RuntimeError：无法启动新线程

Question

我的目标是使用 python 中的 Scikit-Optimize 库来最小化 function 值，以便找到 xgboost model 的优化参数。 该过程涉及使用不同的随机参数运行 model 5,000 次。

但是，似乎循环在某个时候停止并给了我一个 RuntimeError: can't start new thread。 我正在使用 ubuntu 20.04 并且正在运行 python 3.8.5，Scikit-Optimize 版本是 0.8.1。 我在 windows 10 中运行了相同的代码，看来我没有遇到此 RuntimeError，但是，代码运行速度要慢得多。

我想我可能需要一个线程池来解决这个问题，但是在搜索了 web 之后，我没有找到实现线程池的解决方案。

以下是代码的简化版本：

#This function will be passed to Scikit-Optimize to find the optimized parameters (Params)

def find_best_xgboost_para(params):`
        
        #Defines the parameters that I want to optimize

        learning_rate,gamma,max_depth,min_child_weight,reg_alpha,reg_lambda,subsample,max_bin,num_parallel_tree,colsamp_lev,colsamp_tree,StopSteps\
        =float(params[0]),float(params[1]),int(params[2]),int(params[3]),\
        int(params[4]),int(params[5]),float(params[6]),int(params[7]),int(params[8]),float(params[9]),float(params[10]),int(params[11])
                        
        
        xgbc=XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=colsamp_lev,
               colsample_bytree=colsamp_tree, gamma=gamma, learning_rate=learning_rate, max_delta_step=0,
               max_depth=max_depth, min_child_weight=min_child_weight, missing=None, n_estimators=nTrees,
               objective='binary:logistic',random_state=101, reg_alpha=reg_alpha,
               reg_lambda=reg_lambda, scale_pos_weight=1,seed=101,
               subsample=subsample,importance_type='gain',gpu_id=GPUID,max_bin=max_bin,
               tree_method='gpu_hist',num_parallel_tree=num_parallel_tree,predictor='gpu_predictor',verbosity=0,\
               refresh_leaf=0,grow_policy='depthwise',process_type=TreeUpdateStatus,single_precision_histogram=SinglePrecision)
        
        tscv = TimeSeriesSplit(CV_nSplit)
        
        error_data=xgboost.cv(xgbc.get_xgb_params(), CVTrain, num_boost_round=CVBoostRound, nfold=None, stratified=False, folds=tscv, metrics=(), \
                   obj=None, feval=f1_eval, maximize=False, early_stopping_rounds=StopSteps, fpreproc=None, as_pandas=True, \
                   verbose_eval=True, show_stdv=True, seed=101, shuffle=shuffle_trig)
    
        eval_set = [(X_train, y_train), (X_test, y_test)]
        xgbc.fit(X_train, y_train, eval_metric=f1_eval, early_stopping_rounds=StopSteps, eval_set=eval_set,verbose=True)
        
        xgbc_predictions=xgbc.predict(X_test)
        

        error=(1-metrics.f1_score(y_test, xgbc_predictions,average='macro'))
        del xgbc
 
        return error

    #Define the range of values that Scikit-Optimize can choose from to find the optimized parameters

    lr_low, lr_high=float(XgParamDict['lr_low']), float(XgParamDict['lr_high'])
    gama_low, gama_high=float(XgParamDict['gama_low']), float(XgParamDict['gama_high'])
    depth_low, depth_high=int(XgParamDict['depth_low']), int(XgParamDict['depth_high'])
    child_weight_low, child_weight_high=int(XgParamDict['child_weight_low']), int(XgParamDict['child_weight_high'])
    alpha_low,alpha_high=int(XgParamDict['alpha_low']),int(XgParamDict['alpha_high'])
    lambda_low,lambda_high=int(XgParamDict['lambda_low']),int(XgParamDict['lambda_high'])
    subsamp_low,subsamp_high=float(XgParamDict['subsamp_low']),float(XgParamDict['subsamp_high'])
    max_bin_low,max_bin_high=int(XgParamDict['max_bin_low']),int(XgParamDict['max_bin_high'])
    num_parallel_tree_low,num_parallel_tree_high=int(XgParamDict['num_parallel_tree_low']),int(XgParamDict['num_parallel_tree_high'])
    colsamp_lev_low,colsamp_lev_high=float(XgParamDict['colsamp_lev_low']),float(XgParamDict['colsamp_lev_high'])
    colsamp_tree_low,colsamp_tree_high=float(XgParamDict['colsamp_tree_low']),float(XgParamDict['colsamp_tree_high'])
    StopSteps_low,StopSteps_high=float(XgParamDict['StopSteps_low']),float(XgParamDict['StopSteps_high'])

    #Pass the target function (find_best_xgboost_para) as well as parameter ranges to Scikit-Optimize, 'res' will be an array of values that will need to be pass to another function

    res=gbrt_minimize(find_best_xgboost_para,[(lr_low,lr_high),(gama_low, gama_high),(depth_low,depth_high),(child_weight_low,child_weight_high),\
                              (alpha_low,alpha_high),(lambda_low,lambda_high),(subsamp_low,subsamp_high),(max_bin_low,max_bin_high),\
                              (num_parallel_tree_low,num_parallel_tree_high),(colsamp_lev_low,colsamp_lev_high),(colsamp_tree_low,colsamp_tree_high),\
                              (StopSteps_low,StopSteps_high)],random_state=101,n_calls=5000,n_random_starts=1500,verbose=True,n_jobs=-1) 

以下是错误信息：

Traceback (most recent call last):

File "/home/FactorOpt.py", line 91, in <module>Opt(**FactorOptDict)

File "/home/anaconda3/lib/python3.8/site-packages/skopt/optimizer/gbrt.py", line 179, in gbrt_minimize return base_minimize(func, dimensions, base_estimator,

File "/home/anaconda3/lib/python3.8/site-packages/skopt/optimizer/base.py", line 301, in base_minimize
  next_y = func(next_x)

File "/home/anaconda3/lib/python3.8/modelling/FactorOpt.py", line 456, in xgboost_opt
res=gbrt_minimize(find_best_xgboost_para,[(lr_low,lr_high),(gama_low, gama_high),(depth_low,depth_high),(child_weight_low,child_weight_high),\

File "/home/anaconda3/lib/python3.8/site-packages/skopt/optimizer/gbrt.py", line 179, in gbrt_minimize
return base_minimize(func, dimensions, base_estimator,

File "/home/anaconda3/lib/python3.8/site-packages/skopt/optimizer/base.py", line 302, in base_minimize
result = optimizer.tell(next_x, next_y)

File "/home/anaconda3/lib/python3.8/site-packages/skopt/optimizer/optimizer.py", line 493, in tell
return self._tell(x, y, fit=fit)

File "/home/anaconda3/lib/python3.8/site-packages/skopt/optimizer/optimizer.py", line 536, in _tell
est.fit(self.space.transform(self.Xi), self.yi)

File "/home/anaconda3/lib/python3.8/site-packages/skopt/learning/gbrt.py", line 85, in fit
self.regressors_ = Parallel(n_jobs=self.n_jobs, backend='threading')(

File "/home/anaconda3/lib/python3.8/site-packages/joblib/parallel.py", line 1048, in __call__
if self.dispatch_one_batch(iterator):

File "/home/anaconda3/lib/python3.8/site-packages/joblib/parallel.py", line 866, in dispatch_one_batch
self._dispatch(tasks)

File "/home/anaconda3/lib/python3.8/site-packages/joblib/parallel.py", line 784, in _dispatch
job = self._backend.apply_async(batch, callback=cb)

File "/home/anaconda3/lib/python3.8/site-packages/joblib/_parallel_backends.py", line 252, in apply_async
return self._get_pool().apply_async(

File "/home/anaconda3/lib/python3.8/site-packages/joblib/_parallel_backends.py", line 407, in _get_pool
self._pool = ThreadPool(self._n_jobs)

File "/home/anaconda3/lib/python3.8/multiprocessing/pool.py", line 925, in __init__
Pool.__init__(self, processes, initializer, initargs)

File "/home/anaconda3/lib/python3.8/multiprocessing/pool.py", line 232, in __init__
self._worker_handler.start()

File "/home/anaconda3/lib/python3.8/threading.py", line 852, in start
_start_new_thread(self._bootstrap, ())

RuntimeError: can't start new thread