Kubeflow Examples in Unified AI

A collection of Python scripts, Jupyter notebooks, Dockerfiles, and reproducible use cases demonstrating how to build, train, and deploy machine learning models using Kubeflow, the foundational AI tooling stack on Kubernetes within Unified AI.

Building documentation locally

This section describes how to install dependencies and to build the documentation for the project.

# 1. Install uv (macOS and Linux)
curl -LsSf https://astral.sh/uv/install.sh | sh
# 2. Install dependencies and run mkdocs serve
uv sync
uv run mkdocs serve

The site will be served at: http://127.0.0.1:8000/

Pre-commit hooks for end-of-file-fixer, mixed-line-ending, trailing-whitespace and detect-secrets

uv run pre-commit run -a

Launch Jupyter locally

uv run jupyter notebook

Clone repository

You need to authorize a personal access token for use with single sign-on

git clone https://github.com/ucl-arc-unified-ai/kubeflow-examples.git