Setting up a workflow platform and a local model service involves more than launching two containers. A repeatable installation needs known configuration, clear dependency handling, readiness checks, and instructions another person can follow.
The Gignaati project in my portfolio is a desktop application for Docker-based setup of n8n and Ollama, including dependency resolution and health checks. Its public account reports a deployment-time improvement but does not publish raw timing data. This checklist is a planning guide, not a claim that every item was implemented in that project.
Specify the environment
Record the supported operating system, Docker requirements, disk space, network assumptions, and hardware constraints for the intended model. Keep version choices explicit so a future setup does not silently install a different combination of services.
Separate configuration from secrets
Use documented environment configuration for ordinary settings and a secure mechanism for credentials. Do not embed keys in an installer, source file, or diagnostic output. Explain which values an operator must provide and how to rotate them.
Plan model availability and safe repeat runs
Decide whether model files are downloaded during setup or supplied separately. Model size, storage, network speed, and cache state affect installation time. Show progress and handle interrupted downloads without leaving the system ambiguous.
Distinguish a fresh setup from an existing installation. Re-running setup should not unexpectedly delete workflow data, overwrite user configuration, or create duplicate resources. Explain what will be kept and replaced before changing an existing environment.
Test the complete handoff
Verify service readiness, open the workflow interface, and confirm the model endpoint with a harmless test. Provide steps to stop, restart, update, back up, and troubleshoot the installation. See the [Gignaati deployment case study](/case-studies/gignaati-docker-deployment-automation/) for the documented project background.