在不同进程之间共享内存中的复杂python对象

Question

null

Answer 1

对于复杂的对象，没有现成的方法在进程之间直接共享内存。如果您有简单的ctypes，您可以在C样式的共享内存中执行此操作，但它不会直接映射到python对象。

如果您在任何时候只需要一部分数据，而不是整个36GB数据，那么有一个简单的解决方案可以很好地工作。为此，可以使用multiprocessing.managers中的syncmanager。使用该方法，您可以设置一个服务器，为您的数据提供代理类（您的数据不存储在类中，代理只提供对它的访问）。然后，客户机使用baseManager连接到服务器，并调用代理类中的方法来检索数据。

在幕后，Manager类负责处理您所要求的数据，并通过开放端口将其从服务器发送到客户机。因为每次调用都要处理数据，如果需要整个数据集，这样做效率就不高了。在客户机中只需要一小部分数据的情况下，该方法节省了大量时间，因为服务器只需要加载一次数据。

服务器

#!/usr/bin/python
import  sys
from    multiprocessing.managers import SyncManager
import  numpy

# Global for storing the data to be served
gVectors = {}

# Proxy class to be shared with different processes
# Don't but the big vector data in here since that will force it to 
# be piped to the other process when instantiated there, instead just
# return the global vector data, from this process, when requested.
class GloVeProxy(object):
    def __init__(self):
        pass

    def getNVectors(self):
        global gVectors
        return len(gVectors)

    def getEmpty(self):
        global gVectors
        return numpy.zeros_like(gVectors.values()[0])

    def getVector(self, word, default=None):
        global gVectors
        return gVectors.get(word, default)

# Class to encapsulate the server functionality
class GloVeServer(object):
    def __init__(self, port, fname):
        self.port = port
        self.load(fname)

    # Load the vectors into gVectors (global)
    @staticmethod
    def load(filename):
        global gVectors
        f = open(filename, 'r')
        for line in f:
            vals = line.rstrip().split(' ')
            gVectors[vals[0]] = numpy.array(vals[1:]).astype('float32')

    # Run the server
    def run(self):
        class myManager(SyncManager): pass  
        myManager.register('GloVeProxy', GloVeProxy)
        mgr = myManager(address=('', self.port), authkey='GloVeProxy01')
        server = mgr.get_server()
        server.serve_forever()

if __name__ == '__main__':
    port  = 5010
    fname = '/mnt/raid/Data/Misc/GloVe/WikiGiga/glove.6B.50d.txt'

    print 'Loading vector data'
    gs = GloVeServer(port, fname)

    print 'Serving data. Press <ctrl>-c to stop.'
    gs.run()

客户

from   multiprocessing.managers import BaseManager
import psutil   #3rd party module for process info (not strictly required)

# Grab the shared proxy class.  All methods in that class will be availble here
class GloVeClient(object):
    def __init__(self, port):
        assert self._checkForProcess('GloVeServer.py'), 'Must have GloVeServer running'
        class myManager(BaseManager): pass
        myManager.register('GloVeProxy')
        self.mgr = myManager(address=('localhost', port), authkey='GloVeProxy01')
        self.mgr.connect()
        self.glove = self.mgr.GloVeProxy()

    # Return the instance of the proxy class
    @staticmethod
    def getGloVe(port):
        return GloVeClient(port).glove

    # Verify the server is running
    @staticmethod
    def _checkForProcess(name):
        for proc in psutil.process_iter():
            if proc.name() == name:
                return True
        return False

if __name__ == '__main__':
    port = 5010
    glove = GloVeClient.getGloVe(port)

    for word in ['test', 'cat', '123456']:
        print('%s = %s' % (word, glove.getVector(word)))

请注意，psutil库只是用来检查服务器是否运行，而不是必需的。确保将服务器命名为gloveserver.py或更改html" target="_blank">代码中的检查bypsutil，以便查找正确的名称。