多線程在Python與紙漿

我有接受列表R.在這個函數的功能，我一直在使用「漿」定義的優化問題，這是我的函數：多線程在Python與紙漿

import pulp 
from multiprocessing.dummy import Pool as ThreadPool 

def optimize(R): 

    variables = ["x1","x2","x3","x4"] 

    costs = {"x1":R[0], "x2":R[1], "x3":R[2], "x4":R[3]} 

    constraint = {"x1":5, "x2":7, "x3":4, "x4":3} 

    prob_variables = pulp.LpVariable.dicts("Intg",variables, 
           lowBound=0, 
           upBound=1, 
           cat=pulp.LpInteger) 


    prob = pulp.LpProblem("test1", pulp.LpMaximize) 

    # defines the constraints 
    prob += pulp.lpSum([constraint[i]*prob_variables[i] for i in variables]) <= 14 

    # defines the objective function to maximize 
    prob += pulp.lpSum([costs[i]*prob_variables[i] for i in variables]) 

    pulp.GLPK().solve(prob) 

    # Solution 
    return pulp.value(prob.objective)

要獲得輸出，我用一個列表作爲我的輸入和輸出是正確的：

my_input = [[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]] 

results =[] 
for i in range(0,len(my_input)): 
    results.append(optimize(my_input[i])) 

    print("*"*20) 
    print(results)

不過，我想使用，而不是多線程的for循環。所以，我用：

my_input = [[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]] 
pool = ThreadPool(4) 
results = pool.map(optimize, my_input)

但它給了我一些錯誤：

Traceback (most recent call last): 
    File "/Users/Mohammad/PycharmProjects/untitled10/multi_thread.py", line 35, in <module> 
    results = pool.map(optimize, my_input) 
    File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/multiprocessing/pool.py", line 260, in map 
    return self._map_async(func, iterable, mapstar, chunksize).get() 
    File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/multiprocessing/pool.py", line 608, in get 
    raise self._value 
    File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/multiprocessing/pool.py", line 119, in worker 
    result = (True, func(*args, **kwds)) 
    File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/multiprocessing/pool.py", line 44, in mapstar 
    return list(map(*args)) 
    File "/Users/Mohammad/PycharmProjects/untitled10/multi_thread.py", line 27, in optimize 
    pulp.GLPK().solve(prob) 
    File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/PuLP-1.6.1-py3.5.egg/pulp/solvers.py", line 179, in solve 
    return lp.solve(self) 
    File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/PuLP-1.6.1-py3.5.egg/pulp/pulp.py", line 1643, in solve 
    status = solver.actualSolve(self, **kwargs) 
    File "/Library/Frameworks/Python.framework/Versions/3.5/lib/python3.5/site-packages/PuLP-1.6.1-py3.5.egg/pulp/solvers.py", line 377, in actualSolve 
    raise PulpSolverError("PuLP: Error while executing "+self.path) 
pulp.solvers.PulpSolverError: PuLP: Error while executing glpsol

任何人可以幫助我嗎？在我的實際代碼中，my_input列表的長度爲27（而不是上面的代碼中的4），對於每個列表，在我的函數中，我必須執行80k優化（而不是上面的代碼中的一個）。所以，多線程對我來說是一個很大的幫助。

來源

2016-07-06 m0_as

「但它給了我一些錯誤。」請*添加*錯誤信息。始終幫助人們幫助你。 – MisterMiyagi

@MisterMiyagi我加了錯誤。謝謝。 –

你確定它與線程完全相關嗎？檢查線程，如http://stackoverflow.com/questions/32688324/pulpsolvererror-pulp-error-while-trying-to-execute-glpsol-in-python-2-7試圖事先調試錯誤消息。 – Tttt1228

我已經看到類pulp.solvers.COIN_CMD有一個threads的說法，雖然文檔是相當簡潔。看看代碼源，它似乎確實是向求解器提供線程的一種方式。

如果命名確實是問題，請考慮將給定問題的所需名稱索引作爲該函數的輸入參數添加。喜歡的東西：

def optimize(tup): # here, tup contains (idx, R), so as to be callable using pool.map 
    ... 
    prob = pulp.LpProblem('test'+str(idx), pulp.LpMaximize) 
    ...

，然後是這樣的：

my_input = [[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]] 
pool = ThreadPool(4) 
results = pool.map(optimize, enumerate(my_input))

來源

2016-07-06 19:50:45 Tttt1228

多線程在Python與紙漿

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