我发现的最佳解决方案（它仅适用于某些类型的问题）是使用 Python 的 BaseManager 和 SyncManager 类进行客户端/服务器设置。为此，您首先设置一个为数据提供代理类的服务器。
数据服务器.py
#!/usr/bin/python
from multiprocessing.managers import SyncManager
import numpy
# Global for storing the data to be served
gData = {}
# Proxy class to be shared with different processes
# Don't put big data in here since that will force it to be piped to the
# other process when instantiated there, instead just return a portion of
# the global data when requested.
class DataProxy(object):
def __init__(self):
pass
def getData(self, key, default=None):
global gData
return gData.get(key, None)
if __name__ == '__main__':
port = 5000
print 'Simulate loading some data'
for i in xrange(1000):
gData[i] = numpy.random.rand(1000)
# Start the server on address(host,port)
print 'Serving data. Press <ctrl>-c to stop.'
class myManager(SyncManager): pass
myManager.register('DataProxy', DataProxy)
mgr = myManager(address=('', port), authkey='DataProxy01')
server = mgr.get_server()
server.serve_forever()
运行上面一次并让它运行。下面是您用来访问数据的客户端类。
数据客户端.py
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 DataClient(object):
def __init__(self, port):
assert self._checkForProcess('DataServer.py'), 'Must have DataServer running'
class myManager(BaseManager): pass
myManager.register('DataProxy')
self.mgr = myManager(address=('localhost', port), authkey='DataProxy01')
self.mgr.connect()
self.proxy = self.mgr.DataProxy()
# Verify the server is running (not required)
@staticmethod
def _checkForProcess(name):
for proc in psutil.process_iter():
if proc.name() == name:
return True
return False
下面是使用多处理来尝试此操作的测试代码。
测试MP.py
#!/usr/bin/python
import time
import multiprocessing as mp
import numpy
from DataClient import *
# Confusing, but the "proxy" will be global to each subprocess,
# it's not shared across all processes.
gProxy = None
gMode = None
gDummy = None
def init(port, mode):
global gProxy, gMode, gDummy
gProxy = DataClient(port).proxy
gMode = mode
gDummy = numpy.random.rand(1000) # Same as the dummy in the server
#print 'Init proxy ', id(gProxy), 'in ', mp.current_process()
def worker(key):
global gProxy, gMode, gDummy
if 0 == gMode: # get from proxy
array = gProxy.getData(key)
elif 1 == gMode: # bypass retrieve to test difference
array = gDummy
else: assert 0, 'unknown mode: %s' % gMode
for i in range(1000):
x = sum(array)
return x
if __name__ == '__main__':
port = 5000
maxkey = 1000
numpts = 100
for mode in [1, 0]:
for nprocs in [16, 1]:
if 0==mode: print 'Using client/server and %d processes' % nprocs
if 1==mode: print 'Using local data and %d processes' % nprocs
keys = [numpy.random.randint(0,maxkey) for k in xrange(numpts)]
pool = mp.Pool(nprocs, initializer=init, initargs=(port,mode))
start = time.time()
ret_data = pool.map(worker, keys, chunksize=1)
print ' took %4.3f seconds' % (time.time()-start)
pool.close()
当我在我的机器上运行这个时，我得到......
Using local data and 16 processes
took 0.695 seconds
Using local data and 1 processes
took 5.849 seconds
Using client/server and 16 processes
took 0.811 seconds
Using client/server and 1 processes
took 5.956 seconds
这在您的多处理系统中是否适用取决于获取数据的频率。每次传输都会产生少量开销。如果您减少迭代次数，您可以看到这一点x=sum(array)环形。在某些时候，您花在获取数据上的时间会多于处理数据的时间。
除了多处理之外，我还喜欢这种模式，因为我只需在服务器程序中加载一次大数组数据，并且它会一直保持加载状态，直到我终止服务器。这意味着我可以针对数据运行一堆单独的脚本，并且它们执行速度很快；无需等待数据加载。
虽然这里的方法有点类似于使用数据库，但它的优点是可以处理任何类型的 python 对象，而不仅仅是简单的字符串和整数数据库表等。我发现使用数据库速度更快一些这些简单的类型，但对我来说，它往往更多地以编程方式工作，并且我的数据并不总是轻松移植到数据库。
