JIT Chapter 79: Docker for AI Services
Build your Docker skill first, then learn to understand and refine it into a production-ready asset
- Status
- stable
- Trust
- unverified
- Approved
- ag-archive/0.1.0 ·
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.
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
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.
Outcome & Method
You finish with a hardened image for the Chapter 70 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.
Prerequisites
- Chapter 70 Task API ready to containerize
- Part 6 fundamentals (FastAPI, Python)
- Terminal comfort; Docker experience not required (Lesson 1 installs)
Build Your Docker Skill
Create your Docker deployment skill in one prompt, then learn to improve it throughout the chapter
Docker Installation & Setup
Containers solve the "works on my machine" problem by packaging applications with their dependencies into portable, isolated units that share the host kernel, making them faster and lighter than VMs.