All about containers, Docker, and Kubernetes for Python engineers.
Key Features
- 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
Book Description
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
All the resource files are uploaded on the GitHub repository at https://github.com/PacktPublishing/Python-in-Containers
What you will learn
- Build container images having Python applications and ship them to Docker Hub
- Run Jupyter Notebooks and create virtual machines in Docker
- Use Docker Desktop for Windows Pro and macOS and Docker Toolbox for Windows Home
- Create custom container images from scratch and automate container image builds with Dockerfile
- Design Flask and Django multi-container deployments and automate them with Docker Compose
- Containerize TensorFlow models into microservices and use Kubernetes with Minikube on a development host
- Deploy complex multi-container applications in Docker Swarm and Kubernetes
Who this book is for
This course is for Python programmers, data scientists, and machine learning engineers who want to use Python with Docker tools for building modern cloud-native applications.
