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recsysdemo

A mini ML system which can train some prediction models for recommendation on a given user+item click interaction data set and automatically deploy models to be served by a simple python web API end point

Steps to run the notebooks/code Used. Run in root mode on Unix Based System

  • ~/recsysdemo$ chmod a+x rundocker.sh && ./rundocker.sh

  • Open http://localhost:9001/tree/notebooks in your browser to examine the notebooks

  • Open http://localhost:5000/docs in your browser to examine the prediction api.

  • If you system has CUDA + GPU configured properly you can try below command to startup

  • ~/recsysdemo$ chmod a+x rundocker.sh && ./rundocker_gpu.sh

Test API Request

    curl --location --request POST 'http://localhost:5000/predict' \
    --header 'Content-Type: application/json' \
    --data-raw '{
    "req_id":"1242765",
    "id":732810,
    "user_id":28349,
    "store_id":366,
    "device":"app_ios",
    "platform":"iOS App",
    "channel":"Direct",
    "created_at":"2021-02-03 23:47:27",
    "num_of_items_req":5
    }'