diff --git a/monai/utils/generateddata.py b/monai/utils/generateddata.py
new file mode 100644
index 0000000000..26904943e1
--- /dev/null
+++ b/monai/utils/generateddata.py
@@ -0,0 +1,44 @@
+# Copyright 2020 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import numpy as np
+
+from monai.utils.arrayutils import rescale_array
+
+
+def create_test_image_2d(width, height, num_objs=12, rad_max=30, noise_max=0.0, num_seg_classes=5):
+    """
+    Return a noisy 2D image with `numObj' circles and a 2D mask image. The maximum radius of the circles is given as
+    `radMax'. The mask will have `numSegClasses' number of classes for segmentations labeled sequentially from 1, plus a
+    background class represented as 0. If `noiseMax' is greater than 0 then noise will be added to the image taken from
+    the uniform distribution on range [0,noiseMax).
+    """
+    image = np.zeros((width, height))
+
+    for i in range(num_objs):
+        x = np.random.randint(rad_max, width - rad_max)
+        y = np.random.randint(rad_max, height - rad_max)
+        rad = np.random.randint(5, rad_max)
+        spy, spx = np.ogrid[-x : width - x, -y : height - y]
+        circle = (spx * spx + spy * spy) <= rad * rad
+
+        if num_seg_classes > 1:
+            image[circle] = np.ceil(np.random.random() * num_seg_classes)
+        else:
+            image[circle] = np.random.random() * 0.5 + 0.5
+
+    labels = np.ceil(image).astype(np.int32)
+
+    norm = np.random.uniform(0, num_seg_classes * noise_max, size=image.shape)
+    noisyimage = rescale_array(np.maximum(image, norm))
+
+    return noisyimage, labels
diff --git a/runtests.sh b/runtests.sh
index 299b47dee6..a6e06f9198 100755
--- a/runtests.sh
+++ b/runtests.sh
@@ -6,7 +6,7 @@ set -e
 homedir="$( cd -P "$( dirname "${BASH_SOURCE[0]}" )" && pwd )"
 cd $homedir
 
-#export PYTHONPATH="$homedir:$PYTHONPATH"
+export PYTHONPATH="$homedir:$PYTHONPATH"
 
 # configuration values
 doCoverage=false
@@ -81,7 +81,7 @@ ${cmdprefix}${cmd} -m unittest
 # network training/inference/eval tests
 if [ "$doNetTests" = 'true' ]
 then
-    for i in examples/*.py
+    for i in tests/integration_*.py
     do
         echo $i
         ${cmdprefix}${cmd} $i
diff --git a/tests/integration_unet2d.py b/tests/integration_unet2d.py
new file mode 100644
index 0000000000..dc1b1b863a
--- /dev/null
+++ b/tests/integration_unet2d.py
@@ -0,0 +1,58 @@
+# Copyright 2020 MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import sys
+
+import torch
+import numpy as np
+
+from ignite.engine import create_supervised_trainer
+
+from monai import data, networks, utils
+
+
+def run_test(batch_size=64, train_steps=100, device=torch.device("cuda:0")):
+    def generate_test_batch():
+        for _ in range(train_steps):
+            im, seg = utils.generateddata.create_test_image_2d(128, 128, noise_max=1, num_objs=4, num_seg_classes=1)
+            yield im[None], seg[None].astype(np.float32)
+
+    def _prepare_batch(batch, device=None, non_blocking=False):
+        x, y = batch
+        return torch.from_numpy(x).to(device), torch.from_numpy(y).to(device)
+
+    net = networks.nets.UNet(
+        dimensions=2,
+        in_channels=1,
+        num_classes=1,
+        channels=(4, 8, 16, 32),
+        strides=(2, 2, 2),
+        num_res_units=2,
+    )
+
+    loss = networks.losses.DiceLoss()
+    opt = torch.optim.Adam(net.parameters(), 1e-4)
+    src = data.streams.BatchStream(generate_test_batch(), batch_size)
+
+    def loss_fn(pred, grnd): 
+        return loss(pred[0], grnd)
+
+    trainer = create_supervised_trainer(net, opt, loss_fn, device, False, _prepare_batch)
+
+    trainer.run(src, 1)
+
+    return trainer.state.output
+
+
+if __name__ == "__main__":
+    result = run_test()
+
+    sys.exit(0 if result < 1 else 1)
diff --git a/tests/utils.py b/tests/utils.py
index f780220b77..2e10a29912 100644
--- a/tests/utils.py
+++ b/tests/utils.py
@@ -15,7 +15,7 @@
 import torch
 import numpy as np
 
-from monai.utils.arrayutils import rescale_array
+from monai.utils.generateddata import create_test_image_2d
 
 quick_test_var = "QUICKTEST"
 
@@ -26,35 +26,6 @@ def skip_if_quick(obj):
     return unittest.skipIf(is_quick, "Skipping slow tests")(obj)
 
 
-def create_test_image(width, height, num_objs=12, rad_max=30, noise_max=0.0, num_seg_classes=5):
-    """
-    Return a noisy 2D image with `numObj' circles and a 2D mask image. The maximum radius of the circles is given as
-    `radMax'. The mask will have `numSegClasses' number of classes for segmentations labeled sequentially from 1, plus a
-    background class represented as 0. If `noiseMax' is greater than 0 then noise will be added to the image taken from
-    the uniform distribution on range [0,noiseMax).
-    """
-    image = np.zeros((width, height))
-
-    for i in range(num_objs):
-        x = np.random.randint(rad_max, width - rad_max)
-        y = np.random.randint(rad_max, height - rad_max)
-        rad = np.random.randint(5, rad_max)
-        spy, spx = np.ogrid[-x : width - x, -y : height - y]
-        circle = (spx * spx + spy * spy) <= rad * rad
-
-        if num_seg_classes > 1:
-            image[circle] = np.ceil(np.random.random() * num_seg_classes)
-        else:
-            image[circle] = np.random.random() * 0.5 + 0.5
-
-    labels = np.ceil(image).astype(np.int32)
-
-    norm = np.random.uniform(0, num_seg_classes * noise_max, size=image.shape)
-    noisyimage = rescale_array(np.maximum(image, norm))
-
-    return noisyimage, labels
-
-
 class ImageTestCase(unittest.TestCase):
     im_shape = (128, 128)
     input_channels = 1
@@ -62,7 +33,7 @@ class ImageTestCase(unittest.TestCase):
     num_classes = 3
 
     def setUp(self):
-        im, msk = create_test_image(self.im_shape[0], self.im_shape[1], 4, 20, 0, self.num_classes)
+        im, msk = create_test_image_2d(self.im_shape[0], self.im_shape[1], 4, 20, 0, self.num_classes)
 
         self.imt = torch.tensor(im[None, None])