我正在编写一个多处理程序，以使用Windows并行处理大型.CSV文件。

我发现了一个类似问题的出色例子。在Windows下运行它时，我收到一个错误，提示csv.reader不能Picklable。

我想我可以在阅读器子流程中打开CSV文件，然后从父流程中将文件名发送给它。但是，我想传递一个已经打开的CSV文件（如应该执行该代码），并带有特定状态，即真正使用共享对象。

知道如何在Windows下执行此操作或那里缺少什么吗？

这是代码（为了方便阅读，我正在转贴）：

"""A program that reads integer values from a CSV file and writes out their
sums to another CSV file, using multiple processes if desired.
"""

import csv
import multiprocessing
import optparse
import sys

NUM_PROCS = multiprocessing.cpu_count()

def make_cli_parser():
    """Make the command line interface parser."""
    usage = "\n\n".join(["python %prog INPUT_CSV OUTPUT_CSV",
            __doc__,
            """
ARGUMENTS:
    INPUT_CSV: an input CSV file with rows of numbers
    OUTPUT_CSV: an output file that will contain the sums\
"""])
    cli_parser = optparse.OptionParser(usage)
    cli_parser.add_option('-n', '--numprocs', type='int',
            default=NUM_PROCS,
            help="Number of processes to launch [DEFAULT: %default]")
    return cli_parser

class CSVWorker(object):
    def __init__(self, numprocs, infile, outfile):
        self.numprocs = numprocs
        self.infile = open(infile)
        self.outfile = outfile
        self.in_csvfile = csv.reader(self.infile)
        self.inq = multiprocessing.Queue()
        self.outq = multiprocessing.Queue()

        self.pin = multiprocessing.Process(target=self.parse_input_csv, args=())
        self.pout = multiprocessing.Process(target=self.write_output_csv, args=())
        self.ps = [ multiprocessing.Process(target=self.sum_row, args=())
                        for i in range(self.numprocs)]

        self.pin.start()
        self.pout.start()
        for p in self.ps:
            p.start()

        self.pin.join()
        i = 0
        for p in self.ps:
            p.join()
            print "Done", i
            i += 1

        self.pout.join()
        self.infile.close()

    def parse_input_csv(self):
            """Parses the input CSV and yields tuples with the index of the row
            as the first element, and the integers of the row as the second
            element.

            The index is zero-index based.

            The data is then sent over inqueue for the workers to do their
            thing.  At the end the input thread sends a 'STOP' message for each
            worker.
            """
            for i, row in enumerate(self.in_csvfile):
                row = [ int(entry) for entry in row ]
                self.inq.put( (i, row) )

            for i in range(self.numprocs):
                self.inq.put("STOP")

    def sum_row(self):
        """
        Workers. Consume inq and produce answers on outq
        """
        tot = 0
        for i, row in iter(self.inq.get, "STOP"):
                self.outq.put( (i, sum(row)) )
        self.outq.put("STOP")

    def write_output_csv(self):
        """
        Open outgoing csv file then start reading outq for answers
        Since I chose to make sure output was synchronized to the input there
        is some extra goodies to do that.

        Obviously your input has the original row number so this is not
        required.
        """
        cur = 0
        stop = 0
        buffer = {}
        # For some reason csv.writer works badly across threads so open/close
        # and use it all in the same thread or else you'll have the last
        # several rows missing
        outfile = open(self.outfile, "w")
        self.out_csvfile = csv.writer(outfile)

        #Keep running until we see numprocs STOP messages
        for works in range(self.numprocs):
            for i, val in iter(self.outq.get, "STOP"):
                # verify rows are in order, if not save in buffer
                if i != cur:
                    buffer[i] = val
                else:
                    #if yes are write it out and make sure no waiting rows exist
                    self.out_csvfile.writerow( [i, val] )
                    cur += 1
                    while cur in buffer:
                        self.out_csvfile.writerow([ cur, buffer[cur] ])
                        del buffer[cur]
                        cur += 1

        outfile.close()

def main(argv):
    cli_parser = make_cli_parser()
    opts, args = cli_parser.parse_args(argv)
    if len(args) != 2:
        cli_parser.error("Please provide an input file and output file.")

    c = CSVWorker(opts.numprocs, args[0], args[1])

if __name__ == '__main__':
    main(sys.argv[1:])

在Windows下运行时，这是我收到的错误：

Traceback (most recent call last):
  File "C:\Users\ron.berman\Documents\Attribution\ubrShapley\test.py", line 130, in <module>
    main(sys.argv[1:])
  File "C:\Users\ron.berman\Documents\Attribution\ubrShapley\test.py", line 127, in main
    c = CSVWorker(opts.numprocs, args[0], args[1])
  File "C:\Users\ron.berman\Documents\Attribution\ubrShapley\test.py", line 44, in __init__
    self.pin.start()
  File "C:\Python27\lib\multiprocessing\process.py", line 130, in start
    self._popen = Popen(self)
  File "C:\Python27\lib\multiprocessing\forking.py", line 271, in __init__
    dump(process_obj, to_child, HIGHEST_PROTOCOL)
  File "C:\Python27\lib\multiprocessing\forking.py", line 193, in dump
    ForkingPickler(file, protocol).dump(obj)
  File "C:\Python27\lib\pickle.py", line 224, in dump
    self.save(obj)
  File "C:\Python27\lib\pickle.py", line 331, in save
    self.save_reduce(obj=obj, *rv)
  File "C:\Python27\lib\pickle.py", line 419, in save_reduce
    save(state)
  File "C:\Python27\lib\pickle.py", line 286, in save
    f(self, obj) # Call unbound method with explicit self
  File "C:\Python27\lib\pickle.py", line 649, in save_dict
    self._batch_setitems(obj.iteritems())
  File "C:\Python27\lib\pickle.py", line 681, in _batch_setitems
    save(v)
  File "C:\Python27\lib\pickle.py", line 286, in save
    f(self, obj) # Call unbound method with explicit self
  File "C:\Python27\lib\multiprocessing\forking.py", line 66, in dispatcher
    self.save_reduce(obj=obj, *rv)
  File "C:\Python27\lib\pickle.py", line 401, in save_reduce
    save(args)
  File "C:\Python27\lib\pickle.py", line 286, in save
    f(self, obj) # Call unbound method with explicit self
  File "C:\Python27\lib\pickle.py", line 548, in save_tuple
    save(element)
  File "C:\Python27\lib\pickle.py", line 331, in save
    self.save_reduce(obj=obj, *rv)
  File "C:\Python27\lib\pickle.py", line 419, in save_reduce
    save(state)
  File "C:\Python27\lib\pickle.py", line 286, in save
    f(self, obj) # Call unbound method with explicit self
  File "C:\Python27\lib\pickle.py", line 649, in save_dict
    self._batch_setitems(obj.iteritems())
  File "C:\Python27\lib\pickle.py", line 681, in _batch_setitems
    save(v)
  File "C:\Python27\lib\pickle.py", line 331, in save
    self.save_reduce(obj=obj, *rv)
  File "C:\Python27\lib\pickle.py", line 396, in save_reduce
    save(cls)
  File "C:\Python27\lib\pickle.py", line 286, in save
    f(self, obj) # Call unbound method with explicit self
  File "C:\Python27\lib\pickle.py", line 753, in save_global
    (obj, module, name))
pickle.PicklingError: Can't pickle <type '_csv.reader'>: it's not the same object as _csv.reader
Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "C:\Python27\lib\multiprocessing\forking.py", line 374, in main
    self = load(from_parent)
  File "C:\Python27\lib\pickle.py", line 1378, in load
    return Unpickler(file).load()
  File "C:\Python27\lib\pickle.py", line 858, in load
    dispatch[key](self)
  File "C:\Python27\lib\pickle.py", line 880, in load_eof
    raise EOFError
EOFError