Machine learning model utilities
Project description
bavard-ml-common
A package of common code and utilities for machine learning and MLOps. Includes classes and methods for:
- ML model serialization/deserialization
- Google Cloud Storage IO operations
- Converting an ML model into a runnable web service
- Common ML model evaluation utilities
- And more
Testing Locally
With Docker and docker-compose installed, run:
./scripts/lint-and-test-package.sh
Releasing The Package
Releasing the package is automatically handled by CI, but three steps must be taken to trigger a successful release:
- Increment the
VERSION
variable insetup.py
to the new desired version (e.g.VERSION="1.1.1"
) - Commit and tag the repo with the exact same value you populated the
VERSION
variable with (e.g.git tag 1.1.1
) - Push the commit and tag to remote. These can be done together using:
git push --atomic origin <branch name> <tag>
CI will then release the package to pypi with that version once the commit and tag are pushed.
Project details
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bavard-ml-common-0.1.0.tar.gz
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