https://github.com/DanielAvdar/ml-orchestrator

Software engineering approach for building kubeflow components
https://github.com/DanielAvdar/ml-orchestrator

Last synced: 10 days ago
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Software engineering approach for building kubeflow components

• Host: GitHub
• URL: https://github.com/DanielAvdar/ml-orchestrator
• Owner: DanielAvdar
• License: mit
• Created: 2024-02-18T11:28:32.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-04-21T05:30:38.000Z (18 days ago)
• Last Synced: 2025-04-22T16:55:50.889Z (17 days ago)
• Language: Python
• Homepage:
• Size: 5.63 MB
• Stars: 3
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Defining Kubeflow Pipeline (KFP) Components with Python Dataclasses

[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ml-orchestrator)](https://pypi.org/project/ml-orchestrator/)

[![version](https://img.shields.io/pypi/v/ml-orchestrator)](https://img.shields.io/pypi/v/ml-orchestrator)

[![License](https://img.shields.io/:license-MIT-blue.svg)](https://opensource.org/licenses/MIT)

![OS](https://img.shields.io/badge/ubuntu-blue?logo=ubuntu)

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[![Tests](https://github.com/DanielAvdar/ml-orchestrator/actions/workflows/ci.yml/badge.svg)](https://github.com/DanielAvdar/ml-orchestrator/actions/workflows/ci.yml)

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[![codecov](https://codecov.io/gh/DanielAvdar/ml-orchestrator/graph/badge.svg?token=N0V9KANTG2)](https://codecov.io/gh/DanielAvdar/ml-orchestrator)

[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)

![Last Commit](https://img.shields.io/github/last-commit/DanielAvdar/ml-orchestrator/main)

## 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

```bash

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](https://ml-orchestrator.readthedocs.io/en/latest/)