Xgboost GPU 加速

import xgboost as xgb
import numpy as np
from sklearn.datasets import fetch_covtype
from sklearn.model_selection import train_test_split
import time

# Fetch dataset using sklearn
cov = fetch_covtype()
X = cov.data
y = cov.target

# Create 0.75/0.25 train/test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, train_size=0.75, random_state=42)

# Specify sufficient boosting iterations to reach a minimum
num_round = 25 #3000
# Leave most parameters as default
param = {'objective': 'multi:softmax', # Specify multiclass classification
         'num_class': 8, # Number of possible output classes
         'tree_method': 'gpu_hist' # Use GPU accelerated algorithm
         }

# Convert input data from numpy to XGBoost format
dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test, label=y_test)

gpu_res = {} # Store accuracy result
tmp = time.time()
# Train model
param['tree_method'] = 'gpu_hist'
xgb.train(param, dtrain, num_round, evals=[(dtest, 'test')], evals_result=gpu_res)
print("GPU Training Time: %s seconds" % (str(time.time() - tmp)))

[0]	test-merror:0.254804
[1]	test-merror:0.247885
[2]	test-merror:0.24427
[3]	test-merror:0.240677
[4]	test-merror:0.238474
[5]	test-merror:0.234763
[6]	test-merror:0.232147
[7]	test-merror:0.229716
[8]	test-merror:0.227162
[9]	test-merror:0.224622
[10]	test-merror:0.222632
[11]	test-merror:0.220773
[12]	test-merror:0.218453
[13]	test-merror:0.215582
[14]	test-merror:0.214605
[15]	test-merror:0.212223
[16]	test-merror:0.211176
[17]	test-merror:0.209868
[18]	test-merror:0.208622
[19]	test-merror:0.205917
[20]	test-merror:0.20434
[21]	test-merror:0.203727
[22]	test-merror:0.202591
[23]	test-merror:0.201621
[24]	test-merror:0.199817
GPU Training Time: 4.505811929702759 seconds

# Repeat for CPU algorithm
tmp = time.time()
param['tree_method'] = 'hist'
cpu_res = {}
xgb.train(param, dtrain, num_round, evals=[(dtest, 'test')], evals_result=cpu_res)
print("CPU Training Time: %s seconds" % (str(time.time() - tmp)))

[0]	test-merror:0.254831
[1]	test-merror:0.247912
[2]	test-merror:0.244298
[3]	test-merror:0.24069
[4]	test-merror:0.238536
[5]	test-merror:0.234804
[6]	test-merror:0.232229
[7]	test-merror:0.229703
[8]	test-merror:0.227162
[9]	test-merror:0.224519
[10]	test-merror:0.222784
[11]	test-merror:0.220705
[12]	test-merror:0.21844
[13]	test-merror:0.21676
[14]	test-merror:0.214736
[15]	test-merror:0.212257
[16]	test-merror:0.210206
[17]	test-merror:0.209345
[18]	test-merror:0.207617
[19]	test-merror:0.206102
[20]	test-merror:0.205194
[21]	test-merror:0.202798
[22]	test-merror:0.202309
[23]	test-merror:0.200554
[24]	test-merror:0.199328
CPU Training Time: 49.719186305999756 seconds