Getting Started with Docker (Nigel Poulton)（Z-Library）

【One-Line Pitch】 A hands-on, beginner-friendly guide that takes you from zero Docker knowledge to running containers, multi-container microservices, and even a local AI chatbot. Best for developers, ops folks, and technical managers who want practical fundamentals without wading through expert-level depth. 【Book Arc】 - **Opening (~0%–12%)**: Introduces the author, the book's scope, and the sample apps on GitHub; sets expectations that you'll master fundamentals, not become an expert. - **Early (~12%–35%)**: Core concepts and jargon — why containers exist, images, containers, registries, OCI standards, containers vs VMs, microservices, Linux vs Windows containers, and containers' role in AI. - **Middle (~35%–55%)**: Installing Docker (Docker Desktop recommended, Multipass as an alternative), verifying the installation, and setting up Docker Hub accounts. - **Middle (~55%–75%)**: First hands-on container work — running, managing, stopping, restarting, and deleting containers, plus using Docker Desktop's UI for management. - **Late (~75%–90%)**: Containerizing a simple web app, understanding container images and Docker Hub workflows, then deploying a multi-container microservices application. - **Ending (~90%–100%)**: Using Docker Model Runner to deploy a local AI chatbot and integrate it with Open WebUI — the book's AI-focused capstone. 【Key Takeaways】 - **Containers solve the "works on my machine" problem** (Early): by packaging an app with its dependencies into a standard image, so what you test is exactly what runs in production. - **Images are layered and built from Dockerfiles** (Early): a base OS layer, app layer, and dependency layer stack into a single image; `docker build` steps through Dockerfile instructions to create them. - **Containers virtualize the OS, not the hardware** (Early): unlike VMs, containers share the host kernel, making them smaller, faster, and more portable — you can run far more containers than VMs on the same hardware. - **Microservices = one feature, one container** (Early): each microservice is built as its own image and deployed as its own container, enabling independent updates and precise scaling. - **Docker Desktop is the recommended environment** (Middle): it provides a full Docker experience with a GUI, and is free for personal use (paid for larger companies). Multipass is a fallback but lacks some features like Model Runner. - **Hands-on container management is the core skill** (Middle): running, stopping, restarting, and deleting containers via CLI and Docker Desktop builds the muscle memory for everything that follows. - **The book culminates in local AI deployment** (Ending): Docker Model Runner lets you run LLMs on local hardware and inside containers, integrating with Open WebUI — connecting Docker skills to the AI era. - **You'll master fundamentals, not become an expert** (Opening): the author is explicit that this is a starting point, preparing you to take next steps independently. 【Reading Tips】 - **Skim Chapter 1 if you already know containers** — it's conceptual groundwork, but the jargon recap at the end is worth a quick review. - **Deep-read Chapters 3–6** — these are the hands-on core; actually run the commands and follow along with the sample apps from GitHub. - **Don't skip the installation chapter** — getting Docker Desktop (or Multipass) working correctly upfront saves debugging headaches later. - **Chapter 7 is the payoff** — if AI is your motivation, you can read Chapters 1–2 for context, then jump to the hands-on container chapters before tackling the AI chatbot deployment. - **Use the GitHub sample apps** — they're designed to reinforce each chapter's lessons; cloning them early makes the hands-on sections smoother. 【Coverage Limits】 This guide is based on stratified excerpts covering roughly the first half of the book (through container management); later chapters on containerizing web apps, microservices deployment, and the AI chatbot are summarized from the table of contents and chapter descriptions rather than detailed content.