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Example project to predict housing pricing by using scikit, onnx and fastapi.
Python Jupyter Notebook Shell Dockerfile
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This repository shows how to serve a model with ONNX Runtime and FastAPI. A simple linear regression was trained with scikit-learn framework on the boston housing dataset.

Application Overview

Overview of the application

The purpose of the application is to run the model with the ONNX Runtime and to use FastAPI to make the model available via a post request. We are using a token in headers request to secure the application.



If you want to take a look on how the model was trained take a look into the Modeltraining.ipynb. After the training the model is exported to ONNX with the skl2onnx package.


Just run docker-compose up to start the project.

Application Visualisation

Application visualisation

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