在Linux下py3k多处理中，为什么我的线程数比我要求的池多？

Question

我正在尝试并行化一些工作，这些工作在我的Mac上运行（在Mac OS 10.7下为Pyton 3.2.2），但是在Linux群集上却出现以下错误，我在运行4个内核并访问Python 3.2的Linux群集上运行该错误。 错误消息将继续，直到我手动中断执行为止。

Exception in thread Thread-2:
Traceback (most recent call last):
  File "/n/sw/python-3.2/lib/python3.2/threading.py", line 736, in _bootstrap_inner
    self.run()
  File "/n/sw/python-3.2/lib/python3.2/threading.py", line 689, in run
    self._target(*self._args, **self._kwargs)
  File "/n/sw/python-3.2/lib/python3.2/multiprocessing/pool.py", line 338, in _handle_tasks
    put(task)
_pickle.PicklingError: Can't pickle <class 'function'>: attribute lookup builtins.function failed

Process PoolWorker-2:
Process PoolWorker-4:
Traceback (most recent call last):
Traceback (most recent call last):
  File "/n/sw/python-3.2/lib/python3.2/multiprocessing/process.py", line 259, in _bootstrap
  File "/n/sw/python-3.2/lib/python3.2/multiprocessing/process.py", line 259, in _bootstrap
Process PoolWorker-1:
Traceback (most recent call last):
  File "/n/sw/python-3.2/lib/python3.2/multiprocessing/process.py", line 259, in _bootstrap
Process PoolWorker-12:
Traceback (most recent call last):
  File "/n/sw/python-3.2/lib/python3.2/multiprocessing/process.py", line 259, in _bootstrap
    self.run()
  File "/n/sw/python-3.2/lib/python3.2/multiprocessing/process.py", line 114, in run
    self._target(*self._args, **self._kwargs)
  File "/n/sw/python-3.2/lib/python3.2/multiprocessing/pool.py", line 102, in worker
Process PoolWorker-11:
Traceback (most recent call last):
  File "/n/sw/python-3.2/lib/python3.2/multiprocessing/process.py", line 259, in _bootstrap
    self.run()
  File "/n/sw/python-3.2/lib/python3.2/multiprocessing/process.py", line 114, in run
    self._target(*self._args, **self._kwargs)
  File "/n/sw/python-3.2/lib/python3.2/multiprocessing/pool.py", line 102, in worker

当然，作为参考，这是我的代码的一部分。 我看不到这种multiprocessing.Pool调用应如何导致这些错误，尤其是。 数字大于4个进程的PoolWorkers。 感谢您的任何想法！

import csv
import networkx as nx
import time
import shutil
import datetime
import pydot
import os
import re
import logging
from operator import itemgetter
import numpy as np
from multiprocessing import Pool
import itertools

# Dictionary for edge attributes in projected graph:
# 0: overlap_length
# 1: overlap_start
# 2: overlap_end
# 3: cell
# 4: level

def chunks(l,n):
    """Divide a list of nodes `l` in `n` chunks"""
    l_c = iter(l)
    while 1:
        x = tuple(itertools.islice(l_c,n))
        if not x:
            return
        yield x

def overlaps(G,B,u,nbrs2):
    l = []
    for v in nbrs2:
        for mutual_cell in set(B[u]) & set(B[v]):
            for uspell in B.get_edge_data(u,mutual_cell).values():
                ustart = uspell[1]
                uend = uspell[2]
                for vspell in B.get_edge_data(v,mutual_cell).values():
                    vstart = vspell[1]
                    vend = vspell[2]
                    if uend > vstart and vend > ustart:
                        ostart = max(ustart,vstart)
                        oend = min(uend,vend)
                        olen = (oend-ostart+1)/86400
                        ocell = mutual_cell
                        if (v not in G[u] or ostart not in [ edict[1] for edict in G[u][v].values() ]):
                            l.append((u,v,{0: olen,1: ostart,2: oend,3: ocell}))
    return l

def _pmap1(arg_tuple):
    """Pool for multiprocess only accepts functions with one argument. This function
    uses a tuple as its only argument.
    """
    return overlaps(arg_tuple[0],arg_tuple[1],arg_tuple[2],arg_tuple[3])

def time_overlap_projected_graph_parallel(B, nodes):
    G=nx.MultiGraph()
    G.add_nodes_from((n,B.node[n]) for n in nodes)
    add_edges_from = nx.MultiGraph.add_edges_from
    get_edge_data = nx.MultiGraph.get_edge_data
    p = Pool(processes=4)
    node_divisor = len(p._pool)
    for u in nodes:
        unbrs = set(B[u])
        nbrs2 = set((n for nbr in unbrs for n in B[nbr])) - set([u])
        # iterate over subsets of neighbors - parallelize
        node_chunks = list(chunks(nbrs2,int(len(nbrs2)/int(node_divisor))))
        num_chunks = len(node_chunks)
        pedgelists = p.map(_pmap1,
                           zip([G]*num_chunks,
                               [B]*num_chunks,
                               [u]*num_chunks,
                               node_chunks))
        ll = []
        for l in pedgelists:
            ll.extend(l)
        G.add_edges_from(ll)
        # compile long list
           # add edges from long list in a single step
    return G

Answer 1

好的，我只是在不经意间尝试cProfile在集群上并行运行，而我只是让测试离线运行。 该代码运行良好，但性能分析失败-并行脚本通常应如此。 它与群集或LSF不相关。 抱歉。