Tutorial Model Library
For source types, versioning rules, and best practices, see Model Library.
This tutorial walks you through registering a Python script and verifying it works in a canvas. Takes about 10 minutes.
The Co-engineer can register models for you. Upload your script in the Co-engineer chat and say "Register this as a model." It will infer the input/output schema automatically and handle the registration. This tutorial is for when you want to do it manually or understand what's happening under the hood.
A Python script with a function named run, main, execute, predict, or simulate that takes inputs as arguments and returns a dict. A minimal example:
def run(coating_thickness: float, porosity: float, temperature: float):
result = coating_thickness * porosity * (1 + 0.002 * temperature)
return {"adjusted_capacity": result}Click Models Library in the sidebar → Register Model → Upload file.
Upload your script. Protos reads it and infers the input/output schema automatically from your function signature. Review what it found — add units to every numeric field and a description to anything non-obvious. This documentation is what makes the model usable by your team later.
Fill in the name, description, and domain, then click Save.
Go to Simulation Studio, create a canvas, and add a Model block. Search for the model you just registered — the input fields appear as connection points.
Wire a Parameter block to each input, click Start sequence, and check the result. If it fails, the error message in the model block's detail panel will tell you what went wrong.
When you update the model: open it in the Model Library → New Version → upload the updated script → add a changelog note. Old canvases keep running on the previous version — their results stay reproducible. See Model Library → Model Versioning.
If your model is in a public GitHub repo, use Register Model → GitHub instead of uploading a file. Protos clones the repo, detects the entry point, and generates a wrapper. The repo needs a run/main/execute function or a protos.toml file declaring the interface.
→ Tutorial: Building Your First Canvas — add your registered model as a block in a canvas and run it.