Python多处理 - 调试OSError:[Errno 12]无法分配内存

Question

我面临以下问题.我正在尝试并行化一个更新文件的函数,但我无法启动Pool()因为一个OSError: [Errno 12] Cannot allocate memory.我开始在服务器上四处看看,这并不像我使用旧的,弱的/实际内存.见htop: 此外,free -m显示除了大约7GB的交换内存外,我还有足够的RAM: 而我正在尝试使用的文件也不是那么大.我将粘贴我的代码(和堆栈跟踪),其中,大小如下:

使用的predictionmatrix数据框占用大约.根据pandasdataframe.memory_usage() 文件geo.geojson为80MB 是2MB

我该如何调试呢？我可以检查什么以及如何检查？感谢您的任何提示/技巧!

码:

def parallelUpdateJSON(paramMatch, predictionmatrix, data):
    for feature in data['features']: 
        currentfeature = predictionmatrix[(predictionmatrix['SId']==feature['properties']['cellId']) & paramMatch]
        if (len(currentfeature) > 0):
            feature['properties'].update({"style": {"opacity": currentfeature.AllActivity.item()}})
        else:
            feature['properties'].update({"style": {"opacity": 0}})

def writeGeoJSON(weekdaytopredict, hourtopredict, predictionmatrix):
    with open('geo.geojson') as f:
        data = json.load(f)
    paramMatch = (predictionmatrix['Hour']==hourtopredict) & (predictionmatrix['Weekday']==weekdaytopredict)
    pool = Pool()
    func = partial(parallelUpdateJSON, paramMatch, predictionmatrix)
    pool.map(func, data)
    pool.close()
    pool.join()

    with open('output.geojson', 'w') as outfile:
        json.dump(data, outfile)

堆栈跟踪:

---------------------------------------------------------------------------
OSError                                   Traceback (most recent call last)
<ipython-input-428-d6121ed2750b> in <module>()
----> 1 writeGeoJSON(6, 15, baseline)

<ipython-input-427-973b7a5a8acc> in writeGeoJSON(weekdaytopredict, hourtopredict, predictionmatrix)
     14     print("Start loop")
     15     paramMatch = (predictionmatrix['Hour']==hourtopredict) & (predictionmatrix['Weekday']==weekdaytopredict)
---> 16     pool = Pool(2)
     17     func = partial(parallelUpdateJSON, paramMatch, predictionmatrix)
     18     print(predictionmatrix.memory_usage())

/usr/lib/python3.5/multiprocessing/context.py in Pool(self, processes, initializer, initargs, maxtasksperchild)
    116         from .pool import Pool
    117         return Pool(processes, initializer, initargs, maxtasksperchild,
--> 118                     context=self.get_context())
    119 
    120     def RawValue(self, typecode_or_type, *args):

/usr/lib/python3.5/multiprocessing/pool.py in __init__(self, processes, initializer, initargs, maxtasksperchild, context)
    166         self._processes = processes
    167         self._pool = []
--> 168         self._repopulate_pool()
    169 
    170         self._worker_handler = threading.Thread(

/usr/lib/python3.5/multiprocessing/pool.py in _repopulate_pool(self)
    231             w.name = w.name.replace('Process', 'PoolWorker')
    232             w.daemon = True
--> 233             w.start()
    234             util.debug('added worker')
    235 

/usr/lib/python3.5/multiprocessing/process.py in start(self)
    103                'daemonic processes are not allowed to have children'
    104         _cleanup()
--> 105         self._popen = self._Popen(self)
    106         self._sentinel = self._popen.sentinel
    107         _children.add(self)

/usr/lib/python3.5/multiprocessing/context.py in _Popen(process_obj)
    265         def _Popen(process_obj):
    266             from .popen_fork import Popen
--> 267             return Popen(process_obj)
    268 
    269     class SpawnProcess(process.BaseProcess):

/usr/lib/python3.5/multiprocessing/popen_fork.py in __init__(self, process_obj)
     18         sys.stderr.flush()
     19         self.returncode = None
---> 20         self._launch(process_obj)
     21 
     22     def duplicate_for_child(self, fd):

/usr/lib/python3.5/multiprocessing/popen_fork.py in _launch(self, process_obj)
     65         code = 1
     66         parent_r, child_w = os.pipe()
---> 67         self.pid = os.fork()
     68         if self.pid == 0:
     69             try:

OSError: [Errno 12] Cannot allocate memory

UPDATE

根据@ robyschek的解决方案,我已将我的代码更新为:

global g_predictionmatrix 

def worker_init(predictionmatrix):
    global g_predictionmatrix
    g_predictionmatrix = predictionmatrix    

def parallelUpdateJSON(paramMatch, data_item):
    for feature in data_item['features']: 
        currentfeature = predictionmatrix[(predictionmatrix['SId']==feature['properties']['cellId']) & paramMatch]
        if (len(currentfeature) > 0):
            feature['properties'].update({"style": {"opacity": currentfeature.AllActivity.item()}})
        else:
            feature['properties'].update({"style": {"opacity": 0}})

def use_the_pool(data, paramMatch, predictionmatrix):
    pool = Pool(initializer=worker_init, initargs=(predictionmatrix,))
    func = partial(parallelUpdateJSON, paramMatch)
    pool.map(func, data)
    pool.close()
    pool.join()


def writeGeoJSON(weekdaytopredict, hourtopredict, predictionmatrix):
    with open('geo.geojson') as f:
        data = json.load(f)
    paramMatch = (predictionmatrix['Hour']==hourtopredict) & (predictionmatrix['Weekday']==weekdaytopredict)
    use_the_pool(data, paramMatch, predictionmatrix)     
    with open('trentino-grid.geojson', 'w') as outfile:
        json.dump(data, outfile)

我仍然得到同样的错误.另外,根据文档,map()应该将我data分成块,所以我认为它不应该复制我的80MB rownum时间.我可能错了...... :)另外我注意到如果我使用较小的输入(~11MB而不是80MB)我没有得到错误.所以我想我正在尝试使用太多的内存,但我无法想象它是如何从80MB到16GB的RAM无法处理的.

Answer 1

使用a时multiprocessing.Pool,启动进程的默认方式是fork.问题fork在于整个过程是重复的.(详见此处).因此,如果您的主进程已经使用了大量内存,则此内存将被复制,从而达到此目的MemoryError.例如,如果您的主进程使用2GB内存并且您使用了8个子进程,则需要18GB在RAM中.

您应该尝试使用不同的启动方法,例如:'forkserver'或'spawn':

from multiprocessing import set_start_method, Pool
set_start_method('forkserver')

# You can then start your Pool without each process
# cloning your entire memory
pool = Pool()
func = partial(parallelUpdateJSON, paramMatch, predictionmatrix)
pool.map(func, data)

这些方法避免重复您的工作空间,Process但启动时可能会慢一些,因为您需要重新加载正在使用的模块.

Answer 2

我们有几次。根据我的系统管理员的说法，unix中有一个“错误”，如果内存不足，进程达到最大文件描述符限制，它将引发相同的错误。

我们有一个文件描述符泄漏，错误提示是[Errno 12]无法分配内存＃012OSError。

因此，您应该查看脚本并仔细检查问题是否不是创建过多的FD。