Why do I get an error when I run BayesianOptimization with XGBoost with large dataframe?

Everything works well when I run BayesianOptimization with XGBoost on a small dataframe but when I use a large dataframe I get a variety of errors. The most recent time that I ran it I received

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
~\anaconda3\envs\tf-gpu\lib\site-packages\bayes_opt\target_space.py in probe(self, params)
    190         try:
--> 191             target = self._cache[_hashable(x)]
    192         except KeyError:

KeyError: (0.5414533613708468, 0.4244312334264955, 2.505215712179838, 3.29866527650986, 6937.176867016293, 0.833081120152225)

During handling of the above exception, another exception occurred:

XGBoostError                              Traceback (most recent call last)
<ipython-input-2-ef2efb90fddb> in <module>
     53 # Use the expected improvement acquisition function to handle negative numbers
     54 # Optimally needs quite a few more initiation points and number of iterations
---> 55 xgb_bo.maximize(init_points=10, n_iter=100, acq='ei')
     56 print(xgb_bo.max)

~\anaconda3\envs\tf-gpu\lib\site-packages\bayes_opt\bayesian_optimization.py in maximize(self, init_points, n_iter, acq, kappa, xi, **gp_params)
    172                 iteration += 1
    173 
--> 174             self.probe(x_probe, lazy=False)
    175 
    176         self.dispatch(Events.OPTIMIZATION_END)

~\anaconda3\envs\tf-gpu\lib\site-packages\bayes_opt\bayesian_optimization.py in probe(self, params, lazy)
    110             self._queue.add(params)
    111         else:
--> 112             self._space.probe(params)
    113             self.dispatch(Events.OPTIMIZATION_STEP)
    114 

~\anaconda3\envs\tf-gpu\lib\site-packages\bayes_opt\target_space.py in probe(self, params)
    192         except KeyError:
    193             params = dict(zip(self._keys, x))
--> 194             target = self.target_func(**params)
    195             self.register(x, target)
    196         return target

<ipython-input-2-ef2efb90fddb> in xgb_evaluate(max_depth, gamma, colsample_bytree, subsample, eta, min_child_weight)
     38              }
     39     # Used around 1000 boosting rounds in the full model
---> 40     cv_result = xgb.cv(params, dtrain, num_boost_round=100, nfold=3)
     41 
     42 

~\anaconda3\envs\tf-gpu\lib\site-packages\xgboost\training.py in cv(params, dtrain, num_boost_round, nfold, stratified, folds, metrics, obj, feval, maximize, early_stopping_rounds, fpreproc, as_pandas, verbose_eval, show_stdv, seed, callbacks, shuffle)
    496                            evaluation_result_list=None))
    497         for fold in cvfolds:
--> 498             fold.update(i, obj)
    499         res = aggcv([f.eval(i, feval) for f in cvfolds])
    500 

~\anaconda3\envs\tf-gpu\lib\site-packages\xgboost\training.py in update(self, iteration, fobj)
    224     def update(self, iteration, fobj):
    225         """"Update the boosters for one iteration"""
--> 226         self.bst.update(self.dtrain, iteration, fobj)
    227 
    228     def eval(self, iteration, feval):

~\anaconda3\envs\tf-gpu\lib\site-packages\xgboost\core.py in update(self, dtrain, iteration, fobj)
   1367             _check_call(_LIB.XGBoosterUpdateOneIter(self.handle,
   1368                                                     ctypes.c_int(iteration),
-> 1369                                                     dtrain.handle))
   1370         else:
   1371             pred = self.predict(dtrain, output_margin=True, training=True)

~\anaconda3\envs\tf-gpu\lib\site-packages\xgboost\core.py in _check_call(ret)
    188     """
    189     if ret != 0:
--> 190         raise XGBoostError(py_str(_LIB.XGBGetLastError()))
    191 
    192 
    >
    >XGBoostError: [07:47:25] C:/Users/Administrator/workspace/xgboost-win64_release_1.1.0/src/tree/updater_gpu_hist.cu:952: Exception in gpu_hist: bad allocation: temporary_buffer::allocate: get_temporary_buffer failed

Before that, I've received 

>OSError: [WinError -529697949] Windows Error 0xe06d7363

Here's the code that I am running.

        
    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    
    number_of_rows = 10000000
    
    print('reading data')
    train_file = "D:\\reading_data/training_data.csv"
    df = pd.read_csv(train_file, sep=',', nrows=number_of_rows)
    df = df.replace([np.inf, -np.inf], np.nan).dropna(axis=0)
    print('done reading data')
    
    import xgboost as xgb
    from bayes_opt import BayesianOptimization
    from sklearn.metrics import mean_squared_error
    
    from sklearn.model_selection import train_test_split
    
    X_train, X_test, y_train, y_test = train_test_split(df.drop(['Symbol', 'new_date', 'old_dep_var'], axis=1),
                                                        df['old_dep_var'], test_size=0.25)
    del(df)
    dtrain = xgb.DMatrix(X_train, label=y_train)
    del(X_train)
    dtest = xgb.DMatrix(X_test)
    del(X_test)
    
    def xgb_evaluate(max_depth, gamma, colsample_bytree, subsample, eta, min_child_weight):
        params = {'objective': 'reg:squarederror',
                  'eval_metric': 'rmse',
                  'max_depth': int(max_depth),
                  'subsample': 0.8,
                  'eta': 0.1,
                  'gamma': gamma,
                  'colsample_bytree': colsample_bytree,
                  'tree_method':'gpu_hist',
                  'min_child_weight': 100
                 }
        cv_result = xgb.cv(params, dtrain, num_boost_round=100, nfold=3)
        
        
        return -1.0 * cv_result['test-rmse-mean'].iloc[-1]
    
    xgb_bo = BayesianOptimization(xgb_evaluate, {'max_depth': (2, 10), 
                                                 'gamma': (0, 10),
                                                 'colsample_bytree': (0.1, 0.9),
                                                 'subsample': (0.2, 0.95),
                                                 'eta': (0.05, 0.5),
                                                 'min_child_weight': (10, 10000)
                                                })
    xgb_bo.maximize(init_points=10, n_iter=100, acq='ei')
    print(xgb_bo.max)

I've tried: 1) reinstalling bayesian-optimization, 2) creating/using a new conda environment, 3) installing and running Outbyte PC Repair to update all drivers, fix all corrupt system files, and eliminate malware.

I have no idea what else to try. Any help would be appreciated.