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Defining Kubeflow Pipeline (KFP) Components with Python Dataclasses

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Features

  • Dataclass-Driven Component Definition: Define component logic using Python dataclasses, seamlessly translating them into Kubeflow Pipelines (KFP) compatible functions and components.
  • KFP Agnostic: Empower developers to design and implement component logic as standard Python code, independent of the KFP framework.

Installation

pip install ml-orchestrator

Note: ml-orchestrator is designed to be lightweight and free of external dependencies, ensuring efficient runtime performance without additional overhead.

Note: ml-orchestrator does not require the kfp package to parse or create Kubeflow components.

Note: To construct kfp pipelines and utilize the components, the kfp package is required.

Usage

please read the documentation

Metadata

Release files for ml-orchestrator 0.1.2

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ml_orchestrator-0.1.2-py3-none-any.whl Python 3 none any Details

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