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.
Python 2.6/2.7 and Python 3.3 are supported.
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 range(2,n-1): if (n % j) == 0: return False return True primes100 = [is_prime(n) for n in range(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 : primes100 = value(primes100) In : primes100[:10] Out: [True, True, False, True, False, True, False, False, False, True]
version 1.1 (Tue Mar 3 2015) - Python 3 compatibility fixes - fsync(directory) in file backend - Jug hooks (still mostly undocumented, but already enabling internal code simplification)
version 1.0 (Tue May 20 2014) - Adapt status output to terminal width (by Alex Ford) - Add a newline at the end of lockfiles for file backend - Add –cache-file option to specify file for status --cache
version 0.9.7 (Tue Feb 18 2014)
- Fix use of numpy subclasses
- Fix redis URL parsing
- Fix shell for newer versions of IPython
- Correctly fall back on non-sqlite status
- Allow user to call set_jugdir() inside jugfile
version 0.9.6 (Tue Aug 6 2013)
- Faster decoding
- Add jug-execute script
- Add describe() function
- Add write_task_out() function
version 0.9.5 (May 27 2013)
- Added debug mode
- Even better map.reduce.map using blocked access
- Python 3 support
- Documentation improvements
version 0.9.4 (Apr 15 2013)
- Add CustomHash wrapper to set __jug_hash__
- Print traceback on import error
- Exit when no progress is made even with barrier
- Use Tasklets for better jug.mapreduce.map
- Use Ipython debugger if available (patch by Alex Ford)
- Faster –aggressive-unload
- Add currymap() function
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 with Tasks (previously, slicing with tasks was not allowed)
For older version see ChangeLog file.
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