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[GPU-Plugin] Major refactor 2 (#2664)

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* Change cmake option

* Move source files

* Move google tests

* Move python tests

* Move benchmarks

* Move documentation

* Remove makefile support

* Fix test run

* Move GPU tests
This commit is contained in:
Rory Mitchell
2017-09-08 09:57:16 +12:00
committed by GitHub
parent 8244f6f120
commit 15267eedf2
21 changed files with 76 additions and 249 deletions
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112
tests/python-gpu/test_large_sizes.py Normal file
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from __future__ import print_function
#pylint: skip-file
import sys
import time
sys.path.append("../../tests/python")
import xgboost as xgb
import testing as tm
import numpy as np
import unittest
from nose.plugins.attrib import attr
def eprint(*args, **kwargs):
print(*args, file=sys.stderr, **kwargs) ; sys.stderr.flush()
print(*args, file=sys.stdout, **kwargs) ; sys.stdout.flush()
rng = np.random.RandomState(1994)
# "realistic" size based upon http://stat-computing.org/dataexpo/2009/ , which has been processed to one-hot encode categoricalsxsy
cols = 31
# reduced to fit onto 1 gpu but still be large
rows3 = 5000 # small
rows2 = 4360032 # medium
rows1 = 42360032 # large
#rows1 = 152360032 # can do this for multi-gpu test (very large)
rowslist = [rows1, rows2, rows3]
@attr('slow')
class TestGPU(unittest.TestCase):
def test_large(self):
eprint("Starting test for large data")
tm._skip_if_no_sklearn()
for rows in rowslist:
eprint("Creating train data rows=%d cols=%d" % (rows,cols))
tmp = time.time()
np.random.seed(7)
X = np.random.rand(rows, cols)
y = np.random.rand(rows)
print("Time to Create Data: %r" % (time.time() - tmp))
eprint("Starting DMatrix(X,y)")
tmp = time.time()
ag_dtrain = xgb.DMatrix(X,y,nthread=40)
print("Time to DMatrix: %r" % (time.time() - tmp))
max_depth=6
max_bin=1024
# regression test --- hist must be same as exact on all-categorial data
ag_param = {'max_depth': max_depth,
'tree_method': 'exact',
'nthread': 0,
'eta': 1,
'silent': 0,
'debug_verbose': 5,
'objective': 'binary:logistic',
'eval_metric': 'auc'}
ag_paramb = {'max_depth': max_depth,
'tree_method': 'hist',
'nthread': 0,
'eta': 1,
'silent': 0,
'debug_verbose': 5,
'objective': 'binary:logistic',
'eval_metric': 'auc'}
ag_param2 = {'max_depth': max_depth,
'tree_method': 'gpu_hist',
'nthread': 0,
'eta': 1,
'silent': 0,
'debug_verbose': 5,
'n_gpus': 1,
'objective': 'binary:logistic',
'max_bin': max_bin,
'eval_metric': 'auc'}
ag_param3 = {'max_depth': max_depth,
'tree_method': 'gpu_hist',
'nthread': 0,
'eta': 1,
'silent': 0,
'debug_verbose': 5,
'n_gpus': -1,
'objective': 'binary:logistic',
'max_bin': max_bin,
'eval_metric': 'auc'}
ag_res = {}
ag_resb = {}
ag_res2 = {}
ag_res3 = {}
num_rounds = 1
tmp = time.time()
#eprint("hist updater")
#xgb.train(ag_paramb, ag_dtrain, num_rounds, [(ag_dtrain, 'train')],
# evals_result=ag_resb)
#print("Time to Train: %s seconds" % (str(time.time() - tmp)))
tmp = time.time()
eprint("gpu_hist updater 1 gpu")
xgb.train(ag_param2, ag_dtrain, num_rounds, [(ag_dtrain, 'train')],
evals_result=ag_res2)
print("Time to Train: %s seconds" % (str(time.time() - tmp)))
tmp = time.time()
eprint("gpu_hist updater all gpus")
xgb.train(ag_param3, ag_dtrain, num_rounds, [(ag_dtrain, 'train')],
evals_result=ag_res3)
print("Time to Train: %s seconds" % (str(time.time() - tmp)))