Examples: start here¶
This section is organised by what you want to do: ingest data, build machine learning, keep models honest, work with language models. Inside every example there is a small toggle showing how to run it on each environment.
The toggle remembers your choice. Click Databricks once and every example on every page shows you the Databricks way. Click Laptop and everything switches. The pipelines themselves never change between tabs. That is the whole point of the engine.
All example code lives in one repository:
ubunye-ai-ecosystems/ubunye-examples on GitHub
Before you start, once per environment¶
Sign up for Databricks Free Edition (no card). In the workspace choose Create, then Git folder, and paste the examples repository URL. It is public, so you need no token. Every example is then a notebook you open and run.
You need Python, Java 17 and an internet connection.
git clone https://github.com/ubunye-ai-ecosystems/ubunye-examples
cd ubunye-examples
pip install "pyspark==3.5.3" "delta-spark==3.2.0" "ubunye-engine[spark,ml]" "requests"
The scripts under platforms/ handle Spark, Delta and a local metastore.
Examples that read the Databricks sample tables need seeded stand-ins with
the same shape, created once:
You need Docker only. Build the image once:
The machine learning examples use a second image with the open source model weights baked in, so nothing is downloaded at run time:
Why you can trust these pages¶
Every example has been run for real, with its output inspected. One test goes further: it runs the same pipeline on the laptop, in Docker, on Kubernetes, against object storage and on Databricks, then fails the build unless all of them produced the same output, byte for byte. See Run anywhere.