Data Science Stack documentation

Data science stack (DSS) is a ready-to-run environment for machine learning and data science. It’s built on open-source tooling (including MicroK8s, JupyterLab and MLflow) and usable on any Ubuntu/Snap-enabled workstation.

DSS provides a Command Line Interface (CLI) for managing containerised ML environments images such as PyTorch or TensorFlow, on top of MicroK8s.

Typically, creating ML environments on a workstation involves complex and hard-to-reverse configuration. DSS solves this problem by making accessible, production-ready, isolated and reproducible ML environments, that make full use of a workstation’s GPUs.

Both ML beginners and engineers who need to build complex development and runtime environments will see set-up time reduced to a minimum, allowing them to get on with useful work within minutes.

In this documentation

Tutorial

Get started - a hands-on introduction to DSS for newcomers

How-to guides

Step-by-step guides covering key operations and common tasks with DSS

Explanation

Discussion and clarification of key topics

Project and community

Data Science Stack is an open-source project that values its community. We warmly welcome contributions, suggestions, fixes, and constructive feedback from everyone.