2024-04-30 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: Authored by a Microsoft Regional Director, this book shows you how to leverage the power of the community-driven GitHub Actions workflow platform to automate repetitive engineering tasks Key Features Automate CI/CD workflows and deploy securely to cloud providers like Azure, AWS, or GCP using OpenID Create your own custom actions with Docker, JavaScript programming, or shell scripts and share them with others Discover ways to automate complex scenarios beyond the basic ones documented in GitHub Book DescriptionSay goodbye to tedious tasks! GitHub Actions is a powerful workflow engine that automates everything in the GitHub ecosystem, letting you focus on what matters most. This book explains the GitHub Actions workflow syntax, the different kinds of actions, and how GitHub-hosted and self-hosted workflow runners work. You’ll get tips on how to author and debug GitHub Actions and workflows with Visual Studio Code (VS Code), run them locally, and leverage the power of GitHub Copilot. The book uses hands-on examples to walk you through real-world use cases that will help you automate the entire release process. You’ll cover everything, from automating the generation of release notes to building and testing your software and deploying securely to Azure, Amazon Web Services (AWS), or Google Cloud using OpenID Connect (OIDC), secrets, variables, environments, and approval checks. The book goes beyond CI/CD by demonstrating recipes to execute IssueOps and automate other repetitive tasks using the GitHub CLI, GitHub APIs and SDKs, and GitHub Token. You’ll learn how to build your own actions and reusable workflows to share building blocks with the community or within your organization. By the end of this GitHub book, you'll have gained the skills you need to automate tasks and work with remarkable efficiency and agility.What you will learn Author and debug GitHub Actions workflows with VS Code and Copilot Run your workflows on GitHub-provided VMs (Linux, Windows, and macOS) or host your own runners in your infrastructure Understand how to secure your workflows with GitHub Actions Boost your productivity by automating workflows using GitHub's powerful tools, such as the CLI, APIs, SDKs, and access tokens Deploy to any cloud and platform in a secure and reliable way with staged or ring-based deployments Who this book is for This book is for anyone looking for a practical approach to learning GitHub Actions, regardless of their experience level. Whether you're a software developer, a DevOps engineer, anyone who has already experimented with Actions, or someone completely new to CI/CD tools like Jenkins or Azure Pipelines, you’ll find expert insights in this book. Basic knowledge of using Git and command lines is a must.

Download or read book Learning Spark written by Jules S. Damji and published by O'Reilly Media. This book was released on 2020-07-16 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data is bigger, arrives faster, and comes in a variety of formats—and it all needs to be processed at scale for analytics or machine learning. But how can you process such varied workloads efficiently? Enter Apache Spark. Updated to include Spark 3.0, this second edition shows data engineers and data scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Through step-by-step walk-throughs, code snippets, and notebooks, you’ll be able to: Learn Python, SQL, Scala, or Java high-level Structured APIs Understand Spark operations and SQL Engine Inspect, tune, and debug Spark operations with Spark configurations and Spark UI Connect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka Perform analytics on batch and streaming data using Structured Streaming Build reliable data pipelines with open source Delta Lake and Spark Develop machine learning pipelines with MLlib and productionize models using MLflow

Download or read book Docker in Practice Second Edition written by Ian Miell and published by Simon and Schuster. This book was released on 2019-02-06 with total page 707 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Docker in Practice, Second Edition presents over 100 practical techniques, hand-picked to help you get the most out of Docker. Following a Problem/Solution/Discussion format, you'll walk through specific examples that you can use immediately, and you'll get expert guidance on techniques that you can apply to a whole range of scenarios. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Docker's simple idea-wrapping an application and its dependencies into a single deployable container-created a buzz in the software industry. Now, containers are essential to enterprise infrastructure, and Docker is the undisputed industry standard. So what do you do after you've mastered the basics? To really streamline your applications and transform your dev process, you need relevant examples and experts who can walk you through them. You need this book. About the Book Docker in Practice, Second Edition teaches you rock-solid, tested Docker techniques, such as replacing VMs, enabling microservices architecture, efficient network modeling, offline productivity, and establishing a container-driven continuous delivery process. Following a cookbook-style problem/solution format, you'll explore real-world use cases and learn how to apply the lessons to your own dev projects. What's inside Continuous integration and delivery The Kubernetes orchestration tool Streamlining your cloud workflow Docker in swarm mode Emerging best practices and techniques About the Reader Written for developers and engineers using Docker in production. About the Author Ian Miell and Aidan Hobson Sayers are seasoned infrastructure architects working in the UK. Together, they used Docker to transform DevOps at one of the UK's largest gaming companies. Table of Contents PART 1 - DOCKER FUNDAMENTALS Discovering Docker Understanding Docker: Inside the engine room PART 2 - DOCKER AND DEVELOPMENT Using Docker as a lightweight virtual machine Building images Running containers Day-to-day Docker Configuration management: Getting your house in order PART 3 - DOCKER AND DEVOPS Continuous integration: Speeding up your development pipeline Continuous delivery: A perfect fit for Docker principles Network simulation: Realistic environment testing without the pain PART 4 - ORCHESTRATION FROM A SINGLE MACHINE TO THE CLOUD A primer on container orchestration The data center as an OS with Docker Docker platforms PART 5 - DOCKER IN PRODUCTION Docker and security Plain sailing: Running Docker in production Docker in production: Dealing with challenges

Download or read book Hands On Machine Learning with Scikit Learn Keras and TensorFlow written by Aurélien Géron and published by "O'Reilly Media, Inc.". This book was released on 2019-09-05 with total page 851 pages. Available in PDF, EPUB and Kindle. Book excerpt: Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets

Download or read book Learn Git in a Month of Lunches written by Rick Umali and published by Simon and Schuster. This book was released on 2015-09-01 with total page 596 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Learn Git in a Month of Lunches introduces the discipline of source code control using Git. Whether you're a newbie or a busy pro moving your source control to Git, you'll appreciate how this book concentrates on the components of Git you'll use every day. In easy-to-follow lessons designed to take an hour or less, you'll dig into Git's distributed collaboration model, along with core concepts like committing, branching, and merging. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Book Git is the source code control system preferred by modern development teams. Its decentralized architecture and lightning-fast branching let you concentrate on your code instead of tedious version control tasks. At first, Git may seem like a sprawling beast. Fortunately, to get started you just need to master a few essential techniques. Read on! Learn Git in a Month of Lunches introduces the discipline of source code control using Git. Helpful for both newbies who have never used source control and busy pros, this book concentrates on the components of Git you'll use every day. In easy-to-follow lessons that take an hour or less, you'll dig into Git's distributed collaboration model, along with core concepts like committing, branching, and merging. This book is a road map to the commands and processes you need to be instantly productive. What's Inside Start from square one—no experience required The most frequently used Git commands Mental models that show how Git works Learn when and how to branch code About the Reader No previous experience with Git or other source control systems is required. About the Author Rick Umali uses Git daily as a developer and is a skilled consultant, trainer, and speaker. Table of Contents Before you begin An overview of Git and version control Getting oriented with Git Making and using a Git repository Using Git with a GUI Tracking and updating files in Git Committing parts of changes The time machine that is Git Taking a fork in the road Merging branches Cloning Collaborating with remotes Pushing your changes Keeping in sync Software archaeology Understanding git rebase Workflows and branching conventions Working with GitHub Third-party tools and Git Sharpening your Git

Download or read book DevOps Paradox written by Viktor Farcic and published by Packt Publishing Ltd. This book was released on 2019-08-30 with total page 533 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover DevOps secrets from leading experts. Viktor Farcic interviews DevOps industries voices including Mike Kail, Greg Bledsoe, Jeff Sussna, James Turnbull, Kohsuke Kawaguchi, Liz Keogh, and more. Key FeaturesLeading DevOps experts share their insights into modern DevOps practiceEngage with the real-world challenges of putting DevOps to workStrengthen your DevOps practices now and prepare for future DevOps trendsBook Description DevOps promises to break down silos, uniting organizations to deliver high quality output in a cross-functional way. In reality it often results in confusion and new silos: pockets of DevOps practitioners fight the status quo, senior decision-makers demand DevOps paint jobs without committing to true change. Even a clear definition of what DevOps is remains elusive. In DevOps Paradox, top DevOps consultants, industry leaders, and founders reveal their own approaches to all aspects of DevOps implementation and operation. Surround yourself with expert DevOps advisors. Viktor Farcic draws on experts from across the industry to discuss how to introduce DevOps to chaotic organizations, align incentives between teams, and make use of the latest tools and techniques. With each expert offering their own opinions on what DevOps is and how to make it work, you will be able to form your own informed view of the importance and value of DevOps as we enter a new decade. If you want to see how real DevOps experts address the challenges and resolve the paradoxes, this book is for you. What you will learn Expert opinions on: Introducing DevOps into real-world, chaotic business environmentsDeciding between adopting cutting edge tools or sticking with tried-and-tested methodsInitiating necessary business change without positional powerManaging and overcoming fear of change in DevOps implementationsAnticipating future trends in DevOps and how to prepare for themGetting the most from Kubernetes, Docker, Puppet, Chef, and AnsibleCreating the right incentives for DevOps success across an organizationThe impact of new techniques, such as Lambda, serverless, and schedulers, on DevOps practiceWho this book is for Anybody interested in DevOps will gain a lot from this book. If you want to get beyond the simplistic ideals and engage with the deep challenges of putting DevOps to work in the real world, this book is for you.