如何跟踪从多处理p返回的异步结果

Question

我正试图将multiprocessing添加到一些代码中，这些代码具有我无法修改的函数。我想异步地将这些函数作为作业提交给多处理池。我正在做的事情很像所示的代码here。但是，我不知道如何跟踪结果。如何知道返回的结果对应于哪个应用函数？

需要强调的重要一点是，我不能修改现有函数（其他东西依赖于它们保持原样），结果可以按照不同于函数作业应用于池的顺序返回。

谢谢你对此的任何想法！

编辑：下面是一些尝试代码：

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：此问题已被[搁置]，原因如下：

"Questions asking for code must demonstrate a minimal understanding of the problem being solved. Include attempted solutions, why they didn't work, and the expected results. See also: Stack Overflow question checklist"

这个问题不是请求代码，而是请求思想和一般指导。对所考虑的问题有一个最低限度的理解（注意术语“多处理”、“池”和“异步”的正确使用，以及注意the reference to prior code）。关于尝试的解决办法，我承认，尝试的解决办法将是有益的。我现在已经添加了这样的代码。我希望我已经解决了引起[搁置]地位的关切。

Answer 1

没有看到实际的代码，我只能笼统地回答。但有两个普遍的解决办法。

首先，不要使用callback并忽略AsyncResults，而是将它们存储在某种集合中。然后你就可以用这个收藏了。例如，如果希望能够使用某个函数作为键来查找该函数的结果，只需使用以下函数创建一个键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，可以更改回调函数以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

---

我用函数本身作为一个键。但是你想用索引来代替，这同样简单。任何你拥有的价值，你都可以作为一把钥匙。

---

同时，您链接的示例实际上只是对一堆参数调用同一个函数，等待它们全部完成，并以任意顺序将结果保留为某些iterable。这正是^{}所做的，但要简单得多。您可以用以下内容替换链接代码中的所有复杂内容：

pool = mp.Pool()
results = list(pool.imap_unordered(foo_pool, range(10)))
pool.close()
pool.join()

然后，如果希望结果按原始顺序而不是任意顺序排列，可以切换到imap或map。所以：

pool = mp.Pool()
results = pool.map(foo_pool, range(10))
pool.close()
pool.join()

如果你需要类似但过于复杂的东西来适应map范式，^{}可能会让你的生活比multiprocessing更轻松。如果您使用的是Python 2.x，则必须安装the backport。但是你可以用AsyncResults或callbacks（或map）做一些更难做的事情，就像把一大堆未来组成一个大未来。请参阅链接文档中的示例。

---

最后一句话：

The important points to emphasise are that I can not modify the existing functions…

如果不能修改函数，则始终可以将其包装。例如，假设我有一个函数，它返回一个数字的平方，但是我正在尝试异步构建一个dict映射数字到它们的平方，所以我也需要将原始数字作为结果的一部分。很简单：

def number_and_square(x):
    return x, square(x)

现在，我只需要apply_async(number_and_square)而不是square，就可以得到我想要的结果。

在上面的例子中，我没有这样做，因为在第一种情况下，我从调用端将密钥存储到集合中，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与这两种方法一样简单，并且在这两种方法都不适用的情况下都是合适的。