It is a simple API Framework for serving your Machine Learning model.
Don’t write glue code for API and Keras model! We did it for You! https://mlapi.io
If you want to host your own ml api with use of mlapi, check out our documentation page.
https://ermlab.github.io/mlapi.io/
docker build -t mlapi .
docker run -d --name mlapi -e APP_SETTINGS=DevelopmentConfig -e DB_SECRET_KEY=extremely_secret_key -p 0.0.0.0:8000:8000 mlapi gunicorn -b 0.0.0.0:8000 mlapi.app
docker exec -d mlapi bash -c "python manage.py create_db"Then the API is available at localhost:8000.
virtualenv -p python3 .venv
source .venv/bin/activate
pip install -r requirements.txt
export DB_SECRET_KEY=extremely_secret_key
python manage.py create_dbFor ease of use the DB_SECRET_KEY value can be written inside API/db/config.py.
Then you will need to create the first DB admin user which then will allow you to create other users via API rather than using the command-line and direct DB connection.
python
> from mlapi.app import database as db
> from db.dbModels import User
> admin = User(email="test@test.test", password="secret_admin_password", uses=1000, is_admin=True)
> db.session.add(admin)
> db.session.commit()
> exit()source .venv/bin/activate
DB_SECRET_KEY=extremely_secret_key gunicorn mlapi.appIf you want the server to restart on every code change just add a --reload parameter after gunicorn in the last command.
Basic tips on how to communicate with the API can be found here.
Of course, the address to communicate with your local instance will be localhost:8000.