在我们的实验室中，我们有NVIDIA Tesla K80 GPU加速器计算，具有以下特性：Intel(R) Xeon(R) CPU E5-2670 v3 @2.30GHz, 48 CPU processors, 128GB RAM, 12 CPU cores在Linux 64位下运行。
我正在运行下面的代码，它在垂直地将不同的多个字符串添加到RandomForestRegressor模型的单个系列中之后执行GridSearchCV。

import sys
import imp
import glob
import os
import pandas as pd
import math
from sklearn.feature_extraction.text import CountVectorizer 
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
import matplotlib
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
import numpy as np
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import GridSearchCV
from sklearn.naive_bayes import MultinomialNB
from sklearn.linear_model import LassoCV
from sklearn.metrics import r2_score, mean_squared_error, make_scorer
from sklearn.model_selection import train_test_split
from math import sqrt
from sklearn.cross_validation import train_test_split

df = pd.concat(map(pd.read_csv, glob.glob(os.path.join('', "cubic*.csv"))), ignore_index=True)
#df = pd.read_csv('cubic31.csv')

for i in range(1,3):
    df['X_t'+str(i)] = df['X'].shift(i)

print(df)

df.dropna(inplace=True)

X = (pd.DataFrame({ 'X_%d'%i : df['X'].shift(i) for i in range(3)}).apply(np.nan_to_num, axis=0).values)

X = df.drop('Y', axis=1)
y = df['Y']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.40)

X_train = X_train.drop('time', axis=1)
X_test = X_test.drop('time', axis=1)

#Fit models with some grid search CV=5 (not to low), use the best model
parameters = {'n_estimators': [10,30,100,500,1000]}
clf_rf = RandomForestRegressor(random_state=1)
clf = GridSearchCV(clf_rf, parameters, cv=5, scoring='neg_mean_squared_error')
model = clf.fit(X_train, y_train)
model.cv_results_['params'][model.best_index_]
math.sqrt(model.best_score_*-1)
model.grid_scores_

#####
print()
print(model.grid_scores_)

print(math.sqrt(model.best_score_*-1))

#reg = RandomForestRegressor(criterion='mse')
clf_rf.fit(X_train,y_train)
modelPrediction = clf_rf.predict(X_test)
print(modelPrediction)

print("Number of predictions:",len(modelPrediction))

meanSquaredError=mean_squared_error(y_test, modelPrediction)
print("Mean Square Error (MSE):", meanSquaredError)
rootMeanSquaredError = sqrt(meanSquaredError)
print("Root-Mean-Square Error (RMSE):", rootMeanSquaredError)

####### to add the trendline
fig, ax = plt.subplots()
#df.plot(x='time', y='Y', ax=ax)
ax.plot(df['time'].values, df['Y'].values)

fig, ax = plt.subplots()
index_values=range(0,len(y_test))

y_test.sort_index(inplace=True)
X_test.sort_index(inplace=True)

modelPred_test = clf_rf.predict(X_test)
ax.plot(pd.Series(index_values), y_test.values)

PlotInOne=pd.DataFrame(pd.concat([pd.Series(modelPred_test), pd.Series(y_test.values)], axis=1))

plt.figure(); PlotInOne.plot(); plt.legend(loc='best')

字符串
当我为一个巨大的数据集（大约200万行）运行这个程序时，需要3天以上的时间来执行GridSearchCV。我想知道，因此，如果Python线程可以利用多个CPU。我们如何使这个（或其他Python程序）利用多个CPU，以便在短时间内更快地完成任务？谢谢你的任何提示！