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. ML Enabler is a service that provides better integration during model development as well integrating model inferences to mapping tools.

ML Enabler was initially developed by Development Seed in partnership with Humanitarian OpenStreetMap Team.

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.

The CloudFormation template is designed to be AWS Account agnostic and will create all necessary resources with the exception of an SSL certificate (leave blank to disable https)

The cloudformation template is designed to be deployed with cfn-config, or a cfn-config compatible client. OpenAddresses/Deploy is a compatible client with a bunch of extra nice features.

The following parameters have special considerations you should be aware of.

These values must be compatible with Fargate. See Task CPU Limitations for compatible values.

The name of the SSL Cert as managed by AWS to use with the HTTPS ELB. This value can be left blank to disable all HTTPS Load Balancing

See API.md

1. Copy example.env to ml_enabler.env
2. Run docker-compose build
3. Run docker-compose up

1. Create a virtualenv - python3 -m venv venv
2. Enable the virtualenv ./venv/bin/activate
3. Install dependencies pip install -r requirements.txt
4. Setup the database:

• Setup database. If you're on a Mac use Postgres.app, or use docker
• Copy example.env to ml_enabler.env and add database configuration
• Initialize tables flask db upgrade

6. Start the app

• export FLASK_APP="ml_enabler"
• export FLASK_ENV="development"
• flask run

1. Create a database for your tests:

• createdb ml_enabler_test
• Enabler postgis echo 'CREATE EXTENSION postgis' | psql -d ml_enabler_test

2. Run tests with python3 -m unittest discover ml_enabler/tests/