This repository provides an example of KServer custom storage initializer that showcases how users can automate a machine learning model deployment stored as OCI artifact using KServe.
NOTE: This repository was mainly intended for demoing purposes
The implementation was inspired by the default KServe storage container, this means that its integration should be pretty straightforward if required at some point.
Please take a look at Get Started guide for a very simple step-by-step example on how to use this OCI custom storage initializer to deploy an ML model stored as OCI artifact using KServe.
Prerequisites:
- Python
- Poetry
NOTE: I would suggest to use a Python virtual environment as development environment
Install the dependencies:
make install-depsBuild the python wheel:
make buildInstall the python package:
make installBuild the container image:
make image-buildPush the container image:
make image-push