diff --git a/example/notebooks/alexnet.ipynb b/example/notebooks/alexnet.ipynb
index b7bb6bf266c2..c030d873cd08 100644
--- a/example/notebooks/alexnet.ipynb
+++ b/example/notebooks/alexnet.ipynb
@@ -29,7 +29,6 @@
    },
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
     "import mxnet as mx"
    ]
   },
@@ -402,7 +401,7 @@
     }
    ],
    "source": [
-    "mx.visualization.plot_network(\"AlexNet\", softmax)"
+    "mx.viz.plot_network(\"AlexNet\", softmax)"
    ]
   },
   {
@@ -425,28 +424,32 @@
     "# We set batch size for to 256\n",
     "batch_size = 256\n",
     "# We need to set correct path to image record file\n",
-    "# For ```mean_image```. if it doesn't exist, the iterator will generate one. Usually on normal HDD, it costs less than 10 minutes\n",
+    "# For ```mean_image```. if it doesn't exist, the iterator will generate one\n",
+    "# On HDD, single thread is able to process 800 images / sec\n",
     "# the input shape is in format (channel, height, width)\n",
     "# rand_crop option make source image randomly cropped to input_shape (3, 224, 224)\n",
     "# rand_mirror option make source image randomly mirrored\n",
     "# We use 2 threads to processing our data\n",
     "train_dataiter = mx.io.ImageRecordIter(\n",
+    "    shuffle=True,\n",
     "    path_imgrec=\"./Data/ImageNet/train.rec\",\n",
     "    mean_img=\"./Data/ImageNet/mean_224.bin\",\n",
     "    rand_crop=True,\n",
     "    rand_mirror=True,\n",
-    "    input_shape=(3, 224, 224),\n",
+    "    data_shape=(3, 224, 224),\n",
     "    batch_size=batch_size,\n",
-    "    nthread=2)\n",
+    "    prefetch_buffer=4,\n",
+    "    preprocess_threads=2)\n",
     "# similarly, we can declare our validation iterator\n",
     "val_dataiter = mx.io.ImageRecordIter(\n",
     "    path_imgrec=\"./Data/ImageNet/val.rec\",\n",
     "    mean_img=\"./Data/ImageNet/mean_224.bin\",\n",
     "    rand_crop=False,\n",
     "    rand_mirror=False,\n",
-    "    input_shape=(3, 224, 224),\n",
+    "    data_shape=(3, 224, 224),\n",
     "    batch_size=batch_size,\n",
-    "    nthread=2)"
+    "    prefetch_buffer=4,\n",
+    "    preprocess_threads=2)"
    ]
   },
   {
@@ -531,7 +534,7 @@
     "# When we use data iterator, we don't need to set y because label comes from data iterator directly\n",
     "# In this case, eval_data is also a data iterator\n",
     "# We will use accuracy to measure our model's performace\n",
-    "model.fit(X=train_dataiter, eval_data=val_dataiter, eval_metric='acc', verbose=True)\n",
+    "model.fit(X=train_dataiter, eval_data=val_dataiter, eval_metric='acc')\n",
     "# You need to wait for a while to get the result"
    ]
   },
diff --git a/tests/python/common/get_data.py b/tests/python/common/get_data.py
index 270132e448b8..65e8ac59ad6f 100644
--- a/tests/python/common/get_data.py
+++ b/tests/python/common/get_data.py
@@ -14,18 +14,14 @@ def GetMNIST_pkl():
 def GetMNIST_ubyte():
     if not os.path.isdir("data/"):
         os.system("mkdir data/")
-    if not os.path.exists('data/train-images-idx3-ubyte'):
-        os.system("wget http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz -P data/")
-        os.system("gunzip data/train-images-idx3-ubyte.gz")
-    if not os.path.exists('data/train-labels-idx1-ubyte'):
-        os.system("wget http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz -P data/")
-        os.system("gunzip data/train-labels-idx1-ubyte.gz")
-    if not os.path.exists('data/t10k-images-idx3-ubyte'):
-        os.system("wget http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz -P data/")
-        os.system("gunzip data/t10k-images-idx3-ubyte.gz")
-    if not os.path.exists('data/t10k-labels-idx1-ubyte'):
-        os.system("wget http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz -P data/")
-        os.system("gunzip data/t10k-labels-idx1-ubyte.gz")
+    if (not os.path.exists('data/train-images-idx3-ubyte')) or \
+       (not os.path.exists('data/train-labels-idx1-ubyte')) or \
+       (not os.path.exists('data/t10k-images-idx3-ubyte')) or \
+       (not os.path.exists('data/t10k-labels-idx1-ubyte')):
+        os.system("wget http://webdocs.cs.ualberta.ca/~bx3/data/mnist.zip -P data/")
+        os.chdir("./data")
+        os.system("unzip -u mnist.zip")
+        os.chdir("..")
 
 # download cifar
 def GetCifar10():
@@ -34,5 +30,5 @@ def GetCifar10():
     if not os.path.exists('data/cifar10.zip'):
         os.system("wget http://webdocs.cs.ualberta.ca/~bx3/data/cifar10.zip -P data/")
         os.chdir("./data")
-        os.system("unzip cifar10.zip")
+        os.system("unzip -u cifar10.zip")
         os.chdir("..")