Question

可以使用joblib在python中对函数进行多次调用。

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

给予：[0, 1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 196, 225, 256, 289, 324, 361]

但是，我真正想要的是并行调用类实例列表上的类函数。该函数只存储一个类变量。然后我会访问这个变量。

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

这会出错：

追踪（最近一次呼叫最后一次）：

文件“”，第1行，in       runfile（'G：/ My Drive / CODE / stackoverflow / parallel_classfunc / parallel_classfunc.py'，   wdir ='G：/我的驱动器/ CODE / stackoverflow / parallel_classfunc'）

文件   “C：\ ProgramData \ Anaconda2 \ LIB \站点包\ Spyder的\ utils的\网站\ sitecustomize.py”   第710行，在runfile中       execfile（filename，namespace）

文件   “C：\ ProgramData \ Anaconda2 \ LIB \站点包\ Spyder的\ utils的\网站\ sitecustomize.py”   第86行，在execfile中       exec（compile（scripttext，filename，'exec'），glob，loc）

档案“G：/我的   驱动器/ CODE /计算器/ parallel_classfunc / parallel_classfunc.py”   第12行，在       并行（n_jobs = 4）（延迟（运行中运行的run.p（）））

文件   “C：\ ProgramData \ Anaconda2 \ lib \ site-packages \ joblib \ parallel.py”，行   183，在延迟       pickle.dumps（功能）

文件“C：\ ProgramData \ Anaconda2 \ lib \ copy_reg.py”，第70行，in   _reduce_ex       引发TypeError，“无法选择％s对象”％base。 name

TypeError：无法pickle生成器对象

如何将joblib用于这样的类？还是有更好的方法？

Answer 1

如何 是否可以将 joblib 与此类使用？

让我们首先提出一些代码抛光：

并非所有内容都符合joblib.Parallel()( delayed() )呼叫签名功能：

# >>> type( runs )                        <type 'list'>
# >>> type( runs[0] )                     <class '__main__.A'>
# >>> type( run.p() for run in runs )     <type 'generator'>

所以，让我们让DEMO对象“通过” 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
               )

结果？充当魅力！

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

使用python joblib调用并行类函数

1 个答案:

结果？充当魅力！