fair-py-ops 0.1.1

Model registry and ML pipeline orchestration for fAIr

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

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

See Getting Started for detailed setup, environment options, and running individual examples.

Documentation

• Getting Started : Installation, local setup, Kubernetes, and running 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/

Available Commands

Run just to see all recipes. Common commands:

just setup          # Install dependencies and set up environment
just example        # Run all three example pipelines
just lint           # Run Ruff linting and type checking (ty)
just test           # Run unit tests
just k8s            # Set up Kubernetes dev environment
just local          # Switch back to local mode

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