Search Results for "dynamicreports-examples"

When you run Act it reads in your GitHub Actions and determines the set of actions that need to be run. It uses the Docker API to either pull or build the necessary images, as defined in your workflow files, and finally determines the execution path based on the dependencies that were defined. Once it has the execution path, it then uses the Docker API to run containers for each action based on the images prepared earlier. The environment variables and filesystem are all configured to match...

Gitness is an open source development platform packed with the power of code hosting and automated DevOps pipelines. Gitness represents a massive investment in the next generation of drones. Where Drone focuses on continuous integration, Gitness adds source code hosting, bringing code management and pipelines closer together. The goal is for Gitness to eventually be at full parity with Drone in terms of pipeline capabilities, allowing users to seamlessly migrate from Drone to Gitness.

This repository collects a variety of example Jenkins Pipeline scripts, tips, snippets, and patterns for both the declarative and scripted pipeline syntaxes. It includes examples for global shared libraries, Jenkinsfile variations, and usage of plugins, to help users adopt pipelines in their Jenkins automation workflows. Examples of plugin integrations (e.g. artifact upload, workspace management). Tips & tricks for script construction and structure. Pipeline patterns and best practices.

...Cyclone is architectured with a low-level workflow engine that is application agnostic, offering capabilities like workflow DAG scheduling, resource lifecycle management and most importantly, a pluggable and extensible framework for extending the core APIs. Above which, Cyclone provides built-in support for high-level functionalities, with CI/CD pipelines and AI DevOps being two notable examples, and it is possible to expand to more use cases as well. With Cyclone, users end up with the flexibility of workflow orchestration and the usability of complete CI/CD and AI DevOps solutions. Parameterization: stage (unit of execution) can be parameterized to maximize configuration reuse.