It is possible to make multiple calls to a function in python using joblib.
from joblib import Parallel, delayed
def normal(x):
print "Normal", x
return x**2
if __name__ == '__main__':
results = Parallel(n_jobs=2)(delayed(normal)(x) for x in range(20))
print results
Gives: [0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 196, 225, 256, 289, 324, 361]
However, what I really want is to call a class function on a list of class instances in parallel. The function simply stores a class variable. Then later I will access this variable.
from joblib import Parallel, delayed
class A(object):
def __init__(self, x):
self.x = x
def p(self):
self.y = self.x**2
if __name__ == '__main__':
runs = [A(x) for x in range(20)]
Parallel(n_jobs=4)(delayed(run.p() for run in runs))
for run in runs:
print run.y
This gives an error:
Traceback (most recent call last):
File "", line 1, in runfile('G:/My Drive/CODE/stackoverflow/parallel_classfunc/parallel_classfunc.py', wdir='G:/My Drive/CODE/stackoverflow/parallel_classfunc')
File "C:\ProgramData\Anaconda2\lib\site-packages\spyder\utils\site\sitecustomize.py", line 710, in runfile execfile(filename, namespace)
File "C:\ProgramData\Anaconda2\lib\site-packages\spyder\utils\site\sitecustomize.py", line 86, in execfile exec(compile(scripttext, filename, 'exec'), glob, loc)
File "G:/My Drive/CODE/stackoverflow/parallel_classfunc/parallel_classfunc.py", line 12, in Parallel(n_jobs=4)(delayed(run.p() for run in runs))
File "C:\ProgramData\Anaconda2\lib\site-packages\joblib\parallel.py", line 183, in delayed pickle.dumps(function)
File "C:\ProgramData\Anaconda2\lib\copy_reg.py", line 70, in _reduce_ex raise TypeError, "can't pickle %s objects" % base.name
TypeError: can't pickle generator objects
How is it possible to use joblib with classes like this? Or is there a better approach?
How is it possible to use
joblibwith classes like this ?
Let's propose some code polishing first :
Not all things will fit the joblib.Parallel()( delayed() ) call-signature capabilities to swallow:
# >>> type( runs ) <type 'list'>
# >>> type( runs[0] ) <class '__main__.A'>
# >>> type( run.p() for run in runs ) <type 'generator'>
so, let's make the DEMO-objects to pass "through" aContainerFUN():
StackOverflow_DEMO_joblib.Parallel.py :
from sklearn.externals.joblib import Parallel, delayed
import time
class A( object ):
def __init__( self, x ):
self.x = x
self.y = "Defined on .__init__()"
def p( self ):
self.y = self.x**2
def aNormalFUN( aValueOfX ):
time.sleep( float( aValueOfX ) / 10. )
print ": aNormalFUN() has got aValueOfX == {0:} to process.".format( aValueOfX )
return aValueOfX * aValueOfX
def aContainerFUN( aPayloadOBJECT ):
time.sleep( float( aPayloadOBJECT.x ) / 10. )
# try: except: finally:
pass; aPayloadOBJECT.p()
print "| aContainerFUN: has got aPayloadOBJECT.id({0:}) to process. [ Has made .y == {1:}, given .x == {2: } ]".format( id( aPayloadOBJECT ), aPayloadOBJECT.y, aPayloadOBJECT.x )
time.sleep( 1 )
if __name__ == '__main__':
# ------------------------------------------------------------------
results = Parallel( n_jobs = 2
)( delayed( aNormalFUN )( aParameterX )
for aParameterX in range( 11, 21 )
)
print results
print '.'
# ------------------------------------------------------------------
pass; runs = [ A( x ) for x in range( 11, 21 ) ]
# >>> type( runs ) <type 'list'>
# >>> type( runs[0] ) <class '__main__.A'>
# >>> type( run.p() for run in runs ) <type 'generator'>
Parallel( verbose = 10,
n_jobs = 2
)( delayed( aContainerFUN )( run )
for run in runs
)
C:\Python27.anaconda> python StackOverflow_DEMO_joblib.Parallel.py
: aNormalFUN() has got aValueOfX == 11 to process.
: aNormalFUN() has got aValueOfX == 12 to process.
: aNormalFUN() has got aValueOfX == 13 to process.
: aNormalFUN() has got aValueOfX == 14 to process.
: aNormalFUN() has got aValueOfX == 15 to process.
: aNormalFUN() has got aValueOfX == 16 to process.
: aNormalFUN() has got aValueOfX == 17 to process.
: aNormalFUN() has got aValueOfX == 18 to process.
: aNormalFUN() has got aValueOfX == 19 to process.
: aNormalFUN() has got aValueOfX == 20 to process.
[121, 144, 169, 196, 225, 256, 289, 324, 361, 400]
.
| aContainerFUN: has got aPayloadOBJECT.id(50369168) to process. [ Has made .y == 121, given .x == 11 ]
| aContainerFUN: has got aPayloadOBJECT.id(50369168) to process. [ Has made .y == 144, given .x == 12 ]
[Parallel(n_jobs=2)]: Done 1 tasks | elapsed: 2.4s
| aContainerFUN: has got aPayloadOBJECT.id(12896752) to process. [ Has made .y == 169, given .x == 13 ]
| aContainerFUN: has got aPayloadOBJECT.id(12896752) to process. [ Has made .y == 196, given .x == 14 ]
[Parallel(n_jobs=2)]: Done 4 tasks | elapsed: 4.9s
| aContainerFUN: has got aPayloadOBJECT.id(12856464) to process. [ Has made .y == 225, given .x == 15 ]
| aContainerFUN: has got aPayloadOBJECT.id(12856464) to process. [ Has made .y == 256, given .x == 16 ]
| aContainerFUN: has got aPayloadOBJECT.id(50368592) to process. [ Has made .y == 289, given .x == 17 ]
| aContainerFUN: has got aPayloadOBJECT.id(50368592) to process. [ Has made .y == 324, given .x == 18 ]
| aContainerFUN: has got aPayloadOBJECT.id(12856528) to process. [ Has made .y == 361, given .x == 19 ]
| aContainerFUN: has got aPayloadOBJECT.id(12856528) to process. [ Has made .y == 400, given .x == 20 ]
[Parallel(n_jobs=2)]: Done 10 out of 10 | elapsed: 13.3s finished