Data Science Stack documentation¶
Data Science Stack (DSS) is a ready-to-run environment for Machine Learning (ML) and data science. It’s built on open-source tooling, including Canonical K8s, JupyterLab, and MLflow, and is usable on any Ubuntu/Snap-enabled workstation.
DSS provides a Command Line Interface (CLI) for managing containerised ML environment images such as PyTorch or TensorFlow, on top of Canonical K8s.
Typically, creating ML environments on a workstation involves complex and hard-to-reverse configurations. DSS solves this problem by providing accessible, production-ready, isolated, and reproducible ML environments that fully utilise 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 started with meaningful work within minutes.
In this documentation¶
Get started with DSS.
Step-by-step guides covering key operations and common tasks with DSS.
Learn about DSS system architecture.
Project and community¶
Data Science Stack is an open-source project that values its community. Contributions, suggestions, fixes, and constructive feedback are welcome from everyone.
Read our Code of conduct.
Contribute and report bugs.
Join the Discourse forum.
Talk to us on Matrix.