Add temp output to debug centos crash

tests/python/gpu/test_gluon_gpu.py

@@ -597,7 +597,8 @@ def test_cudnn_dropout_reproducibility():
     assert_almost_equal(a.grad, b.grad)
 
 @mx.util.use_np
-def test_cuda_graphs():
+@pytest.mark.serial
+def test_cuda_graphs(capsys):
     class GraphTester(gluon.HybridBlock):
         def __init__(self, function_to_test, **kwargs):
             super(GraphTester, self).__init__(**kwargs)
@@ -653,54 +654,56 @@ def generate_inputs(self):
 
     N = 10
 
-    with environment({'MXNET_ENABLE_CUDA_GRAPHS': '1',
-                      'MXNET_USE_FUSION': '0'}):
-        device = mx.gpu(0)
-        for test_desc in tested_ops:
-            print("Testing ", test_desc.name)
-            inputs = test_desc.generate_inputs()
-            inputsg = [i.copy() for i in inputs]
-            for i in inputsg:
-                i.attach_grad()
-            seed = random.randint(0, 10000)
-            net = GraphTester(test_desc.f)
-            netg = GraphTester(test_desc.f)
-
-            # initialize parameters
-            net.initialize(device=device)
-            netg.initialize(device=device)
-
-            net(*inputs)
+    with capsys.disabled():
+        with environment({'MXNET_ENABLE_CUDA_GRAPHS': '1',
+                          'MXNET_CUDA_GRAPHS_VERBOSE': '1',
+                          'MXNET_USE_FUSION': '0'}):
+            device = mx.gpu(0)
+            for test_desc in tested_ops:
+                sys.stdout.write('Testing {}\n'.format( test_desc.name))
+                inputs = test_desc.generate_inputs()
+                inputsg = [i.copy() for i in inputs]
+                for i in inputsg:
+                    i.attach_grad()
+                seed = random.randint(0, 10000)
+                net = GraphTester(test_desc.f)
+                netg = GraphTester(test_desc.f)
+
+                # initialize parameters
+                net.initialize(device=device)
+                netg.initialize(device=device)
+
+                net(*inputs)
 
-            for p1, p2 in zip(net.collect_params().values(), netg.collect_params().values()):
-                p2.set_data(p1.data())
-
-            netg.hybridize(static_alloc=True, static_shape=True)
-
-            print("Testing inference mode")
-            with random_seed(seed):
-                for _ in range(N):
-                    assert_almost_equal(net(*inputs), netg(*inputsg))
+                for p1, p2 in zip(net.collect_params().values(), netg.collect_params().values()):
+                    p2.set_data(p1.data())
 
-            mx.npx.waitall()
-            print("Testing training mode")
-            for _ in range(N):
-                with random_seed(seed):
-                    with mx.autograd.record():
-                        out = net(*inputs)
-                    out.backward()
+                netg.hybridize(static_alloc=True, static_shape=True)
 
+                print("    Testing inference mode")
                 with random_seed(seed):
-                    with mx.autograd.record():
-                        outg = netg(*inputsg)
-                    outg.backward()
+                    for _ in range(N):
+                        assert_almost_equal(net(*inputs), netg(*inputsg))
 
-                assert_almost_equal(out, outg)
-                for i, ig in zip(inputs, inputsg):
-                    assert_almost_equal(i.grad, ig.grad)
-
-                for p1, p2 in zip(net.collect_params().values(), netg.collect_params().values()):
-                    assert_almost_equal(p1.data(), p2.data())
-                    if p1.grad_req != 'null':
-                        assert_almost_equal(p1.grad(), p2.grad())
+                mx.npx.waitall()
+                print("    Testing training mode")
+                for _ in range(N):
+                    with random_seed(seed):
+                        with mx.autograd.record():
+                            out = net(*inputs)
+                        out.backward()
+
+                    with random_seed(seed):
+                        with mx.autograd.record():
+                            outg = netg(*inputsg)
+                        outg.backward()
+
+                    assert_almost_equal(out, outg)
+                    for i, ig in zip(inputs, inputsg):
+                        assert_almost_equal(i.grad, ig.grad)
+
+                    for p1, p2 in zip(net.collect_params().values(), netg.collect_params().values()):
+                        assert_almost_equal(p1.data(), p2.data())
+                        if p1.grad_req != 'null':
+                            assert_almost_equal(p1.grad(), p2.grad())
             mx.npx.waitall()