Kubeflow Pipelines Components Repository

Welcome to the official Kubeflow Pipelines Components repository! This is the centralized hub for reusable components and pipelines within the Kubeflow ecosystem, providing a structured collection of AI workflow building blocks for Kubernetes.

🎯 Purpose

The Kubeflow Pipelines Components repository serves as:

• Centralized Asset Library: A dedicated space for hosting reusable components and pipelines, promoting consistency and ease of access across the Kubeflow ecosystem
• Standardized Documentation Hub: Ensuring each component and pipeline includes comprehensive documentation and metadata for better discoverability and usability
• Community Collaboration Platform: Providing clear guidelines and governance to encourage contributions from the community
• Automated Quality Assurance: Implementing automated testing and maintenance processes to ensure reliability and up-to-date status of all assets

📦 Repository Structure

├── components/ # Core reusable components
│   ├── <component category>/
│   ├── <component category>/
│   └── <component category>/
│
├── pipelines/ # Complete AI pipelines
│   ├── <component category>/
│   ├── <component category>/
│   └── <component category>/
│
├── docs/                # Documentation
└── scripts/             # Utility scripts

Component Categories

• Training: Components for model training, hyperparameter tuning, and optimization
• Evaluation: Model evaluation, metrics calculation, and performance analysis
• Data Processing: Data preparation, transformation, and feature engineering
• Deployment: Model serving, endpoint management, and production deployment

🚀 Installation

⚠️ Work in Progress: This repository is currently under development. The packages described below are not yet available on PyPI. This section outlines the planned installation process for when the packages are released.

Prerequisites

• Python 3.11 or later
• Kubeflow Pipelines SDK

Install Components (Coming Soon)

Install the official Kubeflow SDK with components:

# Not yet available - coming soon!
pip install kubeflow

Verify Installation

Once the packages are available, you'll be able to verify the installation:

# Coming soon - example verification code
from kfp_components.components import training, evaluation, data_processing

# Example: Use a training component
from kfp_components.components.training import my_component

# List available components
print(dir(training))

💻 Usage

Using Components in Your Pipeline

from kfp import dsl
from kfp_components.components.training import model_trainer
from kfp_components.components.evaluation import model_evaluator

@dsl.pipeline(
    name="my-ai-pipeline",
    description="Example pipeline using KFP components"
)
def my_pipeline(
    dataset_path: str,
    model_name: str
):
    # Train model using a reusable component
    training_task = model_trainer(
        dataset=dataset_path,
        model_name=model_name
    )
    
    # Evaluate the trained model
    evaluation_task = model_evaluator(
        model=training_task.outputs["model"],
        test_dataset=dataset_path
    )

Component Metadata

Each component includes standardized metadata:

• Stability: alpha, beta, or stable
• Dependencies: Kubeflow and external service requirements
• Last Verified: Date of last verification
• Documentation: Links to detailed documentation

📚 Documentation

Core Documentation

• Component Specification: Detailed information on component structure and definition
• Creating Components: Guidelines for authoring new components
• Pipeline Concepts: Overview of pipelines in Kubeflow

Repository Documentation

• Onboarding Guide: Getting started for new contributors
• Contributing Guidelines: How to contribute components and pipelines
• Best Practices: Component authoring best practices
• Governance: Repository governance and ownership model
• Agent Guidelines: Guidance for code-generation agents

🤝 Contributing

We welcome contributions from the community! To contribute:

1. Review Guidelines: Read our Contributing Guidelines
2. Follow Standards: Ensure your component includes:
   • component.py or pipeline.py - The implementation
   • metadata.yaml - Standardized metadata
   • README.md - Component documentation
   • OWNERS - Maintainer information
   • tests/ - Unit tests
   • example_pipelines.py - Usage examples
3. Submit PR: Open a pull request with your contribution

Quality Standards

All contributions must:

• Pass linting and formatting checks (Black, pydocstyle)
• Include comprehensive docstrings
• Compile successfully with kfp.compiler
• Include metadata with fresh lastVerified date
• Pass automated CI/CD checks

🔧 Maintenance

Automated Maintenance

The repository includes automated maintenance processes:

• Verification Reminders: Components are flagged when lastVerified is older than 9 months
• Dependency Updates: Dependabot monitors and suggests dependency updates
• Security Scanning: Critical CVEs trigger automated remediation PRs
• Removal Process: Components not verified within 12 months are proposed for removal

Component Ownership

Each component has designated owners listed in its OWNERS file who:

• Review and approve changes
• Update metadata and verification status
• Manage component lifecycle
• Respond to issues and questions

📦 Releases

• Core Package: kubeflow - Official Kubeflow SDK including community-maintained components
• Versioning: Follows semantic versioning aligned with Kubeflow releases
• Release Cadence: Regular releases aligned with Kubeflow Pipelines SDK

🔗 Links

• Kubeflow Website
• Kubeflow Pipelines
• Issue Tracker
• Community Slack
• Mailing List

📄 License

This project is licensed under the Apache License 2.0.

🙏 Acknowledgments

Thank you to all contributors and the Kubeflow community for making this repository possible!

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For questions, issues, or suggestions, please open an issue in our GitHub repository or reach out on the Kubeflow Slack.