Pre-release
This release is a pre-release and may not be stable for production use.
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
1 maintainer
Metadata
1 maintainer
Release files for ml-orchestrator 0.1.1b0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| ml_orchestrator-0.1.1b0.tar.gz | 9.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| ml_orchestrator-0.1.1b0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 21.8 kB
Release files / ml_orchestrator-0.1.1b0.tar.gz
| Download URL | ml_orchestrator-0.1.1b0.tar.gz |
|---|---|
| Size | 9.1 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Release files / ml_orchestrator-0.1.1b0-py3-none-any.whl
| Download URL | ml_orchestrator-0.1.1b0-py3-none-any.whl |
|---|---|
| Size | 12.7 kB |
| Tags | Python 3 |
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