Chapter 49: Docker for AI Services

You build the docker-deployment skill first, then use each lesson to test and refine it. By the end you own a production-ready Docker capability for your agent services.

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Goals

• Understand container fundamentals (images, layers, runtime)
• Write production Dockerfiles with multi-stage builds and optimization
• Debug containers (logs, exec, inspect, port conflicts, restart policies)
• Harden images (env vars, health checks, non-root users)
• Apply spec-driven workflows and turn the patterns into a reusable skill

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Lesson Progression

#	Lesson	Focus
0	Build Your Docker Skill	Scaffold from official docs
1	Installation & Setup	Validate prerequisites
2	Container Fundamentals	Images vs. containers
3	First Dockerfile	Build and run images
4	Lifecycle & Debugging	Exec, logs, inspect, restarts
5	Multi-Stage Builds	Size and cache optimization
6	Production Hardening	Health checks, users, envs
7	Docker Image Builder Skill	Encode patterns and prompts
8	Capstone: Containerize Your API	Production-ready image

Each lesson ends with a skill reflection: test, find gaps, and improve the skill.

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Outcome & Method

You finish with a hardened image for the Chapter 40 Task API (in-memory and SQLModel variants) pushed to a registry, plus a reusable Docker skill. The chapter uses the 4-Layer method: foundations → optimization → skill design → spec-driven capstone.

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Prerequisites

• Chapter 40 Task API ready to containerize
• Part 6 fundamentals (FastAPI, Python)
• Terminal comfort; Docker experience not required (Lesson 1 installs)