Webscikit is a set of tools to run a HTTPServer as a JSON Webservice for scikit-learn predictions. It comes with two examples: boston and boston2
It is work in progress, so bug and feature requests are highly appreciated!
Features:
The server can handle multiple models. The models and urls are registered at webscikit.conf .
Multiple data-scientist could work locally on their own models, and then later deploy their model to the server.
The models can be deployed when the server is online.
Each model can save additional metadata needed to transform and predict new data.
You can easily start a new project with create_project.py newProjectName
In the directory examples/ are examples of different models (boston, boston2 etc.) and also example of requests to the server.
How does it work:
- The model gets fitted by the data scientist, gzip-pickled and then uploaded to the server.
- Http-Clients make POST-requests and send json-files to transform / predict new data and get a Json - response back.
If you wan to run the examples:
source export_WEBSCIKITMODELSPATH.sh
cd server/
./webserver.py
cd ../example/requests/
./curl_boston.sh
./curl_boston2.sh