下面的代码在GoogleColab上执行需要5.0分钟，而在我的机器上执行大约需要3.0分钟。在我测试的所有其他任务（机器学习或其他）中，colab以50-100%的优势击败了我的机器。我尝试安装不同的sklearn版本，使用gpu运行，还尝试使用n_作业值，但时间要么变慢了，要么保持不变。

from sklearn.datasets import load_breast_cancer
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.feature_selection import RFE
from sklearn.pipeline import Pipeline
from sklearn.model_selection import RandomizedSearchCV, KFold
from datetime import datetime
param_grid = [
    {'feature_selection': [RFE(estimator=GradientBoostingClassifier(random_state=0))],
     'feature_selection__n_features_to_select': [2],
     'scaling': [StandardScaler()],
     'classification': [GradientBoostingClassifier(random_state=0)],
     'classification__n_estimators': [100, 500],
     'classification__max_features': ['auto', 'log2'],
     'classification__max_depth': [2, 4],
     'classification__learning_rate': [0.01, ],
     'classification__loss': ['exponential'],
     'classification__min_samples_split': [2, 200],
     'classification__min_samples_leaf': [1, 20]},
    {'feature_selection': [RFE(estimator=LogisticRegression(random_state=0))],
     'feature_selection__n_features_to_select': [2],
     'scaling': [StandardScaler()],
     'classification': [LogisticRegression(random_state=0)],
     'classification__C': [0.1, 100, 1000],
     'classification__penalty': ['l1'],
     'classification__solver': ['liblinear']}
]
pipe = Pipeline(steps=[('scaling', StandardScaler()),
                       ('feature_selection', RFE(estimator=GradientBoostingClassifier())),
                       ('classification', GradientBoostingClassifier())])
grid_obj = RandomizedSearchCV(estimator=pipe, param_distributions=param_grid,
                              scoring='neg_brier_score', cv=KFold(shuffle=True), random_state=0,
                              return_train_score=True, n_jobs=-1, verbose=10)
X, y = load_breast_cancer(return_X_y=True)
grid_obj.fit(X, y)

google colab结果：

# [Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.
# [Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:   13.3s
# [Parallel(n_jobs=-1)]: Done   4 tasks      | elapsed:   25.8s
# [Parallel(n_jobs=-1)]: Done   9 tasks      | elapsed:  1.0min
# [Parallel(n_jobs=-1)]: Done  14 tasks      | elapsed:  1.4min
# [Parallel(n_jobs=-1)]: Done  21 tasks      | elapsed:  2.2min
# [Parallel(n_jobs=-1)]: Done  28 tasks      | elapsed:  2.8min
# [Parallel(n_jobs=-1)]: Done  37 tasks      | elapsed:  3.8min
# [Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:  4.6min
# [Parallel(n_jobs=-1)]: Done  50 out of  50 | elapsed:  5.0min finished