A service that integrates ML models to applications like Tasking Manager.
Machine Learning has proven to be very successful to make mapping fast with high quality. With a diverse set of models and tools, it is hard to integrate them to existing tools like Tasking Manager and iD. HOT is developing ml-enabler to enable AI-assist to existing mapping tools.
ml-enabler is two projects:
- ml-enabler-api (this repo) - Storage and API to hold tile level ML prediction data.
- ml-enabler-cli (repo) - CLI for interacting with models and subscribe them to the ml-enabler-api
The API uses the following terms:
Model -- A model is a machine learning model. With ml-enabler, we use the TFService convention of publishing models. This allows to spin up containers of the model for prediction and query the data for storage. For an example of a complete implementation, see Development Seed's looking-glass. ml-enabler-api can store data from several versions of the same model.
Prediction -- A prediction is a set of results from an ML Model for a bounding box (region) and at a specific tile level. For results that are not at tile level, the ml-enabler-cli will ensure this is aggregated a granular yet performant level. Predictions are tied to specific versions of a model.
Prediction tiles -- Prediction tiles are the results of the prediction. The tiles are indexed using quadkeys for easy spatial search.
Using this API
- Create a virtualenv -
python3 -m venv venv
- Enable the virtualenv
- Install dependencies
pip install -r requirements.txt
- Setup the database:
- Setup database. If you're on a Mac use Postgres.app, or use docker
ml_enabler.envand add database configuration
- Initialize tables
flask db upgrade
- Start the app
- Create a database for your tests:
- Enabler postgis
echo 'CREATE EXTENSION postgis' | psql -d ml_enabler_test
- Run tests with
python3 -m unittest discover ml_enabler/tests/