FireWorks

FireWorks is a code for defining, managing, and executing scientific workflows. It can be used to automate most types of calculations over arbitrary computing resources, including those that have a queueing system.

Is FireWorks for me?

FireWorks is intended to be a friendly workflow software that is easy to get started with, but flexible enough to handle complicated use cases.

Some (but not all) of its features include:

  • Storage and management of workflows through MongoDB, a noSQL datastore that is flexible and easy to use.
  • A clean and powerful Python API for defining, submitting, running, and maintaining workflows.
  • Ability to distribute calculations over multiple worker nodes, each of which might use different a queueing system and process a different type of calculation.
  • Support for dynamic workflows that that react to results programmatically. You can pre-specify code that performs actions based on the output of a job (e.g., terminate a workflow, add a new step, or completely alter the workflow)
  • Automatic detection of duplicate sub-workflows - skip duplicated portions between two workflows while still running unique sections
  • Loosely-coupled and modular infrastructure that is intentionally hackable. Use some FireWorks components without using everything, and more easily adapt FireWorks to your application.
  • Monitoring of workflows through a web service
  • Plug-and-play on several large supercomputing clusters and queueing systems (future)
  • Package many small jobs into a single large job (useful for running on HPC machines that prefer a small number of large-CPU jobs). (future)

Limitations

Some limitations of FireWorks include:

  • FireWorks has not been stress-tested to hundreds of jobs within a single workflow.
  • FireWorks has not been stress-tested to millions of workflows.
  • FireWorks does not automatically optimize the distribution of computing tasks over worker nodes (e.g., to minimize data movement or to match jobs to appropriate hardware); however, you can define such optimizations yourself.
  • FireWorks has only been tested on Linux and Macintosh machines (and not Windows).

If you encounter any problems while using FireWorks, please let us know (see Contributing / Contact / Bug Reports).

Quickstart and Tutorials

Quickstart (“Wiggle your big toe”)

To get started with FireWorks, we suggest that you follow our quickstart tutorial, which should take about half an hour for experienced Python users and covers basic installation and usage.

Basic usage

After completing the quickstart, we suggest that you follow our core tutorials that cover the primary features of FireWorks. Depending on your application, you may not need to complete all the tutorials.

Managing jobs and deployment

This series of tutorials cover how to manage your jobs and deploy FireWorks in a production environment.

Contributing / Contact / Bug Reports

Want to see something added or changed? There are many ways to make that a reality! Some ways to get involved are:

  • Help us improve the documentation - tell us where you got ‘stuck’ and improve the install process for everyone.
  • Let us know if you need support for a queueing system or certain features.
  • Point us to areas of the code that are difficult to understand or use.
  • Contribute code! If you are interested in this option, please see our contribution guidelines.

The collaborative way to submit questions, issues / bug reports, and all other communication is through the FireWorks Github page. You can also contact: developer contact

The list of contributors to FireWorks can be found here.

License

FireWorks is released under a modified BSD license; the full text can be found here.

Comprehensive Documentation

Some comprehensive documentation is listed below (only for the brave!)