kubeflow extension
Project description
Defining Kubeflow Pipeline (KFP) Components with Python Dataclasses
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
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