Server API for ML Blink.
- Install Docker
- Create an
.env
file using.env.example
as a starting point (i.e.,cp .env.example .env
)- Set
FLASK_ENV=development
- There's no need to set the
MONGO_URI
for local development - Set
ORIGINS=http://localhost:8080
- Set
- Unzip the beta-pack dataset images (
PanSTARRS_ltd
andUSNO1001
) in/ml_blink_api/static/beta_images
in their corresponding directories - Run
docker-compose build
to build the Docker services- Only run this once or if the Dockerfile has been updated
- Start:
docker-compose up
- Stop :
docker-compose down
- Run
mongo 127.0.0.1:27017
to start the MongoDB shell- This is not required to start the application, but can be useful when you want to manually check the database's collections
- Rebuild the application (changes to the
requirements.txt
or Docker configuration file require a rebuild):
docker-compose up --build
- Documentation of the endpoints exposed by the ML-Blink API, the methods each of them supports, and the attributes these expect.
- A guide to configure an Ubuntu 16.04 server to serve the
ml_blink_api
Flask application in the SNIC Cloud.
- This is a very rudimentary deployment script. It assumes the server is setup as explained in the server configuration guide.
- SSH in the server by running
ssh -i <file_name>.pem ubuntu@<FLOATING_IP_ADDRESS>
and run:
sudo systemctl stop celeryd
sudo service apache2 stop
cd /var/www/ml_blink_api
sudo git pull
sudo pip3 install -r requirements.txt
sudo service apache2 restart
sudo systemctl restart celeryd