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A Task Based Parallelization Framework

Project description

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*: ` <>`_

*Documentation*: ` <>`_

*Video*: On `vimeo <>`_ or `showmedo

*Mailing List*: `

Short Example

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

from jug import TaskGenerator
from time import sleep

def is_prime(n):
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

Now type ``jug status`` 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 &``. 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

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``.
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**:
- 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)

version **0.8.1**:
- Fix redis backend for new version of client module
- Faster file store for large files
- Fix ``invalidate`` with Tasklets
- Install tests and have them be runnable
- Changed hash computation method. This has a special case on numpy arrays
(for speed) and is more extensible through a ``__jug_hash__`` hook
- Fix bug with ``Tasklet`` dependencies not being properly taken into account
- Fix ``shell`` subcommand in newer versions of ipython
- Add ``__file__`` attribute to fake jugmodule

version **0.8**:
- Tasklets
- Fix bugs in sleep-until and cleanup
- Fix bugs with CompoundTask (you needed to run jug execute twice before)

version **0.7.4**:
- Fix case where ~/.jug/configrc does not exist
- Print host name to lock file on file_store
- Refactored implementation of options
- Fix unloading tasks that have not run
- Fix mapreduce for empty input

Version **0.7.3**:
- Parse ~/.jug/configrc
- Fix bug with waiting times
- Special case saving of numpy arrays
- Add more expressive jugdir syntax
- Save dict_store backend to disk

Version **0.7.2**:
- included missing files in the distribution

Version **0.7.1**:
- ``sleep-until`` subcommand
- bugfixes


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.

Project details

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