Kubeflow Pipelines: How to Build your First Kubeflow Pipeline from Scratch

This repository aims to develop a step-by-step tutorial on how to build a Kubeflow Pipeline from scratch in your local machine.

If you want to know in detail about the detailed explanation of how to develop your first kubeflow pipeline, I recommend you take a look at the article: Kubeflow Pipelines: How to Build your First Kubeflow Pipeline from Scratch

Files
How to use
Contributing
Contact
License

1. Files

decision_tree: Contains the files to build the decision_tree component as well as the Dockerfile used to generate the component image.
logistic_regression: Contains the files to build the logistic_regression component as well as the Dockerfile used to generate the component image.
download_data: Contains the files to build the download_data component as well as the Dockerfile used to generate the component image.
pipeline.py: Contains the definition of the pipeline, which when executed generates the FirstPipeline.yaml file.

2. How to use

It is recommended to have previously installed kfp as well as configured kubeflow on top of kubernets or a minimal version such as minikube.
All the images are accesible in Docker Hub: https://hub.docker.com/repository/docker/fernandolpz/only-tests

3. Contributing

Feel free to fork the model and add your own suggestiongs.

Fork the Project
Create your Feature Branch (git checkout -b feature/YourGreatFeature)
Commit your Changes (git commit -m 'Add some YourGreatFeature')
Push to the Branch (git push origin feature/YourGreatFeature)
Open a Pull Request

5. Contact

If you have any question, feel free to reach me out at:

6. License

Distributed under the MIT License. See LICENSE.md for more information.