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Light REST API for machine learned models

Project description

Build status Build Status Windows MIT License Requirements Status GitHub Issues Waffle Notebook Coverage


It implements a light machine learning REST API based on falcon. You can test a dummy wsgi server by running:

start_mlrestapi --name=dummy

And then query it with:

import requests
import ujson
features = ujson.dumps({'X': [0.1, 0.2]})
r ='', data=features)

It should return:

{'Y': [[0.4994216179, 0.4514893599, 0.0490890222]]}

The module was first tried with success in a hackathon in 2018. Participants could upload their model and retrieve their predictions through a REST API to check it was producing the same one as they had. A simple way to put a model into production.


current - 2019-05-17 - 0.00Mb

  • 20: fix issue with falcon 2.0 (2019-05-08)
  • 17: reload module mapped to subfolder (2019-01-11)
  • 16: add full example to start a rest api (2018-12-01)

0.1.111 - 2018-11-16 - 0.12Mb

  • 9: add version number to REST API (2018-11-13)
  • 6: allow a zip file which contains data and code (2018-11-13)
  • 4: add authentification to the rest api (2018-11-13)

0.1.88 - 2018-11-05 - 0.11Mb

  • 8: allow clear logs (2018-11-02)
  • 5: add a load function (2018-11-02)
  • 3: add ip address in the logging (2018-04-15)
  • 2: add encrypted logging (2018-04-15)
  • 1: fix gallery of examples (style) (2018-01-05)

0.1.37 - 2017-12-06 - 0.10Mb

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