mnist-zenml-bentoml-pipeline

This project is based on top of MNIST example project from BentoML.

This project is dependent on python and runs perfectly on version 3.6, 3.7 and 3.8
You can always install required python version using pyenv tool.
After setting up python, install requirements
Next, setup zenml by running zenml init in the root folder. This creates a .zen folder in the root directory that tracks your progress.
Inspect the file zenml_pipeline.py. Observe the different steps the pipeline is composed of.
Run the training pipeline that ends up saving the model to registry using

zenml integration install mlflow
python3 zenml_pipeline.py

bentoml serve service:svc --reload

With the --reload flag, the API server will automatically restart when the source file service.py is being edited, to boost your development productivity.
Verify the endpoint can be accessed locally:

curl -H "Content-Type: multipart/form-data" -F'fileobj=@samples/1.png;type=image/png' http://127.0.0.1:5000/predict_image

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