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Jug: A Task-Based Parallelization Framework

Jug allows you to write code that is broken up into tasks and run different tasks on different processors.

It uses the filesystem to communicate between processes and works correctly over NFS, so you can coordinate processes on different machines.

Jug is a pure Python implementation and should work on any platform.

Website: http://luispedro.org/software/jug

Documentation: http://packages.python.org/Jug

Video: On vimeo or showmedo

Mailing List: http://groups.google.com/group/jug-users

Short Example

Here is a one minute example. Save the following to a file called primes.py:

from jug import TaskGenerator
from time import sleep

@TaskGenerator
def is_prime(n):
    sleep(1.)
    for j in xrange(2,n-1):
        if (n % j) == 0:
            return False
    return True

primes100 = map(is_prime, xrange(2,101))

Of course, this is only for didactical purposes, normally you would use a better method. Similarly, the sleep function is so that it does not run too fast.

Now type jug status primes.py to get:

Task name                  Waiting       Ready    Finished     Running
----------------------------------------------------------------------
primes.is_prime                  0          99           0           0
......................................................................
Total:                           0          99           0           0

This tells you that you have 99 tasks called primes.is_prime ready to run. So run jug execute primes.py &. You can even run multiple instances in the background (if you have multiple cores, for example). After starting 4 instances and waiting a few seconds, you can check the status again (with jug status primes.py):

Task name                  Waiting       Ready    Finished     Running
----------------------------------------------------------------------
primes.is_prime                  0          63          32           4
......................................................................
Total:                           0          63          32           4

Now you have 32 tasks finished, 4 running, and 63 still ready. Eventually, they will all finish and you can inspect the results with jug shell primes.py. This will give you an ipython shell. The primes100 variable is available, but it is an ugly list of jug.Task objects. To get the actual value, you call the value function:

In [1]: primes100 = value(primes100)

In [2]: primes100[:10]
Out[2]: [True, True, False, True, False, True, False, False, False, True]

What's New

version 0.9.2 (Nov 4 2012): - More flexible mapreduce()/map() functions - Make TaskGenerator pickle()able and hash()able - Add invalidate() method to Task - Add --keep-going option to execute - Better help messsage

version 0.9.1 (Jun 11 2012): - Add --locks-only option to cleanup subcommand - Make cache file (for status subcommand) configurable - Add webstatus subcommand - Add bvalue() function - Fix bug in shell subcommand (value was not in global namespace) - Improve identity() - Fix bug in using Tasklets and --aggressive-unload - Fix bug with Tasklets and sleep-until/check

version 0.9: - In the presence of a barrier(), rerun the jugfile. This makes barrier much easier to use. - Add set_jugdir to public API - Added CompoundTaskGenerator - Support subclassing of Task - Avoid creating directories in file backend unless it is necessary - Add jug.mapreduce.reduce (which mimicks the builtin reduce)

For older version see ChangeLog file.

Roadmap

Version 1.0

Version 1.0 is just around the corner. After 0.8 is done, there really are not that many features left. More flexible configuration, a bit more caching, and we are done.

After version 1.0

I want to start adding bells&whistles through extensions. Things like timing, more active monitoring, &c.