Frequently Asked Questions
Common Questions About Homelab AI Assistant Docker Setup
Common questions about the workshop, what to expect, and how to prepare.
What Docker configuration does a homelab AI assistant need? +
Your homelab Docker AI assistant setup requires Docker Desktop or Docker Engine (for Linux homelabs), Docker Model Runner enabled, appropriate memory allocation for your chosen model, and OpenClaw configured to connect to the local Docker Model Runner endpoint. The workshop covers the complete configuration sequence.
Should I use Docker Desktop or Docker Engine on my homelab? +
Docker Desktop is easiest for Mac and Windows homelabs. Docker Engine is better for Linux-based homelabs as it is lighter weight and more appropriate for server environments. The instructor covers both options and helps you choose the right one for your homelab setup.
Can I use Docker Compose for my homelab AI assistant setup? +
Yes. The instructor covers Docker Compose patterns for orchestrating your OpenClaw and Docker Model Runner setup on a homelab. A Docker Compose configuration simplifies the startup process and makes it easy to manage all components as a single service.
How do I ensure my homelab Docker AI assistant restarts after a power outage? +
The workshop covers configuring Docker to start automatically on system boot and setting restart policies for your OpenClaw and Docker Model Runner services. With proper configuration your homelab AI assistant comes back online automatically after any unplanned restart.
Can I run other Docker services alongside my homelab AI assistant? +
Yes. Docker provides good process isolation allowing your homelab AI assistant to run alongside other services like media servers or home automation tools. The instructor covers resource allocation to ensure your AI assistant gets sufficient RAM and CPU without starving other services.
What monitoring should I set up for my homelab Docker AI assistant? +
The workshop covers practical monitoring for homelab deployments — checking Docker container health, monitoring memory usage during model inference, verifying the messaging platform connection status, and setting up simple alerting if the assistant goes offline.
