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     new 2c6d631  [MXNET-307] Add .md tutorials to .ipynb for CI integration 
(#10537)
2c6d631 is described below

commit 2c6d631b5ada8b03693942309b972fad0c5d39f1
Author: ThomasDelteil <thomas.delte...@gmail.com>
AuthorDate: Mon Apr 16 16:54:20 2018 -0700

    [MXNET-307] Add .md tutorials to .ipynb for CI integration (#10537)
    
    * Updated tutorial to add jupyter notebook download
    
    * Adding tutorials to the CI
    
    * fix python2 error
    
    * update following tholane review
    
    * Update cnn.md
    
    update to latest API
---
 docs/tutorials/gluon/customop.md                   |   3 +
 docs/tutorials/gluon/data_augmentation.md          |  17 ++-
 docs/tutorials/gluon/datasets.md                   |  91 ++++++-------
 docs/tutorials/nlp/cnn.md                          |  47 ++++---
 docs/tutorials/python/data_augmentation.md         |  38 +++---
 .../python/data_augmentation_with_masks.md         |  20 ++-
 docs/tutorials/python/kvstore.md                   | 141 ++++++++++++---------
 .../tutorials/python/types_of_data_augmentation.md |  47 ++++---
 docs/tutorials/speech_recognition/ctc.md           |  11 ++
 .../unsupervised_learning/auto_encoders.md         |   5 -
 docs/tutorials/unsupervised_learning/gan.md        |   7 +-
 .../tutorials/vision/large_scale_classification.md |  10 ++
 tests/nightly/test_tutorial_config.txt             |  29 +++--
 13 files changed, 262 insertions(+), 204 deletions(-)

diff --git a/docs/tutorials/gluon/customop.md b/docs/tutorials/gluon/customop.md
index e10f398..df10788 100644
--- a/docs/tutorials/gluon/customop.md
+++ b/docs/tutorials/gluon/customop.md
@@ -196,3 +196,6 @@ x = mx.nd.uniform(shape=(4, 3))
 y = dense(x)
 print(y)
 ```
+
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/gluon/data_augmentation.md 
b/docs/tutorials/gluon/data_augmentation.md
index 97bb46f..df7462c 100644
--- a/docs/tutorials/gluon/data_augmentation.md
+++ b/docs/tutorials/gluon/data_augmentation.md
@@ -10,6 +10,7 @@ import mxnet as mx # used version '1.0.0' at time of writing
 import numpy as np
 from matplotlib.pyplot import imshow
 import multiprocessing
+import os
 
 mx.random.seed(42) # set seed for repeatability
 ```
@@ -27,17 +28,17 @@ def plot_mx_array(array):
 ```
 
 ```python
-!mkdir -p data/images
-!wget 
https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/inputs/0.jpg
 -P ./data/images/
+image_folder = os.path.join('data','images')
+mx.test_utils.download('https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/inputs/0.jpg',
 dirname=image_folder)
 ```
 
 ```python
-example_image = mx.image.imread("./data/images/0.jpg").astype("float32")
+example_image = mx.image.imread(os.path.join(image_folder, 
"0.jpg")).astype("float32")
 plot_mx_array(example_image)
 ```
 
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use/output_5_0.png)
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use/output_5_0.png)<!--notebook-skip-line-->
 
 
 ## Quick start with 
[`ImageFolderDataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html#mxnet.gluon.data.vision.datasets.ImageFolderDataset)
@@ -61,7 +62,7 @@ def aug_transform(data, label):
     return data, label
 
 
-training_dataset = mx.gluon.data.vision.ImageFolderDataset('./data', 
transform=aug_transform)
+training_dataset = mx.gluon.data.vision.ImageFolderDataset('data', 
transform=aug_transform)
 ```
 
 
@@ -75,7 +76,7 @@ plot_mx_array(sample_data*255)
 ```
 
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use/output_10_0.png)
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use/output_10_0.png)<!--notebook-skip-line-->
 
 
 In practice you should load images from a dataset with a 
[`mxnet.gluon.data.DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader)
 to take advantage of automatic batching and shuffling. Under the hood the 
`DataLoader` calls `__getitem__`, but you shouldn't need to call directly for 
anything other than debugging. Some practitioners pre-augment their datasets by 
applying a fixed number of augmentations to each image and  [...]
@@ -93,4 +94,6 @@ for data_batch, label_batch in training_data_loader:
 ```
 
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use/output_12_1.png)
\ No newline at end of file
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use/output_12_1.png)<!--notebook-skip-line-->
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
diff --git a/docs/tutorials/gluon/datasets.md b/docs/tutorials/gluon/datasets.md
index 0c9b537..21eac78 100644
--- a/docs/tutorials/gluon/datasets.md
+++ b/docs/tutorials/gluon/datasets.md
@@ -12,7 +12,10 @@ We first start by generating random data `X` (with 3 
variables) and correspondin
 
 ```python
 import mxnet as mx
+import os
+import tarfile
 
+mx.random.seed(42) # Fix the seed for reproducibility
 X = mx.random.uniform(shape=(10, 3))
 y = mx.random.uniform(shape=(10, 1))
 dataset = mx.gluon.data.dataset.ArrayDataset(X, y)
@@ -56,8 +59,9 @@ for X_batch, y_batch in data_loader:
     print("X_batch has shape {}, and y_batch has shape 
{}".format(X_batch.shape, y_batch.shape))
 ```
 
-    X_batch has shape (5, 3), and y_batch has shape (5, 1)
-    X_batch has shape (5, 3), and y_batch has shape (5, 1)
+`X_batch has shape (5, 3), and y_batch has shape (5, 1)` 
<!--notebook-skip-line-->
+
+`X_batch has shape (5, 3), and y_batch has shape (5, 1)` 
<!--notebook-skip-line-->
 
 
 We can see 2 mini-batches of data (and labels), each with 5 samples, which 
makes sense given we started with a dataset of 10 samples. When comparing the 
shape of the batches to the samples returned by the 
[`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset),
 we've gained an extra dimension at the start which is sometimes called the 
batch axis.
@@ -99,13 +103,14 @@ print("Label: {}".format(label))
 print("Label description: {}".format(label_desc[label]))
 ```
 
-    Data type: <class 'numpy.float32'>
-    Label: 8
-    Label description: Bag
+`Data type: <class 'numpy.float32'>`<!--notebook-skip-line-->
 
+`Label: 8`<!--notebook-skip-line-->
 
+`Label description: Bag`<!--notebook-skip-line-->
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/datasets/fashion_mnist_bag.png)
+
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/datasets/fashion_mnist_bag.png)<!--notebook-skip-line-->
 
 
 When training machine learning models it is important to shuffle the training 
samples every time you pass through the dataset (i.e. each epoch). Sometimes 
the order of your samples will have a spurious relationship with the target 
variable, and shuffling the samples helps remove this. With 
[`DataLoader`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataloader#mxnet.gluon.data.DataLoader)
 it's as simple as adding `shuffle=True`. You don't need to shuffle the val 
[...]
@@ -175,11 +180,11 @@ for epoch in range(epochs):
     print("Epoch {}, training loss: {:.2f}, validation loss: 
{:.2f}".format(epoch, train_loss, valid_loss))
 ```
 
-    Epoch 0, training loss: 0.54, validation loss: 0.45
-    Epoch 1, training loss: 0.40, validation loss: 0.39
-    Epoch 2, training loss: 0.36, validation loss: 0.39
-    Epoch 3, training loss: 0.33, validation loss: 0.34
-    Epoch 4, training loss: 0.32, validation loss: 0.33
+`Epoch 0, training loss: 0.54, validation loss: 0.45`<!--notebook-skip-line-->
+
+`...`<!--notebook-skip-line-->
+
+`Epoch 4, training loss: 0.32, validation loss: 0.33`<!--notebook-skip-line-->
 
 
 # Using own data with included `Dataset`s
@@ -187,46 +192,39 @@ for epoch in range(epochs):
 Gluon has a number of different 
[`Dataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=dataset#mxnet.gluon.data.Dataset)
 classes for working with your own image data straight out-of-the-box. You can 
get started quickly using the 
[`mxnet.gluon.data.vision.datasets.ImageFolderDataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=imagefolderdataset#mxnet.gluon.data.vision.datasets.ImageFolderDataset)
 which loads images directly from a [...]
 
 We will run through an example for image classification, but a similar process 
applies for other vision tasks. If you already have your own collection of 
images to work with you should partition your data into training and test sets, 
and place all objects of the same class into seperate folders. Similar to:
-
+```
     ./images/train/car/abc.jpg
     ./images/train/car/efg.jpg
     ./images/train/bus/hij.jpg
     ./images/train/bus/klm.jpg
     ./images/test/car/xyz.jpg
     ./images/test/bus/uvw.jpg
+```
 
 You can download the Caltech 101 dataset if you don't already have images to 
work with for this example, but please note the download is 126MB.
 
 ```python
-!wget 
http://www.vision.caltech.edu/Image_Datasets/Caltech101/101_ObjectCategories.tar.gz
-!tar -xzf 101_ObjectCategories.tar.gz
+
+data_folder = "data"
+dataset_name = "101_ObjectCategories"
+archive_file = "{}.tar.gz".format(dataset_name)
+archive_path = os.path.join(data_folder, archive_file)
+data_url = "https://s3.us-east-2.amazonaws.com/mxnet-public/";
+
+if not os.path.isfile(archive_path):
+    mx.test_utils.download("{}{}".format(data_url, archive_file), dirname = 
data_folder)
+    print('Extracting {} in {}...'.format(archive_file, data_folder))
+    tar = tarfile.open(archive_path, "r:gz")
+    tar.extractall(data_folder)
+    tar.close()
+    print('Data extracted.')
 ```
 
-After downloading and extracting the data archive, we seperate the data into 
training and test sets (50:50 split), and place images of the same class into 
the same folders, as required for using 
[`ImageFolderDataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=imagefolderdataset#mxnet.gluon.data.vision.datasets.ImageFolderDataset).
+After downloading and extracting the data archive, we have two folders: 
`data/101_ObjectCategories` and `data/101_ObjectCategories_test`. We load the 
data into separate training and testing  
[`ImageFolderDataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=imagefolderdataset#mxnet.gluon.data.vision.datasets.ImageFolderDataset)s.
 
 ```python
-import shutil
-import os
-
-def split_train_test(source_dir='./101_ObjectCategories', 
train_dir='./images/train', test_dir='./images/test'):
-    """
-    Walks through source_dir and alternates between places files in the 
train_dir and the test_dir.
-    """
-    train_set = True
-    for root, dirs, files in os.walk(source_dir):
-        for name in files:
-            current_filepath = os.path.join(root, name)
-            dataset_dir = train_dir if train_set else test_dir
-            new_filepath = current_filepath.replace(source_dir, dataset_dir)
-            try:
-                os.makedirs(os.path.dirname(new_filepath))
-            except FileExistsError:
-                pass
-            shutil.move(current_filepath, new_filepath)
-            train_set = not train_set
-    shutil.rmtree(source_dir)
-
-split_train_test()
+training_path = os.path.join(data_folder, dataset_name)
+testing_path = os.path.join(data_folder, "{}_test".format(dataset_name))
 ```
 
 We instantiate the 
[`ImageFolderDataset`](https://mxnet.incubator.apache.org/api/python/gluon/data.html?highlight=imagefolderdataset#mxnet.gluon.data.vision.datasets.ImageFolderDataset)s
 by providing the path to the data, and the folder structure will be traversed 
to determine which image classes are available and which images correspond to 
each class. You must take care to ensure the same classes are both the training 
and testing datasets, otherwise the label encodings can get muddled.
@@ -235,8 +233,8 @@ Optionally, you can pass a `transform` parameter to these 
[`Dataset`](https://mx
 
 
 ```python
-train_dataset = 
mx.gluon.data.vision.datasets.ImageFolderDataset('./images/train')
-test_dataset = 
mx.gluon.data.vision.datasets.ImageFolderDataset('./images/test')
+train_dataset = mx.gluon.data.vision.datasets.ImageFolderDataset(training_path)
+test_dataset = mx.gluon.data.vision.datasets.ImageFolderDataset(testing_path)
 ```
 
 Samples from these datasets are tuples of data and label. Images are loaded 
from disk, decoded and optionally transformed when the `__getitem__(i)` method 
is called (equivalent to `train_dataset[i]`).
@@ -245,7 +243,7 @@ As with the Fashion MNIST dataset the labels will be 
integer encoded. You can us
 
 
 ```python
-sample_idx = 888
+sample_idx = 539
 sample = train_dataset[sample_idx]
 data = sample[0]
 label = sample[1]
@@ -257,13 +255,15 @@ print("Label description: 
{}".format(train_dataset.synsets[label]))
 assert label == 1
 ```
 
-    Data type: <class 'numpy.uint8'>
-    Label: 2
-    Label description: Faces_easy
+`Data type: <class 'numpy.uint8'>`<!--notebook-skip-line-->
+
+`Label: 1`<!--notebook-skip-line-->
+
+`Label description: Faces_easy` <!--notebook-skip-line-->
+
 
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/datasets/caltech101_face.png)<!--notebook-skip-line-->
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/gluon/datasets/caltech101_face.png
-)
 
 # Using own data with custom `Dataset`s
 
@@ -308,3 +308,4 @@ for X_batch, y_batch in data_iter_loader:
     assert X_batch.shape == (5, 3)
     assert y_batch.shape == (5, 1)
 ```
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
diff --git a/docs/tutorials/nlp/cnn.md b/docs/tutorials/nlp/cnn.md
index 7f56b76..b3d7d0d 100644
--- a/docs/tutorials/nlp/cnn.md
+++ b/docs/tutorials/nlp/cnn.md
@@ -149,11 +149,15 @@ print('vocab_size', vocab_size)
 print('sentence max words', sentence_size)
 ```
 
-    Train/Dev split: 9662/1000
-    train shape: (9662, 56)
-    dev shape: (1000, 56)
-    vocab_size 18766
-    sentence max words 56
+`Train/Dev split: 9662/1000`<!--notebook-skip-line-->
+
+`train shape: (9662, 56)`<!--notebook-skip-line-->
+
+`dev shape: (1000, 56)`<!--notebook-skip-line-->
+
+`vocab_size 18766`<!--notebook-skip-line-->
+
+`sentence max words 56`<!--notebook-skip-line-->
 
 
 Now that we prepared the training and test data by loading, vectorizing and 
shuffling it we can go on to defining the network architecture we want to train 
with the data.
@@ -187,11 +191,12 @@ print('embedding dimensions', num_embed)
 embed_layer = mx.sym.Embedding(data=input_x, input_dim=vocab_size, 
output_dim=num_embed, name='vocab_embed')
 
 # reshape embedded data for next layer
-conv_input = mx.sym.Reshape(data=embed_layer, target_shape=(batch_size, 1, 
sentence_size, num_embed))
+conv_input = mx.sym.Reshape(data=embed_layer, shape=(batch_size, 1, 
sentence_size, num_embed))
 ```
 
-    batch size 50
-    embedding dimensions 300
+`batch size 50` <!--notebook-skip-line-->
+
+`embedding dimensions 300` <!--notebook-skip-line-->
 
 
 The next layer in the network performs convolutions over the ordered embedded 
word vectors in a sentence using multiple filter sizes, sliding over 3, 4 or 5 
words at a time. This is the equivalent of looking at all 3-grams, 4-grams and 
5-grams in a sentence and will allow us to understand how words contribute to 
sentiment in the context of those around them.
@@ -219,10 +224,10 @@ total_filters = num_filter * len(filter_list)
 concat = mx.sym.Concat(*pooled_outputs, dim=1)
 
 # reshape for next layer
-h_pool = mx.sym.Reshape(data=concat, target_shape=(batch_size, total_filters))
+h_pool = mx.sym.Reshape(data=concat, shape=(batch_size, total_filters))
 ```
 
-    convolution filters [3, 4, 5]
+`convolution filters [3, 4, 5]` <!--notebook-skip-line-->
 
 
 Next, we add dropout regularization, which will randomly disable a fraction of 
neurons in the layer (set to 50% here) to ensure that that model does not 
overfit. This prevents neurons from co-adapting and forces them to learn 
individually useful features.
@@ -233,7 +238,7 @@ This is necessary for our model because the dataset has a 
vocabulary of size aro
 ```python
 # dropout layer
 dropout = 0.5
-print 'dropout probability', dropout
+print('dropout probability', dropout)
 
 if dropout > 0.0:
     h_drop = mx.sym.Dropout(data=h_pool, p=dropout)
@@ -242,7 +247,7 @@ else:
 
 ```
 
-    dropout probability 0.5
+`dropout probability 0.5` <!--notebook-skip-line-->
 
 
 Finally, we add a fully connected layer to add non-linearity to the model. We 
then classify the resulting output of this layer using a softmax function, 
yielding a result between 0 (negative sentiment) and 1 (positive).
@@ -277,10 +282,8 @@ import time
 # Define the structure of our CNN Model (as a named tuple)
 CNNModel = namedtuple("CNNModel", ['cnn_exec', 'symbol', 'data', 'label', 
'param_blocks'])
 
-# Define what device to train/test on
-ctx = mx.gpu(0)
-# If you have no GPU on your machine change this to
-# ctx = mx.cpu(0)
+# Define what device to train/test on, use GPU if available
+ctx = mx.gpu() if mx.test_utils.list_gpus() else mx.cpu()
 
 arg_names = cnn.list_arguments()
 
@@ -430,16 +433,9 @@ for iteration in range(epoch):
     learning rate (step size) 0.0005
     epochs to train for 50
     Iter [0] Train: Time: 3.903s, Training Accuracy: 56.290             --- 
Dev Accuracy thus far: 63.300
-    Iter [1] Train: Time: 3.142s, Training Accuracy: 71.917             --- 
Dev Accuracy thus far: 69.400
-    Iter [2] Train: Time: 3.146s, Training Accuracy: 80.508             --- 
Dev Accuracy thus far: 73.900
-    Iter [3] Train: Time: 3.142s, Training Accuracy: 87.233             --- 
Dev Accuracy thus far: 76.300
-    Iter [4] Train: Time: 3.145s, Training Accuracy: 91.057             --- 
Dev Accuracy thus far: 77.100
-    Iter [5] Train: Time: 3.145s, Training Accuracy: 94.073             --- 
Dev Accuracy thus far: 77.700
-    Iter [6] Train: Time: 3.147s, Training Accuracy: 96.000             --- 
Dev Accuracy thus far: 77.400
-    Iter [7] Train: Time: 3.150s, Training Accuracy: 97.399             --- 
Dev Accuracy thus far: 77.100
+    ...
     Iter [8] Train: Time: 3.144s, Training Accuracy: 98.425             --- 
Dev Accuracy thus far: 78.000
     Saved checkpoint to ./cnn-0009.params
-    Iter [9] Train: Time: 3.151s, Training Accuracy: 99.192             --- 
Dev Accuracy thus far: 77.100
     ...
 
 Now that we have gone through the trouble of training the model, we have 
stored the learned parameters in the .params file in our local directory. We 
can now load this file whenever we want and predict the sentiment of new 
sentences by running them through a forward pass of the trained model.
@@ -450,3 +446,6 @@ Now that we have gone through the trouble of training the 
model, we have stored
 
 ## Next Steps
 * [MXNet tutorials index](http://mxnet.io/tutorials/index.html)
+
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/python/data_augmentation.md 
b/docs/tutorials/python/data_augmentation.md
index 30b5b7d..e4dbbb6 100644
--- a/docs/tutorials/python/data_augmentation.md
+++ b/docs/tutorials/python/data_augmentation.md
@@ -11,6 +11,7 @@ import mxnet as mx # used version '1.0.0' at time of writing
 import numpy as np
 from matplotlib.pyplot import imshow
 import multiprocessing
+import os
 
 mx.random.seed(42) # set seed for repeatability
 ```
@@ -28,17 +29,14 @@ def plot_mx_array(array):
 ```
 
 ```python
-!mkdir -p data/images
-!wget 
https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/inputs/0.jpg
 -P ./data/images/
-```
-
-```python
-example_image = mx.image.imread("./data/images/0.jpg").astype("float32")
+image_dir = os.path.join("data", "images")
+mx.test_utils.download('https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/inputs/0.jpg',
 dirname=image_dir)
+example_image = 
mx.image.imread(os.path.join(image_dir,"0.jpg")).astype("float32")
 plot_mx_array(example_image)
 ```
 
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use/output_5_0.png)
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use/output_5_0.png)<!--notebook-skip-line-->
 
 
 ## Quick start using 
[`ImageIter`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=imageiter#mxnet.image.ImageIter)
@@ -49,13 +47,21 @@ One of the most convenient ways to augment your image data 
is via arguments of [
 We show a simple example of this below, after creating an `images.lst` file 
used by the 
[`ImageIter`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=imageiter#mxnet.image.ImageIter).
 Use 
[`tools/im2rec.py`](https://github.com/apache/incubator-mxnet/blob/master/tools/im2rec.py)
 to create the `images.lst` if you don't already have this for your data.
 
 ```python
-!echo -e "0\t0.000000\timages/0.jpg" > ./data/images.lst
+path_to_image = os.path.join("images", "0.jpg")
+index = 0
+label = 0.
+list_file_content = "{0}\t{1:.5f}\t{2}".format(index, label, path_to_image)
+
+path_list_file = os.path.join(image_dir, "images.lst")
+with open(path_list_file, 'w') as f:
+    f.write(list_file_content)
+
 ```
 
 ```python
 training_iter = mx.image.ImageIter(batch_size = 1,
                                    data_shape = (3, 300, 300),
-                                   path_root= './data', 
path_imglist='./data/images.lst',
+                                   path_root= 'data', 
path_imglist=path_list_file,
                                    rand_crop=0.5, rand_mirror=True, 
inter_method=10,
                                    brightness=0.125, contrast=0.125, 
saturation=0.125,
                                    pca_noise=0.02
@@ -73,7 +79,7 @@ for batch in training_iter:
 ```
 
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use/output_28_1.png)
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use/output_28_1.png)<!--notebook-skip-line-->
 
 
[`mxnet.image.ImageDetIter`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=imagedetiter#mxnet.image.ImageDetIter)
 works similarly (with 
[`mxnet.image.CreateDetAugmenter`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=createdetaugmenter#mxnet.image.CreateDetAugmenter)),
 but 
[`mxnet.io.ImageRecordIter`](https://mxnet.incubator.apache.org/api/python/io/io.html?highlight=imagerecorditer#mxnet.io.ImageRecordIter)
 has a slightly different i [...]
 
@@ -93,7 +99,7 @@ assert aug_image.shape == (100, 100, 3)
 ```
 
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use/output_16_0.png)
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use/output_16_0.png)<!--notebook-skip-line-->
 
 
 ### Augmenter Classes
@@ -109,7 +115,7 @@ assert aug_image.shape == (100, 100, 3)
 ```
 
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use/output_19_0.png)
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use/output_19_0.png)<!--notebook-skip-line-->
 
 
 ### Augmenter list
@@ -130,7 +136,7 @@ assert all([isinstance(a, mx.image.Augmenter) for a in 
aug_list])
 ```
 
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use/output_22_1.png)
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use/output_22_1.png)<!--notebook-skip-line-->
 
 
 
@@ -147,9 +153,11 @@ assert all([isinstance(a, mx.image.Augmenter) for a in 
aug_list])
 ```
 
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use/output_23_1.png)
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use/output_23_1.png)<!--notebook-skip-line-->
 
 
 __*Watch Out!*__ Check some examples that are output after applying all the 
augmentations. You may find that the augmentation steps are too severe and may 
actually prevent the model from learning. Some of the augmentation parameters 
used in this tutorial are set high for demonstration purposes (e.g. 
`brightness=1`); you might want to reduce them if your training error stays too 
high during training. Some examples of excessive augmentation are shown below:
 
-<img 
src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use//severe_aug.png";
 alt="Drawing" style="width: 700px;"/>
\ No newline at end of file
+<img 
src="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/use//severe_aug.png";
 alt="Drawing" style="width: 700px;"/>
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
diff --git a/docs/tutorials/python/data_augmentation_with_masks.md 
b/docs/tutorials/python/data_augmentation_with_masks.md
index 28a57b6..ac587ac 100644
--- a/docs/tutorials/python/data_augmentation_with_masks.md
+++ b/docs/tutorials/python/data_augmentation_with_masks.md
@@ -97,15 +97,9 @@ class ImageWithMaskDataset(dataset.Dataset):
 Usually Datasets are used in conjunction with DataLoaders, but we'll sample a 
single base image and mask pair for testing purposes. Calling `dataset[0]` 
(which is equivalent to `dataset.__getitem__(0)`) returns the first base image 
and mask pair from the `_image_list`. At first download the sample images and 
then we'll load them without any augmentation.
 
 ```python
-!wget 
https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/inputs/0.jpg
 -P ./data/images
-```
-
-```python
-!wget 
https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/inputs/0_mask.png
 -P ./data/images
-```
-
-```python
-image_dir = "./data/images"
+image_dir = os.path.join("data", "images")
+mx.test_utils.download('https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/inputs/0.jpg',
 dirname=image_dir)
+mx.test_utils.download('https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/inputs/0_mask.png',
 dirname=image_dir)
 dataset = ImageWithMaskDataset(root=image_dir)
 sample = dataset.__getitem__(0)
 sample_base = sample[0].astype('float32')
@@ -133,7 +127,7 @@ plot_mx_arrays([sample_base, sample_mask])
 ```
 
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/with_mask/output_16_0.png)
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/with_mask/output_16_0.png)<!--notebook-skip-line-->
 
 
 # Implementing `transform` for Augmentation
@@ -196,7 +190,7 @@ It's simple to use augmentation now that we have the 
`joint_transform` function
 
 
 ```python
-image_dir = "./data/images"
+image_dir = os.path.join("data","images")
 ds = ImageWithMaskDataset(root=image_dir, transform=joint_transform)
 sample = ds.__getitem__(0)
 assert len(sample) == 2
@@ -206,7 +200,7 @@ plot_mx_arrays([sample[0]*255, sample[1]*255])
 ```
 
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/with_mask/output_22_0.png)
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/with_mask/output_22_0.png)<!--notebook-skip-line-->
 
 
 # Summary
@@ -246,3 +240,5 @@ mkdir coco_data/masks
 mv -v stuff_val2017_pixelmaps/* coco_data/masks/
 rm -r stuff_val2017_pixelmaps
 ```
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
diff --git a/docs/tutorials/python/kvstore.md b/docs/tutorials/python/kvstore.md
index 44afb87..3e6bbf1 100644
--- a/docs/tutorials/python/kvstore.md
+++ b/docs/tutorials/python/kvstore.md
@@ -10,76 +10,89 @@ Let's consider a simple example: initializing
 a (`int`, `NDArray`) pair into the store, and then pulling the value out:
 
 ```python
-    >>> kv = mx.kv.create('local') # create a local kv store.
-    >>> shape = (2,3)
-    >>> kv.init(3, mx.nd.ones(shape)*2)
-    >>> a = mx.nd.zeros(shape)
-    >>> kv.pull(3, out = a)
-    >>> print a.asnumpy()
-    [[ 2.  2.  2.]
-     [ 2.  2.  2.]]
+import mxnet as mx
+
+kv = mx.kv.create('local') # create a local kv store.
+shape = (2,3)
+kv.init(3, mx.nd.ones(shape)*2)
+a = mx.nd.zeros(shape)
+kv.pull(3, out = a)
+print(a.asnumpy())
 ```
 
+`[[ 2.  2.  2.],[ 2.  2.  2.]]`<!--notebook-skip-line-->
+
 ## Push, Aggregate, and Update
 
 For any key that has been initialized, you can push a new value with the same 
shape to the key:
 
 ```python
-    >>> kv.push(3, mx.nd.ones(shape)*8)
-    >>> kv.pull(3, out = a) # pull out the value
-    >>> print a.asnumpy()
-    [[ 8.  8.  8.]
-     [ 8.  8.  8.]]
+kv.push(3, mx.nd.ones(shape)*8)
+kv.pull(3, out = a) # pull out the value
+print(a.asnumpy())
 ```
 
+`[[ 8.  8.  8.],[ 8.  8.  8.]]`<!--notebook-skip-line-->
+
 The data for pushing can be stored on any device. Furthermore, you can push 
multiple
 values into the same key, where KVStore will first sum all of these
 values and then push the aggregated value:
 
 ```python
-    >>> gpus = [mx.gpu(i) for i in range(4)]
-    >>> b = [mx.nd.ones(shape, gpu) for gpu in gpus]
-    >>> kv.push(3, b)
-    >>> kv.pull(3, out = a)
-    >>> print a.asnumpy()
-    [[ 4.  4.  4.]
-     [ 4.  4.  4.]]
+# The numbers used below assume 4 GPUs
+gpus = mx.test_utils.list_gpus()
+if len(gpus) > 1:
+    contexts = [mx.gpu(i) for i in gpus]
+else:
+    contexts = [mx.cpu(i) for i in range(4)]
+b = [mx.nd.ones(shape, ctx) for ctx in contexts]
+kv.push(3, b)
+kv.pull(3, out = a)
+print(a.asnumpy())
 ```
 
+`[[ 4.  4.  4.],[ 4.  4.  4.]]`<!--notebook-skip-line-->
+
 For each push, KVStore combines the pushed value with the value stored using an
 `updater`. The default updater is `ASSIGN`. You can replace the default to
 control how data is merged:
 
 ```python
-    >>> def update(key, input, stored):
-    >>>     print "update on key: %d" % key
-    >>>     stored += input * 2
-    >>> kv._set_updater(update)
-    >>> kv.pull(3, out=a)
-    >>> print a.asnumpy()
-    [[ 4.  4.  4.]
-     [ 4.  4.  4.]]
-    >>> kv.push(3, mx.nd.ones(shape))
-    update on key: 3
-    >>> kv.pull(3, out=a)
-    >>> print a.asnumpy()
-    [[ 6.  6.  6.]
-     [ 6.  6.  6.]]
+def update(key, input, stored):
+    print("update on key: %d" % key)
+    stored += input * 2
+kv._set_updater(update)
+kv.pull(3, out=a)
+print(a.asnumpy())
+```
+
+`[[ 4.  4.  4.],[ 4.  4.  4.]]`<!--notebook-skip-line-->
+
+```python
+kv.push(3, mx.nd.ones(shape))
+#
+kv.pull(3, out=a)
+print(a.asnumpy())
 ```
 
+`update on key: 3`<!--notebook-skip-line-->
+
+`[[ 6.  6.  6.],[ 6.  6.  6.]]`<!--notebook-skip-line-->
+
+
 ## Pull
 
 You've already seen how to pull a single key-value pair. Similarly, to push, 
you can
 pull the value onto several devices with a single call:
 
 ```python
-    >>> b = [mx.nd.ones(shape, gpu) for gpu in gpus]
-    >>> kv.pull(3, out = b)
-    >>> print b[1].asnumpy()
-    [[ 6.  6.  6.]
-     [ 6.  6.  6.]]
+b = [mx.nd.ones(shape, ctx) for ctx in contexts]
+kv.pull(3, out = b)
+print(b[1].asnumpy())
 ```
 
+`[ 6.  6.  6.]],[[ 6.  6.  6.]`<!--notebook-skip-line-->
+
 ## Handle a List of Key-Value Pairs
 
 All operations introduced so far involve a single key. KVStore also provides
@@ -88,33 +101,39 @@ an interface for a list of key-value pairs.
 For a single device:
 
 ```python
-    >>> keys = [5, 7, 9]
-    >>> kv.init(keys, [mx.nd.ones(shape)]*len(keys))
-    >>> kv.push(keys, [mx.nd.ones(shape)]*len(keys))
-    update on key: 5
-    update on key: 7
-    update on key: 9
-    >>> b = [mx.nd.zeros(shape)]*len(keys)
-    >>> kv.pull(keys, out = b)
-    >>> print b[1].asnumpy()
-    [[ 3.  3.  3.]
-     [ 3.  3.  3.]]
+keys = [5, 7, 9]
+kv.init(keys, [mx.nd.ones(shape)]*len(keys))
+kv.push(keys, [mx.nd.ones(shape)]*len(keys))
+b = [mx.nd.zeros(shape)]*len(keys)
+kv.pull(keys, out = b)
+print(b[1].asnumpy())
 ```
 
+`update on key: 5`<!--notebook-skip-line-->
+
+`update on key: 7`<!--notebook-skip-line-->
+
+`update on key: 9`<!--notebook-skip-line-->
+
+`[[ 3.  3.  3.],[ 3.  3.  3.]]`<!--notebook-skip-line-->
+
 For multiple devices:
 
 ```python
-    >>> b = [[mx.nd.ones(shape, gpu) for gpu in gpus]] * len(keys)
-    >>> kv.push(keys, b)
-    update on key: 5
-    update on key: 7
-    update on key: 9
-    >>> kv.pull(keys, out = b)
-    >>> print b[1][1].asnumpy()
-    [[ 11.  11.  11.]
-     [ 11.  11.  11.]]
+b = [[mx.nd.ones(shape, ctx) for ctx in contexts]] * len(keys)
+kv.push(keys, b)
+kv.pull(keys, out = b)
+print(b[1][1].asnumpy())
 ```
 
+`update on key: 5`<!--notebook-skip-line-->
+
+`update on key: 7`<!--notebook-skip-line-->
+
+`update on key: 9`<!--notebook-skip-line-->
+
+`[[ 11.  11.  11.],[ 11.  11.  11.]]`<!--notebook-skip-line-->
+
 ## Run on Multiple Machines
 Based on parameter server, the `updater` runs on the server nodes.
 When the distributed version is ready, we will update this section.
@@ -135,4 +154,6 @@ When the distributed version is ready, we will update this 
section.
 <!-- flexibly as your choice.  -->
 
 ## Next Steps
-* [MXNet tutorials index](http://mxnet.io/tutorials/index.html)
\ No newline at end of file
+* [MXNet tutorials index](http://mxnet.io/tutorials/index.html)
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
diff --git a/docs/tutorials/python/types_of_data_augmentation.md 
b/docs/tutorials/python/types_of_data_augmentation.md
index 9ace047..4cd1ad7 100644
--- a/docs/tutorials/python/types_of_data_augmentation.md
+++ b/docs/tutorials/python/types_of_data_augmentation.md
@@ -32,12 +32,9 @@ def plot_mx_array(array):
 We load an example image, this will be the target for our augmentations in the 
tutorial. 
 
 ```python
-!wget 
https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/inputs/0.jpg
-```
-
-```python
-example_image = mx.image.imread("./0.jpg")
-assert str(example_image.dtype) == "<class 'numpy.uint8'>"
+mx.test_utils.download('https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/inputs/0.jpg')
+example_image = mx.image.imread("0.jpg")
+assert example_image.dtype == np.uint8
 ```
 
 
@@ -50,7 +47,7 @@ plot_mx_array(example_image)
 ```
 
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_8_0.png)
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_8_0.png)<!--notebook-skip-line-->
 
 
 # Position Augmentation
@@ -75,7 +72,7 @@ assert example_image.shape == (427, 640, 3)
 assert aug_image.shape == (100, 100, 3)
 ```
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_13_1.png)
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_13_1.png)<!--notebook-skip-line-->
 
 
 __*Watch Out!*__ Crop are a great way of adding diversity to your training 
examples, but be careful not to take it to the extreme. An example of this 
would be cropping out an object of interest from the image completely. 
Visualise a few examples after cropping to determine if this will be an issue.
@@ -99,7 +96,7 @@ assert example_image.shape == (427, 640, 3)
 assert aug_image.shape == (50, 74, 3)
 ```
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_17_1.png)
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_17_1.png)<!--notebook-skip-line-->
 
 
 __*Watch out!*__ `size` should be (width, height).
@@ -114,7 +111,7 @@ assert example_image.shape == (427, 640, 3)
 assert aug_image.shape == (50, 100, 3)
 ```
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_19_1.png)
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_19_1.png)<!--notebook-skip-line-->
 
 
 ### Horizontal Flip 
@@ -133,7 +130,7 @@ assert example_image.shape == (427, 640, 3)
 assert aug_image.shape == (427, 640, 3)
 ```
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_22_1.png)
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_22_1.png)<!--notebook-skip-line-->
 
 
 You can get a random vertical flip too using 
[`mxnet.NDArray.swapaxes`](https://mxnet.incubator.apache.org/api/python/ndarray/ndarray.html?highlight=swapaxes#mxnet.ndarray.NDArray.swapaxes)
 (to switch height and width) before and after the random horizontal flip. Once 
again `p` will be the probability of a flip occurring, and is set to 1 for 
demonstration purposes.
@@ -146,7 +143,7 @@ plot_mx_array(aug_image)
 ```
 
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_24_0.png)
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_24_0.png)<!--notebook-skip-line-->
 
 
 # Color Augmentation
@@ -172,7 +169,7 @@ plot_mx_array(aug_image)
 ```
 
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_29_0.png)
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_29_0.png)<!--notebook-skip-line-->
 
 
 ### Contrast
@@ -195,7 +192,7 @@ plot_mx_array(aug_image)
 ```
 
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_32_0.png)
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_32_0.png)
 <!--notebook-skip-line-->
 
 
 ### Saturation 
@@ -211,7 +208,7 @@ plot_mx_array(aug_image)
 ```
 
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_35_0.png)
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_35_0.png)<!--notebook-skip-line-->
 
 
 ### Hue
@@ -227,7 +224,7 @@ plot_mx_array(aug_image)
 ```
 
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_38_0.png)
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_38_0.png)<!--notebook-skip-line-->
 
 
 ### LightingAug
@@ -247,7 +244,7 @@ plot_mx_array(aug_image)
 ```
 
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_41_0.png)
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_41_0.png)<!--notebook-skip-line-->
 
 
 ### Color Normalization 
@@ -276,7 +273,7 @@ plot_mx_array(aug_image)
 ```
 
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_46_0.png)
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_46_0.png)<!--notebook-skip-line-->
 
 
 ###  Grayscale
@@ -294,7 +291,7 @@ plot_mx_array(aug_image)
 ```
 
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_49_0.png)
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_49_0.png)<!--notebook-skip-line-->
 
 
 # Combinations 
@@ -313,7 +310,7 @@ assert aug_image.shape == (100, 100, 3)
 ```
 
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_52_0.png)
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_52_0.png)<!--notebook-skip-line-->
 
 
 
@@ -326,7 +323,7 @@ plot_mx_array(aug_image)
 ```
 
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_53_0.png)
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_53_0.png)<!--notebook-skip-line-->
 
 
 And lastly, you can use 
[`mxnet.image.RandomOrderAug`](https://mxnet.incubator.apache.org/api/python/image.html#mxnet.image.RandomOrderAug)
 to apply multiple augmenters to an image, in a random order.
@@ -335,7 +332,7 @@ And lastly, you can use 
[`mxnet.image.RandomOrderAug`](https://mxnet.incubator.a
 ```python
 example_image_copy = example_image.copy()
 aug_list = [
-    mx.image.RandomCropAug(size=(50, 50)),
+    mx.image.RandomCropAug(size=(250, 250)),
     mx.image.HorizontalFlipAug(p=1),
     mx.image.BrightnessJitterAug(brightness=1),
     mx.image.HueJitterAug(hue=0.5)
@@ -344,11 +341,11 @@ aug = mx.image.RandomOrderAug(aug_list)
 aug_image = aug(example_image_copy)
 plot_mx_array(aug_image)
 
-assert aug_image.shape == (50, 50, 3)
+assert aug_image.shape == (250, 250, 3)
 ```
 
 
-![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_55_0.png)
+![png](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/doc/tutorials/data_aug/outputs/types_of/output_55_0.png)<!--notebook-skip-line-->
 
 # FAQs
 
@@ -377,3 +374,5 @@ Most of the augmenters contain a mixture of control logic 
and `NDArray` operatio
 #### 4) Can I implement custom augmentations?
 
 Yes, you can implement your own class that inherits from 
[`Augmenter`](https://mxnet.incubator.apache.org/api/python/image/image.html?highlight=augmenter#mxnet.image.Augmenter)
 and define the augmentation steps in the `__call__` method. You can also 
implement a `dumps` method which returns a string representation of the 
augmenter and its parameters: it's used when inspecting a list of `Augmenter`s.
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
diff --git a/docs/tutorials/speech_recognition/ctc.md 
b/docs/tutorials/speech_recognition/ctc.md
index 9c9a9c9..0b01fb4 100644
--- a/docs/tutorials/speech_recognition/ctc.md
+++ b/docs/tutorials/speech_recognition/ctc.md
@@ -1,5 +1,14 @@
 # Connectionist Temporal Classification
 
+```python
+
+import mxnet as mx
+print(mx.__version__)
+```
+
+`1.1.0`<!--notebook-skip-line-->
+
+
 [Connectionist Temporal 
Classification](https://www.cs.toronto.edu/~graves/icml_2006.pdf) (CTC) is a 
cost function that is used to train Recurrent Neural Networks (RNNs) to label 
unsegmented input sequence data in supervised learning. For example, in a 
speech recognition application, using a typical cross-entropy loss, the input 
signal needs to be segmented into words or sub-words. However, using CTC-loss, 
it suffices to provide one label sequence for input sequence and the network 
learn [...]
 
 ## CTC-loss in MXNet
@@ -13,3 +22,5 @@ MXNet's example folder contains a [CTC 
example](https://github.com/apache/incuba
 
 ## Next Steps
 * [MXNet tutorials index](http://mxnet.io/tutorials/index.html)
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
diff --git a/docs/tutorials/unsupervised_learning/auto_encoders.md 
b/docs/tutorials/unsupervised_learning/auto_encoders.md
deleted file mode 100644
index 7dfefac..0000000
--- a/docs/tutorials/unsupervised_learning/auto_encoders.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# Autoencoders
-Get the source code for an example of autoencoders running on MXNet on GitHub 
in the 
[autoencoder](https://github.com/dmlc/mxnet/tree/master/example/autoencoder) 
folder.
-
-## Next Steps
-* [MXNet tutorials index](http://mxnet.io/tutorials/index.html)
\ No newline at end of file
diff --git a/docs/tutorials/unsupervised_learning/gan.md 
b/docs/tutorials/unsupervised_learning/gan.md
index c7b879c..1c99a02 100644
--- a/docs/tutorials/unsupervised_learning/gan.md
+++ b/docs/tutorials/unsupervised_learning/gan.md
@@ -212,9 +212,8 @@ So far we have defined a MXNet Symbol for both the 
Generator and the Discriminat
 sigma = 0.02
 lr = 0.0002
 beta1 = 0.5
-# If you do not have a GPU. Use the below outlined
-# ctx = mx.cpu()
-ctx = mx.gpu(0)
+# Define the compute context, use GPU if available
+ctx = mx.gpu() if mx.test_utils.list_gpus() else mx.cpu()
 
 #=============Generator Module=============
 generator = mx.mod.Module(symbol=generatorSymbol, data_names=('rand',), 
label_names=None, context=ctx)
@@ -387,3 +386,5 @@ Along the way, we have learned how to do the image 
manipulation and visualizatio
 ## Acknowledgements
 This tutorial is based on [MXNet DCGAN 
codebase](https://github.com/apache/incubator-mxnet/blob/master/example/gluon/dcgan.py),
 [The original paper on GANs](https://arxiv.org/abs/1406.2661), as well as 
[this paper on deep convolutional GANs](https://arxiv.org/abs/1511.06434).
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
diff --git a/docs/tutorials/vision/large_scale_classification.md 
b/docs/tutorials/vision/large_scale_classification.md
index 089f55b..4f40598 100644
--- a/docs/tutorials/vision/large_scale_classification.md
+++ b/docs/tutorials/vision/large_scale_classification.md
@@ -11,6 +11,14 @@ Training a neural network with a large number of images 
presents several challen
 $ pip install opencv-python
 ```
 
+```python
+import mxnet as mx
+print(mx.__version__)
+```
+
+`1.1.0`<!--notebook-skip-line-->
+
+
 ## Preprocessing
 
 ### Disk space
@@ -253,3 +261,5 @@ It is often straightforward to achieve a reasonable 
validation accuracy, but ach
 If the batch size is too big, it can exhaust GPU memory. If this happens, 
you’ll see the error message “cudaMalloc failed: out of memory” or something 
similar. There are a couple of ways to fix this:
 - Reduce the batch size.
 - Set the environment variable `MXNET_BACKWARD_DO_MIRROR` to 1. It reduces the 
memory consumption by trading off speed. For example, with batch size 64, 
inception-v3 uses 10G memory and trains 30 image/sec on a single K80 GPU. When 
mirroring is enabled, with 10G GPU memory consumption, we can run inception-v3 
using batch size of 128. The cost is that, the speed reduces to 27 images/sec.
+
+<!-- INSERT SOURCE DOWNLOAD BUTTONS -->
\ No newline at end of file
diff --git a/tests/nightly/test_tutorial_config.txt 
b/tests/nightly/test_tutorial_config.txt
index e88074f..be249b0 100644
--- a/tests/nightly/test_tutorial_config.txt
+++ b/tests/nightly/test_tutorial_config.txt
@@ -1,20 +1,31 @@
 basic/ndarray
+basic/ndarray_indexing
 basic/symbol
 basic/module
 basic/data
-python/linear-regression
-python/mnist
-python/predict_image
-onnx/super_resolution
-onnx/fine_tuning_gluon
-onnx/inference_on_onnx_model
-basic/ndarray_indexing
-python/matrix_factorization
+gluon/customop
+gluon/data_augmentation
+gluon/datasets
 gluon/ndarray
 gluon/mnist
 gluon/autograd
 gluon/gluon
 gluon/hybrid
+nlp/cnn
+onnx/super_resolution
+onnx/fine_tuning_gluon
+onnx/inference_on_onnx_model
+python/matrix_factorization
+python/linear-regression
+python/mnist
+python/predict_image
+python/data_augmentation
+python/data_augmentation_with_masks
+python/kvstore
+python/types_of_data_augmentation
 sparse/row_sparse
 sparse/csr
-sparse/train
\ No newline at end of file
+sparse/train
+speech_recognition/ctc
+unsupervised_learning/gan
+vision/large_scale_classification

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