Title: Discussion 2 — Debugging a Misbehaving Container
Course: AIINFRA 101: Applied Infrastructure & Containerization > Section: Week 02 — Docker Fundamentals: Running & Managing Containers

💬 Discussion 2 — Debugging a Misbehaving Container (20 pts)

Imagine you're supporting an AI inference container in production and it starts responding slowly or returning errors. Your first instinct might be to just restart it — but restarting throws away whatever state and clues are currently inside that container. In your post, address:

- Why is the ability to docker exec into a running container a powerful debugging tool, compared to only ever being able to docker restart or docker logs from the outside?
- Before you restart a misbehaving AI inference container, what specific information would you want to collect first (think: process activity, memory/CPU usage, recent log lines, open connections, or files written inside the container)? Name at least three concrete things you'd check and the command you'd use to check each.
- What's one piece of information that would be lost forever the moment you restart or remove the container, unless you had already captured it?

Post your initial response (150+ words), then respond to at least two classmates — ideally engaging with a debugging approach different from your own.