What can multiprocessing and dill do together?

multiprocessing makes some bad choices about pickling. Don't get me wrong, it makes some good choices that enable it to pickle certain types so they can be used in a pool's map function. However, since we have dill that can do the pickling, multiprocessing's own pickling becomes a bit limiting. Actually, if multiprocessing were to use pickle instead of cPickle... and also drop some of it's own pickling overrides, then dill could take over and give a much more full serialization for multiprocessing.

Until that happens, there's a fork of multiprocessing called pathos (the release version is a bit stale, unfortunately) that removes the above limitations. Pathos also adds some nice features that multiprocessing doesn't have, like multi-args in the map function. Pathos is due for a release, after some mild updating -- mostly conversion to python 3.x.

Python 2.7.5 (default, Sep 30 2013, 20:15:49) 
[GCC 4.2.1 (Apple Inc. build 5566)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import dill
>>> from pathos.multiprocessing import ProcessingPool    
>>> pool = ProcessingPool(nodes=4)
>>> result = pool.map(lambda x: x**2, range(10))
>>> result
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

and just to show off a little of what pathos.multiprocessing can do...

>>> def busy_add(x,y, delay=0.01):
...     for n in range(x):
...        x += n
...     for n in range(y):
...        y -= n
...     import time
...     time.sleep(delay)
...     return x + y
... 
>>> def busy_squared(x):
...     import time, random
...     time.sleep(2*random.random())
...     return x*x
... 
>>> def squared(x):
...     return x*x
... 
>>> def quad_factory(a=1, b=1, c=0):
...     def quad(x):
...         return a*x**2 + b*x + c
...     return quad
... 
>>> square_plus_one = quad_factory(2,0,1)
>>> 
>>> def test1(pool):
...     print pool
...     print "x: %s\n" % str(x)
...     print pool.map.__name__
...     start = time.time()
...     res = pool.map(squared, x)
...     print "time to results:", time.time() - start
...     print "y: %s\n" % str(res)
...     print pool.imap.__name__
...     start = time.time()
...     res = pool.imap(squared, x)
...     print "time to queue:", time.time() - start
...     start = time.time()
...     res = list(res)
...     print "time to results:", time.time() - start
...     print "y: %s\n" % str(res)
...     print pool.amap.__name__
...     start = time.time()
...     res = pool.amap(squared, x)
...     print "time to queue:", time.time() - start
...     start = time.time()
...     res = res.get()
...     print "time to results:", time.time() - start
...     print "y: %s\n" % str(res)
... 
>>> def test2(pool, items=4, delay=0):
...     _x = range(-items/2,items/2,2)
...     _y = range(len(_x))
...     _d = [delay]*len(_x)
...     print map
...     res1 = map(busy_squared, _x)
...     res2 = map(busy_add, _x, _y, _d)
...     print pool.map
...     _res1 = pool.map(busy_squared, _x)
...     _res2 = pool.map(busy_add, _x, _y, _d)
...     assert _res1 == res1
...     assert _res2 == res2
...     print pool.imap
...     _res1 = pool.imap(busy_squared, _x)
...     _res2 = pool.imap(busy_add, _x, _y, _d)
...     assert list(_res1) == res1
...     assert list(_res2) == res2
...     print pool.amap
...     _res1 = pool.amap(busy_squared, _x)
...     _res2 = pool.amap(busy_add, _x, _y, _d)
...     assert _res1.get() == res1
...     assert _res2.get() == res2
...     print ""
... 
>>> def test3(pool): # test against a function that should fail in pickle
...     print pool
...     print "x: %s\n" % str(x)
...     print pool.map.__name__
...     start = time.time()
...     res = pool.map(square_plus_one, x)
...     print "time to results:", time.time() - start
...     print "y: %s\n" % str(res)
... 
>>> def test4(pool, maxtries, delay):
...     print pool
...     m = pool.amap(busy_add, x, x)
...     tries = 0
...     while not m.ready():
...         time.sleep(delay)
...         tries += 1
...         print "TRY: %s" % tries
...         if tries >= maxtries:
...             print "TIMEOUT"
...             break
...     print m.get()
... 
>>> import time
>>> x = range(18)
>>> delay = 0.01
>>> items = 20
>>> maxtries = 20
>>> from pathos.multiprocessing import ProcessingPool as Pool
>>> pool = Pool(nodes=4)
>>> test1(pool)
<pool ProcessingPool(ncpus=4)>
x: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]

map
time to results: 0.0553691387177
y: [0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 196, 225, 256, 289]

imap
time to queue: 7.91549682617e-05
time to results: 0.102381229401
y: [0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 196, 225, 256, 289]

amap
time to queue: 7.08103179932e-05
time to results: 0.0489699840546
y: [0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 196, 225, 256, 289]

>>> test2(pool, items, delay)
<built-in function map>
<bound method ProcessingPool.map of <pool ProcessingPool(ncpus=4)>>
<bound method ProcessingPool.imap of <pool ProcessingPool(ncpus=4)>>
<bound method ProcessingPool.amap of <pool ProcessingPool(ncpus=4)>>

>>> test3(pool)
<pool ProcessingPool(ncpus=4)>
x: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]

map
time to results: 0.0523059368134
y: [1, 3, 9, 19, 33, 51, 73, 99, 129, 163, 201, 243, 289, 339, 393, 451, 513, 579]

>>> test4(pool, maxtries, delay)
<pool ProcessingPool(ncpus=4)>
TRY: 1
TRY: 2
TRY: 3
TRY: 4
TRY: 5
TRY: 6
TRY: 7
[0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34]

You may want to try using the multiprocessing_on_dill library, which is a fork of multiprocessing that implements dill on the backend.

For example, you can run:

>>> import multiprocessing_on_dill as multiprocessing
>>> with multiprocessing.Pool() as pool:
...     pool.map(lambda x: x**2, range(10))
... 
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]