50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
# pylint: skip-file
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import sys, argparse
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import xgboost as xgb
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import numpy as np
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from sklearn.datasets import make_classification
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import time
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n = 1000000
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num_rounds = 500
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def run_benchmark(args, gpu_algorithm, cpu_algorithm):
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print("Generating dataset: {} rows * {} columns".format(args.rows,args.columns))
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X, y = make_classification(args.rows, n_features=args.columns, random_state=7)
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dtrain = xgb.DMatrix(X, y)
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param = {'objective': 'binary:logistic',
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'max_depth': 6,
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'silent': 1,
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'eval_metric': 'auc'}
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param['tree_method'] = gpu_algorithm
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print("Training with '%s'" % param['tree_method'])
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tmp = time.time()
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xgb.train(param, dtrain, args.iterations)
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print ("Time: %s seconds" % (str(time.time() - tmp)))
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param['tree_method'] = cpu_algorithm
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print("Training with '%s'" % param['tree_method'])
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tmp = time.time()
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xgb.train(param, dtrain, args.iterations)
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print ("Time: %s seconds" % (str(time.time() - tmp)))
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parser = argparse.ArgumentParser()
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parser.add_argument('--algorithm', choices=['all', 'gpu_exact', 'gpu_hist'], default='all')
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parser.add_argument('--rows',type=int,default=1000000)
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parser.add_argument('--columns',type=int,default=50)
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parser.add_argument('--iterations',type=int,default=500)
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args = parser.parse_args()
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if 'gpu_hist' in args.algorithm:
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run_benchmark(args, args.algorithm, 'hist')
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if 'gpu_exact' in args.algorithm:
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run_benchmark(args, args.algorithm, 'exact')
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if 'all' in args.algorithm:
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run_benchmark(args, 'gpu_exact', 'exact')
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run_benchmark(args, 'gpu_hist', 'hist')
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