Docker packaging guide for Python

以下是一些关于python 集成docker 的文章，很不错

The basics

1. Broken by default: why you should avoid most Dockerfile examples
   Most Dockerfile examples you’ll find on the Web are broken. And that’s a problem.
2. A review of the official Dockerfile best practices: good, bad, and insecure
   The official Docker documentation’s Dockerfile best practices are mostly good—but they omit some important information.
3. The best Docker base image for your Python application (January 2020)
   Ubuntu? Official Python images? Alpine Linux? Here’s how to choose a good base image.
4. Alpine makes Python Docker builds 50× slower, and images 2× larger
   Alpine Linux is often recommended as a smaller, faster base image. But if you’re using Python, it will actually do the opposite.
5. When to switch to Python 3.8
   Python 3.8 is out now—when should you start using it?
6. Connection refused? Docker networking and how it impacts your image
   A command that runs fine on your computer may fail with connection refused when run in a container. You’ll learn why that happens, and how to prevent it.
7. Faster or slower: the basics of Docker build caching
   Docker’s layer caching can speed up your image build—if you write your Dockerfile correctly.
8. Installing system packages in Docker with minimal bloat
   Your Docker build needs to update system packages for security, and perhaps to install them for additional dependnecies. Here’s how to do it without making your image too large, on Debian, Ubuntu, CentOS and RHEL.

Security

1. Avoiding insecure images from Docker build caching
   Docker’s layer caching is great for speeding up builds—but you need to be careful or it’ll cause you to have insecure dependencies.
2. Less capabilities, more security: minimizing privilege escalation in Docker
   To reduce the security risk from your Docker image, you should run it as a non-root user. You should also reduce it capabilities: learn what, why, and how.
3. Docker build secrets, the sneaky way
   When you’re building Docker images you often need some secrets: a password, an SSH key. For now, Docker lacks a good mechanism to pass in secrets in a secure way, which means you need to get sneaky.

Fast builds, small images

1. The high cost of slow Docker builds
   A slow Docker build on the critical path for developer feedback is a lot more expensive than you think.
2. Faster Docker builds with pipenv, poetry, or pip-tools
   Installing dependencies separately from your code allows you to take advantage of Docker’s layer caching. Here’s how to do it with pipenv, poetry, or pip-tools.
3. Elegantly activating a virtualenv in a Dockerfile
   How to activate a virtualenv in a Dockerfile without repeating yourself—plus, you’ll learn what activating a virtualenv actually does.
4. Multi-stage builds #1: Smaller images for compiled code
   You’re building a Docker image for a Python project with compiled code (C/C++/Rust/whatever), and somehow without quite realizing it you’ve created a Docker image that is 917MB… only 1MB of which is your code!
5. Multi-stage builds #2: Python specifics—virtualenv, –user, and other methods
   Now that you understand multi-stage builds, here’s how to implement them for Python applications.
6. Multi-stage builds #3: Why your build is surprisingly slow, and how to speed it up
   Multi-stage builds give you small images and fast builds—in theory. In practice, they require some tricks if you want your builds to actually be fast.

Applications and runtime

1. Configuring Gunicorn for Docker
   Running in a container isn’t the same as running on a virtual machine or physical server: you need to configure Gunicorn (and other servers) appropriately.
2. Activating a Conda environment in your Dockerfile
   Learn how to activate a conda environment in your Dockerfile.
3. Decoupling database migrations from server startup: why and how
   It’s tempting to migrate your database schema when your application container starts up—here’s some reasons to rethink that choice.
4. What’s running in production? Making your Docker images identifiable
   It’s difficult to debug production problems if you don’t know what image is running in production.
5. A Python prompt into a running process: debugging with Manhole
   Your Python process is acting strange—wouldn’t it be useful to get a live Python interpreter prompt inside your running process?

Packaging as a process

1. A thousand little details: developing software for ops
   Some thoughts on how to build software for ops, a domain that suffers from historical complexity and problem space complexity. And in particular, buildng a better way to do Docker packaging.
2. Your Docker build needs a smoke test
   If you don’t test your Docker image before you push it, you’ll waste time (and maybe break production).

Products and Services

Production-ready template: best practices, faster

Instead of synthesizing every article above, spending days debugging your build process, and fighting slow builds, you can ship your Docker images with confidence—in just hours!—by using the Production-Ready Python Containers template.

原文地址：https://www.cnblogs.com/rongfengliang/p/12242412.html