我正在尝试将multiprocessing添加到某些代码中，这些代码具有我无法修改的功能。我想将这些函数作为作业异步提交到多处理池。我正在做的事情很像here所示的代码。但是，我不确定如何跟踪结果。如何知道返回结果与哪个应用函数对应？
要强调的重点是我不能修改现有的功能（其他东西依赖于它们保持原样），并且结果可以按照与将功能作业应用于其中的顺序不同的顺序返回。池。
感谢您对此的任何想法！
编辑：有些尝试代码如下：
import multiprocessing
from multiprocessing import Pool
import os
import signal
import time
import inspect
def multiply(multiplicand1=0, multiplicand2=0):
return multiplicand1*multiplicand2
def workFunctionTest(**kwargs):
time.sleep(3)
return kwargs
def printHR(object):
"""
This function prints a specified object in a human readable way.
"""
# dictionary
if isinstance(object, dict):
for key, value in sorted(object.items()):
print u'{a1}: {a2}'.format(a1=key, a2=value)
# list or tuple
elif isinstance(object, list) or isinstance(object, tuple):
for element in object:
print element
# other
else:
print object
class Job(object):
def __init__(
self,
workFunction=workFunctionTest,
workFunctionKeywordArguments={'testString': "hello world"},
workFunctionTimeout=1,
naturalLanguageString=None,
classInstance=None,
resultGetter=None,
result=None
):
self.workFunction=workFunction
self.workFunctionKeywordArguments=workFunctionKeywordArguments
self.workFunctionTimeout=workFunctionTimeout
self.naturalLanguageString=naturalLanguageString
self.classInstance=self.__class__.__name__
self.resultGetter=resultGetter
self.result=result
def description(self):
descriptionString=""
for key, value in sorted(vars(self).items()):
descriptionString+=str("{a1}:{a2} ".format(a1=key, a2=value))
return descriptionString
def printout(self):
"""
This method prints a dictionary of all data attributes.
"""
printHR(vars(self))
class JobGroup(object):
"""
This class acts as a container for jobs. The data attribute jobs is a list of job objects.
"""
def __init__(
self,
jobs=None,
naturalLanguageString="null",
classInstance=None,
result=None
):
self.jobs=jobs
self.naturalLanguageString=naturalLanguageString
self.classInstance=self.__class__.__name__
self.result=result
def description(self):
descriptionString=""
for key, value in sorted(vars(self).items()):
descriptionString+=str("{a1}:{a2} ".format(a1=key, a2=value))
return descriptionString
def printout(self):
"""
This method prints a dictionary of all data attributes.
"""
printHR(vars(self))
def initialise_processes():
signal.signal(signal.SIGINT, signal.SIG_IGN)
def execute(
jobObject=None,
numberOfProcesses=multiprocessing.cpu_count()
):
# Determine the current function name.
functionName=str(inspect.stack()[0][3])
def collateResults(result):
"""
This is a process pool callback function which collates a list of results returned.
"""
# Determine the caller function name.
functionName=str(inspect.stack()[1][3])
print("{a1}: result: {a2}".format(a1=functionName, a2=result))
results.append(result)
def getResults(job):
# Determine the current function name.
functionName=str(inspect.stack()[0][3])
while True:
try:
result=job.resultGetter.get(job.workFunctionTimeout)
break
except multiprocessing.TimeoutError:
print("{a1}: subprocess timeout for job".format(a1=functionName, a2=job.description()))
#job.result=result
return result
# Create a process pool.
pool1 = multiprocessing.Pool(numberOfProcesses, initialise_processes)
print("{a1}: pool {a2} of {a3} processes created".format(a1=functionName, a2=str(pool1), a3=str(numberOfProcesses)))
# Unpack the input job object and submit it to the process pool.
print("{a1}: unpacking and applying job object {a2} to pool...".format(a1=functionName, a2=jobObject))
if isinstance(jobObject, Job):
# If the input job object is a job, apply it to the pool with its associated timeout specification.
# Return a list of results.
job=jobObject
print("{a1}: job submitted to pool: {a2}".format(a1=functionName, a2=job.description()))
# Apply the job to the pool, saving the object pool.ApplyResult to the job object.
job.resultGetter=pool1.apply_async(
func=job.workFunction,
kwds=job.workFunctionKeywordArguments
)
# Get results.
# Acquire the job result with respect to the specified job timeout and apply this result to the job data attribute result.
print("{a1}: getting results for job...".format(a1=functionName))
job.result=getResults(job)
print("{a1}: job completed: {a2}".format(a1=functionName, a2=job.description()))
print("{a1}: job result: {a2}".format(a1=functionName, a2=job.result))
# Return the job result from execute.
return job.result
pool1.terminate()
pool1.join()
elif isinstance(jobObject, JobGroup):
# If the input job object is a job group, cycle through each job and apply it to the pool with its associated timeout specification.
for job in jobObject.jobs:
print("{a1}: job submitted to pool: {a2}".format(a1=functionName, a2=job.description()))
# Apply the job to the pool, saving the object pool.ApplyResult to the job object.
job.resultGetter=pool1.apply_async(
func=job.workFunction,
kwds=job.workFunctionKeywordArguments
)
# Get results.
# Cycle through each job and and append the result for the job to a list of results.
results=[]
for job in jobObject.jobs:
# Acquire the job result with respect to the specified job timeout and apply this result to the job data attribute result.
print("{a1}: getting results for job...".format(a1=functionName))
job.result=getResults(job)
print("{a1}: job completed: {a2}".format(a1=functionName, a2=job.description()))
#print("{a1}: job result: {a2}".format(a1=functionName, a2=job.result))
# Collate the results.
results.append(job.result)
# Apply the list of results to the job group data attribute results.
jobObject.results=results
print("{a1}: job group results: {a2}".format(a1=functionName, a2=jobObject.results))
# Return the job result list from execute.
return jobObject.results
pool1.terminate()
pool1.join()
else:
# invalid input object
print("{a1}: invalid job object {a2}".format(a1=functionName, a2=jobObject))
def main():
print('-'*80)
print("MULTIPROCESSING SYSTEM DEMONSTRATION\n")
# Create a job.
print("# creating a job...\n")
job1=Job(
workFunction=workFunctionTest,
workFunctionKeywordArguments={'testString': "hello world"},
workFunctionTimeout=4
)
print("- printout of new job object:")
job1.printout()
print("\n- printout of new job object in logging format:")
print job1.description()
# Create another job.
print("\n# creating another job...\n")
job2=Job(
workFunction=multiply,
workFunctionKeywordArguments={'multiplicand1': 2, 'multiplicand2': 3},
workFunctionTimeout=6
)
print("- printout of new job object:")
job2.printout()
print("\n- printout of new job object in logging format:")
print job2.description()
# Create a JobGroup object.
print("\n# creating a job group (of jobs 1 and 2)...\n")
jobGroup1=JobGroup(
jobs=[job1, job2],
)
print("- printout of new job group object:")
jobGroup1.printout()
print("\n- printout of new job group object in logging format:")
print jobGroup1.description()
# Submit the job group.
print("\nready to submit job group")
response=raw_input("\nPress Enter to continue...\n")
execute(jobGroup1)
response=raw_input("\nNote the results printed above. Press Enter to continue the demonstration.\n")
# Demonstrate timeout.
print("\n # creating a new job in order to demonstrate timeout functionality...\n")
job3=Job(
workFunction=workFunctionTest,
workFunctionKeywordArguments={'testString': "hello world"},
workFunctionTimeout=1
)
print("- printout of new job object:")
job3.printout()
print("\n- printout of new job object in logging format:")
print job3.description()
print("\nNote the timeout specification of only 1 second.")
# Submit the job.
print("\nready to submit job")
response=raw_input("\nPress Enter to continue...\n")
execute(job3)
response=raw_input("\nNote the recognition of timeouts printed above. This concludes the demonstration.")
print('-'*80)
if __name__ == '__main__':
main()
编辑：由于以下原因，此问题已被暂停[
]
“要求代码的问题必须表明对要解决的问题的最小理解。包括尝试的解决方案，为什么它们不起作用，以及预期的结果。另请参阅：Stack Overflow question checklist”
这个问题不是要求代码;它要求思想，一般指导。对正在考虑的问题的最小理解得到了证明（注意正确使用术语“多处理”，“池”和“异步”并注意the reference to prior code）。关于尝试的解决方案，我承认在解决方案上的尝试努力将是有益的。我现在已经添加了这样的代码。我希望我已经解决了导致[暂停]状态的问题。
答案 0 :(得分：19)
在没有看到实际代码的情况下，我只能回答一般性问题。但有两种一般的解决方案。
首先，不要使用callback而忽略AsyncResult，而是将它们存储在某种集合中。然后你就可以使用那个集合了。例如，如果您希望能够使用该函数作为键查找函数的结果，只需创建一个键入函数的dict：
def in_parallel(funcs):
results = {}
pool = mp.Pool()
for func in funcs:
results[func] = pool.apply_async(func)
pool.close()
pool.join()
return {func: result.get() for func, result in results.items()}
或者，您可以更改回调函数，以便按键将结果存储在集合中。例如：
def in_parallel(funcs):
results = {}
pool = mp.Pool()
for func in funcs:
def callback(result, func=func):
results[func] = result
pool.apply_async(func, callback=callback)
pool.close()
pool.join()
return results
我正在使用函数本身作为密钥。但是你想要使用索引，这同样容易。您拥有的任何价值，都可以用作关键。
与此同时，您链接的示例实际上只是在一堆参数上调用相同的函数，等待所有参数完成，并将结果保留为任意顺序的迭代。这正是imap_unordered所做的，但更简单。你可以用链接代码替换整个复杂的东西：
pool = mp.Pool()
results = list(pool.imap_unordered(foo_pool, range(10)))
pool.close()
pool.join()
然后，如果您希望结果按原始顺序而不是按任意顺序排列，则可以改为imap或map。所以：
pool = mp.Pool()
results = pool.map(foo_pool, range(10))
pool.close()
pool.join()
如果您需要类似但过于复杂的内容以适应map范例，concurrent.futures可能会让您的生活比multiprocessing更容易。如果您使用的是Python 2.x，则必须安装the backport。但是你可以用AsyncResult或callback s（或map）来做更难的事情，比如将一大堆期货组合成一个大的未来。请参阅链接文档中的示例。
最后一点：
需要强调的重点是我无法修改现有的功能......
如果无法修改功能，可以随时将其包装。例如，假设我有一个函数返回一个数字的平方，但我正在尝试异步地构建一个dict映射数字到它们的方块，所以我需要将原始数字作为结果的一部分。这很简单：
def number_and_square(x):
return x, square(x)
现在，我可以apply_async(number_and_square)而不仅仅是square，并获得我想要的结果。
我在上面的示例中没有这样做，因为在第一种情况下我将密钥存储在来自调用端的集合中，在第二种情况下，我将其绑定到回调函数中。但是将它绑定到函数的包装器中就像这两者中的任何一个一样简单，并且当这两者都不合适时，它们都是合适的。