[BREAKING] prevent multi-gpu usage (#4749)

* prevent multi-gpu usage

* fix distributed test

* combine gpu predictor tests

* set upper bound on n_gpus

tests/python-gpu/test_gpu_linear.py

@@ -29,15 +29,3 @@ class TestGPULinear(unittest.TestCase):
                 param, 150, self.datasets, scale_features=True)
             test_linear.assert_regression_result(results, 1e-2)
             test_linear.assert_classification_result(results)
-
-    @pytest.mark.mgpu
-    @pytest.mark.skipif(**tm.no_sklearn())
-    def test_gpu_coordinate_mgpu(self):
-        parameters = self.common_param.copy()
-        parameters['n_gpus'] = [-1]
-        parameters['gpu_id'] = [1]
-        for param in test_linear.parameter_combinations(parameters):
-            results = test_linear.run_suite(
-                param, 150, self.datasets, scale_features=True)
-            test_linear.assert_regression_result(results, 1e-2)
-            test_linear.assert_classification_result(results)

tests/python-gpu/test_gpu_updaters.py

@@ -36,17 +36,6 @@ class TestGPU(unittest.TestCase):
             cpu_results = run_suite(param, select_datasets=datasets)
             assert_gpu_results(cpu_results, gpu_results)
 
-    @pytest.mark.mgpu
-    def test_gpu_hist_mgpu(self):
-        variable_param = {'n_gpus': [-1], 'max_depth': [2, 10],
-                          'max_leaves': [255, 4],
-                          'max_bin': [2, 256],
-                          'grow_policy': ['lossguide'], 'debug_synchronize': [True]}
-        for param in parameter_combinations(variable_param):
-            param['tree_method'] = 'gpu_hist'
-            gpu_results = run_suite(param, select_datasets=datasets)
-            assert_results_non_increasing(gpu_results, 1e-2)
-
     @pytest.mark.mgpu
     def test_specified_gpu_id_gpu_update(self):
         variable_param = {'n_gpus': [1],

tests/python-gpu/test_large_sizes.py

@@ -25,7 +25,7 @@ cols = 31
 # reduced to fit onto 1 gpu but still be large
 rows3 = 5000  # small
 rows2 = 4360032  # medium
-rows1 = 42360032  # large
+rows1 = 32360032  # large
 # rows1 = 152360032 # can do this for multi-gpu test (very large)
 rowslist = [rows1, rows2, rows3]
 
@@ -67,15 +67,6 @@ class TestGPU(unittest.TestCase):
                          'objective': 'binary:logistic',
                          'max_bin': max_bin,
                          'eval_metric': 'auc'}
-            ag_param3 = {'max_depth': max_depth,
-                         'tree_method': 'gpu_hist',
-                         'nthread': 0,
-                         'eta': 1,
-                         'verbosity': 3,
-                         'n_gpus': -1,
-                         'objective': 'binary:logistic',
-                         'max_bin': max_bin,
-                         'eval_metric': 'auc'}
             ag_res = {}
             ag_resb = {}
             ag_res2 = {}
@@ -93,9 +84,3 @@ class TestGPU(unittest.TestCase):
             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)))

tests/python-gpu/test_pickling.py

@@ -35,8 +35,6 @@ class TestPickling(unittest.TestCase):
         x, y = build_dataset()
         train_x = xgb.DMatrix(x, label=y)
         param = {'tree_method': 'gpu_hist',
-                 'gpu_id': 0,
-                 'n_gpus': -1,
                  'verbosity': 1}
         bst = xgb.train(param, train_x)