增加n_jobs对GridSearchCV没有影响

我设置了一个简单的实验，以在使用KNeighborsClassifier运行sklearn GridSearchCV时检查多核CPU的重要性.我得到的结果令我感到惊讶，我想知道我是否误解了多核的好处，或者我做得不好.

I have setup simple experiment to check importance of the multi core CPU while running sklearn GridSearchCV with KNeighborsClassifier. The results I got are surprising to me and I wonder if I misunderstood the benefits of multi cores or maybe I haven't done it right.

2-8个工作之间的完成时间没有差异.怎么会 ?我已经注意到"CPU性能"选项卡上的差异.在第一个单元运行时，CPU使用率约为13％，而最后一个单元则逐渐增加到100％.我期望它能更快完成.也许不是线性地更快，也就是8个工作将比4个工作快2倍，但要快一点.

There is no difference in time to completion between 2-8 jobs. How come ? I have noticed the difference on a CPU Performance tab. While the first cell was running CPU usage was ~13% and it was gradually increasing to 100% for the last cell. I was expecting it to finish faster. Maybe not linearly faster aka 8 jobs would be 2 times faster then 4 jobs but a bit faster.

这是我的设置方式:

我正在使用jupyter-notebook，单元格是指jupyter-notebook单元格.

I am using jupyter-notebook, cell refers to jupyter-notebook cell.

我已经加载了MNIST，并对X_play中的3000位数字使用了0.05测试大小.

I have loaded MNIST and used 0.05 test size for 3000 digits in a X_play.

from sklearn.datasets import fetch_mldata
from sklearn.model_selection import train_test_split

mnist = fetch_mldata('MNIST original')

X, y = mnist["data"], mnist['target']

X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]
_, X_play, _, y_play = train_test_split(X_train, y_train, test_size=0.05, random_state=42, stratify=y_train, shuffle=True)

在下一个单元格中，我设置了KNN和一个GridSearchCV

In the next cell I have setup KNN and a GridSearchCV

from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import GridSearchCV

knn_clf = KNeighborsClassifier()
param_grid = [{'weights': ["uniform", "distance"], 'n_neighbors': [3, 4, 5]}]

然后，我为8个n_jobs值完成了8个单元格.我的CPU是4核8线程的i7-4770.

Then I done 8 cells for 8 n_jobs values. My CPU is i7-4770 with 4 cores 8 threads.

grid_search = GridSearchCV(knn_clf, param_grid, cv=3, verbose=3, n_jobs=N_JOB_1_TO_8)
grid_search.fit(X_play, y_play)

结果

Parallel(n_jobs=1)]: Done  18 out of  18 | elapsed:  2.0min finished
Parallel(n_jobs=2)]: Done  18 out of  18 | elapsed:  1.4min finished
Parallel(n_jobs=3)]: Done  18 out of  18 | elapsed:  1.3min finished
Parallel(n_jobs=4)]: Done  18 out of  18 | elapsed:  1.3min finished
Parallel(n_jobs=5)]: Done  18 out of  18 | elapsed:  1.4min finished
Parallel(n_jobs=6)]: Done  18 out of  18 | elapsed:  1.4min finished
Parallel(n_jobs=7)]: Done  18 out of  18 | elapsed:  1.4min finished
Parallel(n_jobs=8)]: Done  18 out of  18 | elapsed:  1.4min finished

第二项测试

随机森林分类器的使用要好得多.测试大小为0.5，30000图片.

Random Forest Classifier usage was much better. Test size was 0.5, 30000 images.

from sklearn.ensemble import RandomForestClassifier

rf_clf = RandomForestClassifier()
param_grid = [{'n_estimators': [20, 30, 40, 50, 60], 'max_features': [100, 200, 300, 400, 500], 'criterion': ['gini', 'entropy']}]

Parallel(n_jobs=1)]: Done 150 out of 150 | elapsed: 110.9min finished
Parallel(n_jobs=2)]: Done 150 out of 150 | elapsed: 56.8min finished
Parallel(n_jobs=3)]: Done 150 out of 150 | elapsed: 39.3min finished
Parallel(n_jobs=4)]: Done 150 out of 150 | elapsed: 35.3min finished
Parallel(n_jobs=5)]: Done 150 out of 150 | elapsed: 36.0min finished
Parallel(n_jobs=6)]: Done 150 out of 150 | elapsed: 34.4min finished
Parallel(n_jobs=7)]: Done 150 out of 150 | elapsed: 32.1min finished
Parallel(n_jobs=8)]: Done 150 out of 150 | elapsed: 30.1min finished