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
Mailing List: http://groups.google.com/group/jug-users
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]
version 0.9.3 (Dec 2 2012) - Fix parsing of ports on redis URL (patch by Alcides Viamontes) - Make hashing robust to different orders when using randomized hashing
(patch by Alcides Viamontes)
- Allow regex in invalidate command (patch by Alcides Viamontes)
- Add
--cache --clear
suboption to status - Allow builtin functions for tasks
- Fix status --cache`` (a general bug which seems to be triggered mainly by
bvalue()
usage). - Fix
CompoundTask
(broken by earlier__jug_hash__
hook introduction) - Make
Tasklets
more flexible by allowing slicing withTasks
(previously, slicing with tasks was not allowed)
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.
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.
I want to start adding bells&whistles through extensions. Things like timing, more active monitoring, &c.