Computational Workflow Engine

DAGonStar is a simple Python-based workflow engine that runs jobs on everything from the local machine to distributed virtual HPC clusters hosted in private and public clouds. For a comprehensive lists of all known computational workflow systems, see Computational Data Analysis Workflow Systems maintained by the CWL community. We unite the

A registry for computational workflows. The WorkflowHub is a registry for describing, sharing and publishing scientific computational workflows, irrespective of their type, development and

Coflux is an open-source workflow engine. Use it to orchestrate and observe computational workflows, defined in plain Python. Suitable for data pipelines, background tasks, chat bots.

Parametrization is built into its core using the powerful Jinja templating engine. Features. Anyone with Python knowledge can deploy a workflow. Apache Airflow does not limit the scope of your pipelines you can use it to build ML models, transfer data, manage your infrastructure, and more.

A curated list of awesome open source workflow engines. Full fledged product. Activepieces - Open source no-code business automation, alternative to Zapier AiiDA - Open source workflow manager for computational science with strong focus on performance, provenance, and extensibility. Airflow - Python-based platform for running directed acyclic graphs DAGs of tasks

6 open source workflow engine platforms to consider. Several open source workflow engine tools are available. The six detailed here were selected based on their popularity on GitHub and their support for novel IT operations automation capabilities. Some of these platforms received at least 10,000 stars on GitHub. 1. Apache Airflow

Mistral - Python based workflow engine by the Open Stack project. Moa - Lightweight workflows in bioinformatics. Nextflow - Flow-based computational toolkit for reproducible and scalable bioinformatics pipelines. nFlow - Embeddable JVM-based workflow engine with high availability, fault tolerance, and support for multiple databases. Additional

Open-source workflow engine. How it works Blog Docs GitHub Open main menu. Version 0.7 released. Open-source workflow engine. Orchestrate and observe computational workflows defined in plain Python. Suitable for data pipelines, background tasks, chat bots. Memo-ise side-effecting or slow tasks so that you can re-run a workflow without

Computational workflow managers further extend this abstraction, providing high level tools for managing data and tools, aiming to help users to design and run computational workflows more easily. A computational workflow engine provides an interface for launching workflows, specifying and handling inputs and collecting and exporting outputs

Computational workflows are composed of modular building blocks that have been prepared with standardised interfaces to be linked together and run by a computational engine. Thus, the key characteristic is the separation of the workflow specification from its execution. Capturing the control flow order between components explicitly exposes the