Model trainer workflow with Airflow using as base: https://github.com/puckel/docker-airflow
Receives a geoJson/geostore id, a model and generates a prediction.
Lists all models and allows filtering by datasetID, type, architecture...
Working Postman collection with endpoints
The knowledge sorce came from this medium article
For the AI part of the project the knowledge came from https://github.com/Skydipper/CNN-tests
You will need to have installed docker and docker-compose;
You will need to have control tower and geostore up and running.
Don't forget to populate your .env file with the requirements
run sh start.sh develop
Airflow: localhost:3053
API endpoint: localhost:3056 or if working with CT localhost:9000/v1/model
enter the container:
docker exec -it modeltrainer /bin/bash
In order to connect with the DB you should create server connection with network as the hostname, the port, username and password that you seted up on your .env file
In order to populate the DB you will need to update the data as you need on the /api/data folder.
You will need to connect to the postgres container. To do so:
docker exec -it postgres /bin/bash
cd /data_import
sh import_data.sh
To enter to do queries on the db psql -U airflow -h localhost geopredictor
To export the DB: pg_dump -U airflow geopredictor > geopredictor.pgsql
TODO
TODO