Question

我的代码（遗传优化算法的一部分）并行运行几个进程，等待所有进程完成，读取输出，然后用不同的输入重复。当我用60次重复测试时，一切都很好。由于它有效，我决定使用更现实的重复次数，200。我收到了这个错误：

File "/usr/lib/python2.7/threading.py", line 551, in __bootstrap_inner
 self.run()
File "/usr/lib/python2.7/threading.py", line 504, in run
 self.__target(*self.__args, **self.__kwargs)
File "/usr/lib/python2.7/multiprocessing/pool.py", line 302, in _handle_workers
 pool._maintain_pool()
File "/usr/lib/python2.7/multiprocessing/pool.py", line 206, in _maintain_pool
 self._repopulate_pool()
File "/usr/lib/python2.7/multiprocessing/pool.py", line 199, in _repopulate_pool
 w.start()
File "/usr/lib/python2.7/multiprocessing/process.py", line 130, in start
 self._popen = Popen(self)
File "/usr/lib/python2.7/multiprocessing/forking.py", line 120, in __init__
 self.pid = os.fork()
OSError: [Errno 12] Cannot allocate memory

以下是我使用pool的代码片段：

def RunMany(inputs):
from multiprocessing import cpu_count, Pool
proc=inputs[0]
pool=Pool(processes = proc) 
results=[]
for arg1 in inputs[1]:
    for arg2 in inputs[2]:
        for arg3 in inputs[3]:
            results.append(pool.apply_async(RunOne, args=(arg1, arg2, arg3)))
casenum=0
datadict=dict()
for p in results:
    #get results of simulation once it has finished
    datadict[casenum]=p.get() 
    casenum+=1
return datadict

RunOne函数在我创建的类中创建一个对象，使用计算量很大的python包来解决大约需要30秒的化学问题，并使用化学求解器的输出返回该对象。

因此，我的代码串行调用RunMany，然后RunMany并行调用RunOne。在我的测试中，我使用10个处理器（计算机有16个）和一个20个调用RunOne的池来调用RunOne。换句话说，len（arg1）* len（arg2）* len（arg3）= 20。当我的代码调用RunMany 60次时，一切正常，但是当我调用它200次时，我的内存耗尽。

这是否意味着某些过程本身无法正确清理？我有内存泄漏吗？如何确定是否有内存泄漏，如何找出泄漏的原因？在我的200重复循环中增长的唯一项目是从0大小到200长度的数字列表。我有一个来自我自己构建的自定义类的对象字典，但它的上限是一个长度50个条目 - 每次循环执行时，它会从字典中删除一个项目并将其替换为另一个项目。

修改：以下是调用RunMany的代码片段

for run in range(nruns):
    #create inputs object for RunMany using genetic methods. 
    #Either use starting "population" or create "child" inputs from successful previous runs
    datadict = RunMany(inputs)

    sumsquare=0
    for i in range(len(datadictsenk)): #input condition
        sumsquare+=Compare(datadict[i],Target[i]) #compare result to target

    with open(os.path.join(mainpath,'Outputs','output.txt'),'a') as f:
        f.write('\t'.join([str(x) for x in [inputs.name, sumsquare]])+'\n')

    Objective.append(sumsquare) #add sum of squares to list, to be plotted outside of loop
    population[inputs]=sumsquare #add/update the model in the "population", using the inputs object as a key, and it's objective function as the value
    if len(population)>initialpopulation:
        population = PopulationReduction(population) #reduce the "population" by "killing" unfit "genes"
    avgtime=(datetime.datetime.now()-starttime2)//(run+1)
    remaining=(nruns-run-1)*avgtime
    print(' Finished '+str(run+1)+' / ' +str(nruns)+'. Elapsed: '+str(datetime.datetime.now().replace(microsecond=0)-starttime)+' Remaining: '+str(remaining)+' Finish at '+str((datetime.datetime.now()+remaining).replace(microsecond=0))+'~~~', end="\r")

Answer 1

如我对问题的评论所示，答案来自Puciek。

解决方案是在完成后关闭进程池。我认为它会自动关闭，因为results变量是RunMany的本地变量，并且会在RunMany完成后删除。但是，python并不总是按预期工作。

固定代码是：

def RunMany(inputs):
from multiprocessing import cpu_count, Pool
proc=inputs[0]
pool=Pool(processes = proc) 
results=[]
for arg1 in inputs[1]:
    for arg2 in inputs[2]:
        for arg3 in inputs[3]:
            results.append(pool.apply_async(RunOne, args=(arg1, arg2, arg3)))
#new section
pool.close()
pool.join()    
#end new section
casenum=0
datadict=dict()
for p in results:
    #get results of simulation once it has finished
    datadict[casenum]=p.get() 
    casenum+=1
return datadict

Python无法使用multiprocessing.pool分配内存

1 个答案: