Python sklearn和多處理

Question

我試圖平行化使用多處理從sklearn（在這種情況下，高斯混合模型）分類器的訓練，我得到了很多更糟糕的分類比較順序運行他們。此外，每次訓練後結果都不同，就好像代碼不是線程安全的。任何人都可以解釋我發生了什麼事嗎？下面是代碼，並在年底的線程函數：

nrProc = 8 
semaphore = Semaphore(nrProc) 
m = Manager() 
models = m.list() 
modelsOut = m.list() 
processes = [] 

cnt = 0     
for event_label in data_positive:       
    models.append(mixture.GMM(**classifier_params)) 
    models.append(mixture.GMM(**classifier_params)) 

for event_label in data_positive: 
    if classifier_method == 'gmm':       
     processes.append(Process(target=trainProcess, args=(models[cnt], data_positive[event_label], semaphore, modelsOut))) 
     cnt = cnt + 1       
     processes.append(Process(target=trainProcess, args=(models[cnt], data_negative[event_label], semaphore, modelsOut))) 
     cnt = cnt + 1 
    else: 
     raise ValueError("Unknown classifier method ["+classifier_method+"]") 

for proc in processes: 
    proc.start() 

for proc in processes: 
    proc.join() 


cnt = 0     
for event_label in data_positive: 
    model_container['models'][event_label] = {} 
    model_container['models'][event_label]['positive'] = modelsOut[cnt] 
    cnt = cnt + 1 
    model_container['models'][event_label]['negative'] = modelsOut[cnt] 
    cnt = cnt + 1 

def trainProcess(model, data, semaphore, modelsOut): 
    semaphore.acquire()  
    modelsOut.append(model.fit(data)) 
    semaphore.release() 
    return 0 

Answer 1

因此，解決方案是使用克隆功能從sklearn這確實估計的深層副本。