RADIUSS Shared CI

In a hurry? Quickstart instructions can be found in our five-minute setup guide.

The RADIUSS Shared CI project provides a templated CI infrastructure for LC GitLab that we intend to be sufficiently flexible and general to be adopted and shared by any LLNL open-source projects hosted on GitHub.

The infrastructure is project-agnostic. Each machine we support has a templated pipeline ready for use. Users only need to add jobs for machines of interest and set some variables according to their needs.

Projects using the RADIUSS Shared CI infrastructure keep control over their build and test process. The pre-requisite being that this process can be invoked through a single command line.

A project is then built and tested as usual and most of the CI infrastructure is shared to reduce duplicated effort and ease maintenance for individual projects.

Note

LLNL’s RADIUSS project (Rapid Application Development via an Institutional Universal Software Stack) aims to broaden usage across LLNL and the open source community of a set of libraries and tools used for HPC scientific application development.

Background and Motivation

LLNL open-source projects are prevalently hosted on GitHub. However, for those meant to run on Livermore Computing (LC) systems, testing automatically on LC GitLab instance is paramount.

Initially, the RADIUSS Shared CI infrastructure was designed for projects also using Spack to describe the toolchain and build the dependencies. This “build infrastructure” is defined in RADIUSS Spack Configs documentation.

Cool features provided by RADIUSS Shared CI

  • A shared configuration, for reduced maintenance.

  • Ability to define your jobs command and add as many jobs as desired.

  • Leverage GitLab child pipelines and components to keep the pipeline readable.

  • Each child pipeline reports independently to GitHub: one status per machine.

  • Projects can extend their pipeline at will with additional stages.

  • Print an exact reproducer in the logs.

  • Provide a template for a pipeline dedicated to performance measurements.

  • Optimize machine usage with shared allocation (optional).

  • Filter out pipelines coming from a mirrored draft Pull Request (optional).

Getting Started

User Documentation

Developer Documentation

Reference