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This project implements a common rest server which can serve tensorflow-serving & xgboost models.

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ml_model_rest_server

This project implements a common rest server which can serve tensorflow-serving & xgboost models.

Architecture

Architecture.png

Installation

Dependencies

Steps

  • ./configure
  • cd debug
  • make

Example

  • To serve tf-serving model, start tf-serving first.
POST http://localhost:11000

{"input_type": "ints", "inputs": [1, 2, 4, 5], "model_name": "tensorflow-feature-sum-model", "model_signature_name": "tensorflow-feature-sum-model"}
  • Output
{"y_output":"12"}
  • To serve xgboost models, put the *.cpb files in the debug/src/frontends directory.
POST http://localhost:11000

{"input_type": "floats", "inputs": [1.0, 2.0, 4.0, 5.0], "model_name": "test.cpb", "model_signature_name": "xgboost"}

  • Output
{"y_output":"0.0001"}

Sample models

A sample xgboost test.cpb model is present in resources directory.

Next steps

  • docker integration

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This project implements a common rest server which can serve tensorflow-serving & xgboost models.

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