hot-fair-utilities 4.0.0

Utilities for AI assisted mapping workflows

hot_fair_utilities

Utilities for AI-assisted mapping workflows in fAIr.

Prerequisites

• GDAL system libraries available (Linux or macOS)
• uv
• just

Local installation

just setup

If GDAL is missing on macOS, install it with Homebrew:

brew install gdal

If GDAL is missing on Debian or Ubuntu:

sudo apt-get update
sudo apt-get install -y gdal-bin libgdal-dev

Run sample workflows

just run ramp
just run yolo

just run ramp downloads the baseline checkpoint into ramp-data/baseline when needed.

Ramp training exports the selected best checkpoint as .h5, and inference uses that exported checkpoint directly.

Validation

Run all quality gates and integration checks:

just check
just test-all

Docker images

Modes and image tags:

• ramp + cpu -> fair-utilities:ramp
• ramp + gpu -> fair-utilities:ramp-gpu
• yolo + cpu -> fair-utilities:yolo
• yolo + gpu -> fair-utilities:yolo-gpu

Build commands:

docker build -f docker/Dockerfile.ramp --build-arg FLAVOR=cpu -t fair-utilities:ramp .
docker build -f docker/Dockerfile.ramp --build-arg FLAVOR=gpu -t fair-utilities:ramp-gpu .
docker build -f docker/Dockerfile.yolo --build-arg FLAVOR=cpu -t fair-utilities:yolo .
docker build -f docker/Dockerfile.yolo --build-arg FLAVOR=gpu -t fair-utilities:yolo-gpu .

Notebook test workflow

Run Package_Test.ipynb to validate the package workflow on the sample dataset.

Benchmark docs

See docs/benchmark/sample-datasets.md for benchmark dataset details.

Development notes

Follow docs/Version_control.md for release and versioning guidance.