Minimal example of serving a scikit-learn model over a Python server, using the following Python web frameworks

• Flask
• Tornado
• Sanic (Forthcoming)
• AIOHTTP (Forthcoming)

1. Fit a scikit-learn model - I do some light preprocessing, then fit a simple ridge regression model on the California housing dataset. python train.py
2. Run flask server cd flask && python server.py
3. Hit the server curl -i -H "Content-Type: application/json" -X POST http://127.0.0.1:5000/api/v0/house_value -d @flask/sample_request.json
4. Update the model by re-running step 1. This just takes a bootstrap sample, but in production, you might retrain in batch.
5. Tell the server to reload the model from disk curl -X POST http://127.0.0.1:5000/api/v0/update
6. Re-run the curl request from step 3 and note that you get a different answer!

• A little more involved, to try to test the async capabilities of this framework

1. Start the server: docker run --rm -v $(pwd):/root/sanic -p 8000:8000 -v /Users/timothysweetser/git/flask-example/artifacts:/root/sanic/artifacts -w /root/sanic tsweetser/sanic python main.py
2. Hit the server with a sample request: curl localhost:8000/api/v0/house_value -d @flask/sample_request.json
3. Retrain the model: python train.py
4. Tell the server to reload the model: curl localhost:8000/api/v0/update
5. Rerun step 2 and note that you get a different answer!

• TODO

• This is not a real production system - no error handling, data validation, authentication, logging, etc etc etc
• Please file an issue or make a PR if you see any glaring errors