fAIr-models

Model registry and ML pipeline orchestration for fAIr.

fair-py-ops is the Python package for building ZenML pipelines, validating STAC items, and testing locally. The models/ directory is the single source of truth for base model contributions.

Quick Start

Prerequisites: Docker, uv, just.

git clone https://github.com/hotosm/fAIr-models.git
cd fAIr-models
just setup
just example

just setup installs Python deps, brings up the full stack via Docker Compose (Postgres + MinIO + STAC + MLflow + ZenML), and registers the ZenML stack. just example runs all three reference pipelines end-to-end.

Service	URL	Credentials
ZenML dashboard	http://localhost:8080	`default` / (empty)
MLflow	http://localhost:5000	none
STAC API	http://localhost:8082	none
MinIO console	http://localhost:9001	`minioadmin` / `minioadmin`

See Getting Started for the full guide. For Kubernetes parity or production deploys, see infra/README.md.

Documentation

Getting Started : Installation and running the examples
Architecture : STAC catalog structure, flows, identity model, infrastructure
Contributing a Model : Guide for adding base models to fAIr
API Reference : Python package documentation
Changelog : Release history

Examples

Three reference implementations demonstrate the full workflow for each supported task:

Example	Task	Model	Path
Segmentation	Semantic segmentation	UNet (torchgeo)	`examples/segmentation/`
Classification	Binary classification	ResNet18 (torchvision)	`examples/classification/`
Detection	Object detection	YOLOv11n (ultralytics)	`examples/detection/`

Commands

Run just to see all recipes.

just setup     # install deps + bring up stack + register ZenML stack
just example   # run all 3 example pipelines
just down      # stop the stack (state preserved, fast restart)
just up        # restart after `just down`
just tear      # destroy stack + volumes + local ZenML state
just lint      # ruff + ty
just test      # pytest
just validate  # validate STAC items + model pipelines
just docs      # serve documentation locally
just commit    # run pre-commit hooks + commitizen

Key Concepts

Concept	Description
Base model	Reusable ML blueprint (weights, code, Docker image, STAC item)
Local model	Finetuned model produced by ZenML pipeline on user data
STAC catalog	Model/dataset registry with MLM and Version extensions
ZenML pipeline	Orchestrated training and inference workflows

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