Remove benchmark code in GPU test. (#5141)

* Update Jenkins script.

tests/ci_build/test_python.sh

@@ -26,11 +26,11 @@ fi
 # Run specified test suite
 case "$suite" in
   gpu)
-    pytest -v -s --fulltrace -m "(not slow) and (not mgpu)" tests/python-gpu
+    pytest -v -s --fulltrace -m "not mgpu" tests/python-gpu
     ;;
 
   mgpu)
-    pytest -v -s --fulltrace -m "(not slow) and mgpu" tests/python-gpu
+    pytest -v -s --fulltrace -m "mgpu" tests/python-gpu
     cd tests/distributed
     ./runtests-gpu.sh
     cd -

tests/python-gpu/test_gpu_prediction.py

@@ -3,12 +3,10 @@ from __future__ import print_function
 import numpy as np
 import unittest
 import xgboost as xgb
-import pytest
 
 rng = np.random.RandomState(1994)
 
 
-@pytest.mark.gpu
 class TestGPUPredict(unittest.TestCase):
     def test_predict(self):
         iterations = 10

tests/python-gpu/test_large_sizes.py

@@ -1,86 +0,0 @@
-from __future__ import print_function
-
-import sys
-import time
-import pytest
-
-sys.path.append("../../tests/python")
-import xgboost as xgb
-import numpy as np
-import unittest
-
-
-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 = 32360032  # large
-# rows1 = 152360032 # can do this for multi-gpu test (very large)
-rowslist = [rows1, rows2, rows3]
-
-
-@pytest.mark.slow
-class TestGPU(unittest.TestCase):
-    def test_large(self):
-        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_paramb = {'max_depth': max_depth,
-                         'tree_method': 'hist',
-                         'nthread': 0,
-                         'eta': 1,
-                         'verbosity': 3,
-                         'objective': 'binary:logistic',
-                         'eval_metric': 'auc'}
-            ag_param2 = {'max_depth': max_depth,
-                         'tree_method': 'gpu_hist',
-                         'nthread': 0,
-                         'eta': 1,
-                         'verbosity': 3,
-                         'gpu_id': 0,
-                         '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)))