FireWorks workflow software
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.
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)
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.
TODO: add description
run python setup.py nosetests
Setup on clusters / Tutorial (Future)
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TODO: add proper docs
Create a subclass of QueueAdapter that handles queue issues - an example is PBSAdapterNersc
Create an appropriate JobParameters file for your cluster - an example is provided.
Try running rocket_launcher.py on your cluster with a test job config. See if it prints ‘howdy, you won’ or whatever.
Try changing the executable to be the Rocket. See if it grabs a job properly…
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