Practical Docker With Python
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Practical Docker with Python
Learn the fundamentals of containerization and get acquainted with Docker. This second edition builds upon the foundation of the first book by revising all the chapters, updating the commands, code, and examples to meet the changes in Docker. It also introduces a new chapter on setting up your application for production deployment and breaks down terminologies like Dockerfile and Docker volumes while taking you on a guided tour of building a telegram bot using Python. You'll start with a brief history of how containerization has changed over the years. Next, we look at how to install (including using the new WSL2 mode) and get started with Docker. The next couple of chapters will focus on understanding the Dockerfile, including the structure and the core instructions used in building a Docker image. You'll also see how to distribute Docker images using Docker hub and other private registries. From there, you'll look at using Docker volumes for persisting data. Then learn how to run multi-container applications with Docker compose and learn inter-container networking works with Docker networks. Finally, you'll look at how to prepare a containerized application for production deployments. Throughout the book you'll apply the techniques learned through the chapters by building a Telegram messenger Chatbot and see how much easier Docker makes it possible to build, release, contribute and distribute an application. In addition, the book shows how optimize the Docker images for production servers by using multi-stage builds and improve the reliability of your services by using health checks and restart policies. You will: Compare the difference between containerization and virtualization Understand the Dockerfile and converting your application to Docker image Define and run multi-container applications with Docker compose Review data persistency with Docker volumes.
Dockerizing Python for Production
Author: Paul Orlander
language: en
Publisher: Independently Published
Release Date: 2025-12
What if you could transform any Python application into a fast, portable, production-ready service, without fighting deployment issues, environment inconsistencies, or fragile servers ever again? This book shows you how. Dockerizing Python for Production is a practical, engineer-focused guide that walks you through the modern way software is built, shipped, and automated. It cuts through the noise and gives you a clear, actionable path to containerizing Python applications and deploying them with confidence. Whether you're building APIs, machine learning pipelines, microservices, or enterprise-grade systems, this book shows you exactly how to package, automate, and deliver your work with professional reliability. Inside, you'll learn how to build production-ready Docker images, automate pipelines with GitHub Actions, GitLab CI, and Azure DevOps, orchestrate deployments across AWS, GCP, and Kubernetes, and apply real DevOps workflows used by top engineering teams. You'll see how to eliminate configuration drift, speed up delivery cycles, fix environment issues at the root, and create predictable builds that behave the same on every machine-every time. You'll also gain a deeper understanding of why leading companies rely on containerization, how CI/CD transforms engineering performance, and what it takes to deploy resilient, scalable, and secure Python systems in real production environments. Beyond theory, the book is packed with hands-on examples, step-by-step walkthroughs, and practical techniques you can apply immediately. What makes this book different? It doesn't recycle generic explanations or surface-level tutorials. It teaches Docker, containers, and CI/CD through the lens of real engineering challenges. Every concept is aligned with how modern teams build software today, combining clarity, practicality, and production-focused insights you won't find in typical online guides. If you're ready to upgrade your engineering skills, modernize your workflow, and ship software at a level that stands out, this is your guide. Take the first step toward becoming the Python developer who delivers faster, deploys smarter, and works with confidence across any environment. Start reading now.
Python in Containers
All about containers, Docker, and Kubernetes for Python engineers. About This Video Become well-versed with using Docker tools to create top-class containers running your Python code Master Docker runtime tools such as Compose and Swarm Design your applications to run on Kubernetes and master writing Kubernetes object declarations In Detail Docker and Kubernetes are must-have skills for Python engineers these days. Whether your focus is on machine learning and data science or you use Python as a general programming language, you must understand Docker and Kubernetes, as they form the basis of modern cloud-native applications built using microservice architectures. In this course, you'll learn to do the following: Develop and explore machine learning, data science, and Jupyter Notebooks in Docker Run machine learning models in production with Kubernetes and Docker Swarm Package your Python code into containers Publish your containers in image registries Deploy containers to production, both in Docker and Kubernetes Build highly modular, container-based services in a microservices way Monitor and maintain containerized apps You can use the course in two ways: If you use Python for machine learning and data science, go top-down - start with section 7 to quickly develop practical Docker skills and use sections 2 to 6 to delve deeper into specific container topics If you want to use Python for building web apps and microservices, try the bottom-up approach - use the course in a linear way.