Docker实战:探索容器技术

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"Docker In Action" 《Docker In Action》是由Manning出版社出版的一本关于Docker的实战书籍,作者是Jeff Nickoloff。这本书深入浅出地介绍了Docker容器技术的各个方面,旨在帮助读者掌握如何在实际项目中有效利用Docker。 Docker作为一种轻量级的虚拟化技术,极大地改变了软件开发、测试和部署的流程。通过使用Docker,开发者可以将应用程序及其依赖环境打包到一个可移植的容器中,从而实现环境一致性,降低部署复杂性。 本书首先会讲解Docker的基础概念,包括容器、镜像、仓库等核心概念。读者会学习如何创建和管理Docker容器,以及如何构建自己的Docker镜像。此外,书中还会介绍Dockerfile,这是一个用于自动化构建Docker镜像的文本文件,通过它,你可以轻松地定义和维护你的应用环境。 在进阶部分,书中会涉及Docker网络,讲解如何配置容器间的通信,以及如何使用Docker Compose进行多容器应用的编排。此外,还会介绍Docker Swarm,这是Docker的集群管理工具,用于管理和扩展Docker服务。 安全性和监控也是《Docker In Action》的重要内容。书中会讨论如何保护Docker容器的安全,以及如何通过监控工具收集和分析容器的性能数据。 对于持续集成和持续部署(CI/CD)实践,书中也会给出具体的指导,介绍如何将Docker与Jenkins、GitLab CI/CD等工具结合,实现高效的软件发布流程。 最后,作者可能会分享一些最佳实践和案例研究,帮助读者在实际工作中更好地应用Docker,解决可能遇到的问题。 《Docker In Action》是一本全面且实用的Docker指南,适合对容器技术感兴趣的开发者、系统管理员,以及希望提升DevOps实践效率的团队。通过阅读此书,读者不仅可以理解Docker的工作原理,还能学会如何在实际项目中高效、安全地使用Docker。
2017-12-13 上传
2018-04-04 上传
Docker-in-Action.pdf In 2011, I started working at Amazon.com. In that first week my life was changed as I learned how to use their internal build, dependency modeling, and deployment tool- ing. This was the kind of automated management I had always known was possible but had never seen. I was coming from a team that would deploy quarterly and take 10 hours to do so. At Amazon I was watching rolling deployments push changes I had made earlier that day to hundreds of machines spread all over the globe. If big tech firms had an engineering advantage over the rest of the corporate landscape, this was it. Early in 2013, I wanted to work with Graphite (a metrics collection and graphing suite). One day I sat down to install the software and start integrating a personal proj- ect. At this point I had several years of experience working with open source applica- tions, but few were as dependent on such large swaths of the Python ecosystem. The installation instructions were long and murky. Over the next several hours, I discov- ered many undocumented installation steps. These were things that might have been more obvious to a person with deeper Python ecosystem knowledge. After pouring over several installation guides, reading through configuration files, and fighting an epic battle through the deepest parts of dependency hell, I threw in the towel. Those had been some of the least inspiring hours of my life. I wanted nothing to do with the project. To make matters worse, I had altered my environment in a way that was incompatible with other software that I use regularly. Reverting those changes took an embarrassingly long time. I distinctly remember sitting at my desk one day in May that year. I was between tasks when I decided to check Hacker News for new ways to grow my skillset. Articles about a technology called Docker had made the front page a few times that week. That evening I decided to check it out. I hit the site and had the software installed within a few minutes. I was running Ubuntu on my desktop at home, and Docker only had two dependencies: LXC and the Linux kernel itself. Licensed to Stephanie Bernal <nordicka.n@gmail.com> PREFACE xiv Like everyone else, I kicked the tires with a “Hello, World” example, but learned little. Next I fired up Memcached. It was downloaded and running in under a minute. Then I started WordPress, which came bundled with its own M y SQL server. I pulled a couple different Java images, and then Python images. Then my mind flashed back to that terrible day with Graphite. I popped over to the Docker Index (this was before Docker Hub) and did a quick search. The results came back, and there it was. Some random user had created a Graphite image. I pulled it down and created a new container. It was running. A simple but fully configured Graphite server was running on my machine. I had accomplished in less than a minute of download time what I had failed to do with several hours a few months earlier. Docker was able to demonstrate value with the simplest of examples and minimum effort. I was sold. Over the next week, I tried the patience of a close friend by struggling to direct our conversations toward Docker and containers. I explained how package management was nice, but enforcing file system isolation as a default solved several management problems. I rattled on about resource efficiency and provisioning latency. I repeated this conversation with several other colleagues and fumbled through the container story. Everyone had the same set of tired questions, “Oh, it’s like virtualization?” and “Why do I need this if I have virtual machines?” The more questions people asked, the more I wanted to know. Based on the popularity of the project, this is a story shared by many. I began including sessions about Docker when I spoke publicly. In 2013 and 2014, only a few people had heard of Docker, and even fewer had actually tried the software. For the most part, the crowds consisted of a few skeptical system administrator types and a substantial number of excited developers. People reacted in a multitude of ways. Some were pure rejectionists who clearly preferred the status quo. Others could see problems that they experienced daily solved in a matter of moments. Those peo- ple reacted with an excitement similar to mine. In the summer of 2014, an associate publisher with Manning called me to talk about Docker. After a bit more than an hour on the phone he asked me if there was enough content there for a book. I suggested that there was enough for a few books. He asked me if I was interested in writing it, and I became more excited than I had been for some time. That fall I left Amazon.com and started work on Docker in Action. Today, I'm sitting in front of the finished manuscript. My goal in writing this book was to create something that would help people of mixed backgrounds get up to speed on Docker as quickly as possible, but in such a way that they understand the underlying mechanisms. The hope is that with that knowledge, readers can under- stand how Docker has been applied to certain problems, and how they might apply it in their own use-cases.
2016-04-08 上传
Summary Docker in Action teaches readers how to create, deploy, and manage applications hosted in Docker containers. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The idea behind Docker is simple. Create a tiny virtual environment, called a container, that holds just your application and its dependencies. The Docker engine uses the host operating system to build and account for these containers. They are easy to install, manage, and remove. Applications running inside containers share resources, making their footprints small. About the Book Docker in Action teaches readers how to create, deploy, and manage applications hosted in Docker containers. After starting with a clear explanation of the Docker model, you will learn how to package applications in containers, including techniques for testing and distributing applications. You will also learn how to run programs securely and how to manage shared resources. Using carefully designed examples, the book teaches you how to orchestrate containers and applications from installation to removal. Along the way, you'll discover techniques for using Docker on systems ranging from dev-and-test machines to full-scale cloud deployments. What's Inside Packaging containers for deployment Installing, managing, and removing containers Working with Docker images Distributing with DockerHub About the Reader Readers need only have a working knowledge of the Linux OS. No prior knowledge of Docker is assumed. About the Author Jeff Nickoloff, a software engineer, has presented Docker and its applications to hundreds of developers and administrators at Desert Code Camp, Amazon.com, and technology meetups.
2024-09-05 上传
目标检测(Object Detection)是计算机视觉领域的一个核心问题,其主要任务是找出图像中所有感兴趣的目标(物体),并确定它们的类别和位置。以下是对目标检测的详细阐述: 一、基本概念 目标检测的任务是解决“在哪里?是什么?”的问题,即定位出图像中目标的位置并识别出目标的类别。由于各类物体具有不同的外观、形状和姿态,加上成像时光照、遮挡等因素的干扰,目标检测一直是计算机视觉领域最具挑战性的任务之一。 二、核心问题 目标检测涉及以下几个核心问题: 分类问题:判断图像中的目标属于哪个类别。 定位问题:确定目标在图像中的具体位置。 大小问题:目标可能具有不同的大小。 形状问题:目标可能具有不同的形状。 三、算法分类 基于深度学习的目标检测算法主要分为两大类: Two-stage算法:先进行区域生成(Region Proposal),生成有可能包含待检物体的预选框(Region Proposal),再通过卷积神经网络进行样本分类。常见的Two-stage算法包括R-CNN、Fast R-CNN、Faster R-CNN等。 One-stage算法:不用生成区域提议,直接在网络中提取特征来预测物体分类和位置。常见的One-stage算法包括YOLO系列(YOLOv1、YOLOv2、YOLOv3、YOLOv4、YOLOv5等)、SSD和RetinaNet等。 四、算法原理 以YOLO系列为例,YOLO将目标检测视为回归问题,将输入图像一次性划分为多个区域,直接在输出层预测边界框和类别概率。YOLO采用卷积网络来提取特征,使用全连接层来得到预测值。其网络结构通常包含多个卷积层和全连接层,通过卷积层提取图像特征,通过全连接层输出预测结果。 五、应用领域 目标检测技术已经广泛应用于各个领域,为人们的生活带来了极大的便利。以下是一些主要的应用领域: 安全监控:在商场、银行