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FireWorks workflow software

Project description

FireWorks allows you to define calculation workflows and execute them on remote computers, usually through a queueing system. Workflows are stored in a centralized database, and jobs are pulled from the database by registered computers.

Features

Unique features of FireWorks include:

  • Dynamic workflows that react to results programmatically. A job can be automatically restarted, modified, or cancelled in case of error or other condition. Entire workflows can be changed automatically based on calculation results.
  • Distribute calculations over multiple computing resources simultaneously.
  • Automated duplicate workflow detection
  • Plug-and-play on several large supercomputing clusters and queueing systems (future)
  • Web-based monitoring of workflows (future)

Limitations

FireWorks is intended for applications where realtime performance of the workflow software is not a big issue. For example, if you require steps in a workflow to execute within a few seconds of one another, FireWorks might not be for you. In addition, FireWorks is a centralized workflow system.

Contributing

TODO: add description

Technical Issues

Installation

  • Use pip-install
  • run python setup.py nosetests

Setup on clusters / Tutorial (Future)

TODO: link to another page…

TODO: add proper docs

  1. Create a subclass of QueueAdapter that handles queue issues - an example is PBSAdapterNersc
  2. Create an appropriate JobParameters file for your cluster - an example is provided.
  3. Try running rocket_launcher.py on your cluster with a test job config. See if it prints ‘howdy, you won’ or whatever.
  4. Try changing the executable to be the Rocket. See if it grabs a job properly…

Project details


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