Here at Dell EMC we just announced the general availability of the Dell EMC Ready Solutions for AI - Machine Learning with Hadoop design so I decided to write a somewhat technical article highlighting the power of doing deep learning with the Intel BigDL framework executing on Apache Spark. After a short introduction covering the components of our Hadoop design, I will walk through an example of training an image classification neural network using BigDL.  Finally I show how that example can be extended by training the model to classify a new category of images – emojis.


The combination of Apache Spark and Intel BigDL creates a powerful data science environment by enabling the compute and data intensive operations associated with creating AI to be performed on the same cluster hosting your Hadoop Data Lake and existing analytics workloads. YARN will make sure everything gets their share of the resources.  The Dell EMC Ready Solution also brings Cloudera Data Science Workbench (CDSW) to your Hadoop environment. CDSW enables secure, collaborative access to your Hadoop environment. Data Scientists can choose from a library of published engines that are preconfigured, saving time and frustration dealing with library and framework version control, interoperability etc. Dell EMC includes an engine configured with Intel BigDL and other common Python libraries.

Getting Started

To follow along with the steps in this blog post you can use the docker container included in the BigDL github repository. Installing docker is beyond the scope of this post, however you can find directions to install docker for various operating systems here (link to On my Dell XPS 9370 running Ubuntu 16.04 I use the following commands to get started:

cd ~
git clone
cd BigDL/docker/BigDL
sudo docker build --build-arg BIGDL_VERSION=0.5.0 -t bigdl/bigdl:default .
sudo docker run -it --rm -p 8080:8080 -e NotebookPort=8080 \
-e NotebookToken=”thisistoken” bigdl/bigdl:default

Tip: Notice the space + period that is at the end of our build command. That tells docker to look for our Dockerfile in the current directory.

You should now be able to point a web browser at localhost:8080 and enter the token (“thisistoken” without “” in example). You will be presented with a Jupyter notebook showing several examples of Apache Spark and Intel BigDL operations. For this post we are going to look at the neural_networks/introduction_to_mnist.ipynb notebook. You can launch the notebook by clicking the neural_networks folder and then the introduction_to_mnist.ipynb file.

This example covers the get_mnist function that is used in many of the example notebooks. By going through this we can get a view in to how easy it is to deal with the huge datasets that are necessary for training a neural network. In the get_mnist function we read our dataset and create an RDD of all of the images, sc.parallelize(train_images) loads 60,000 images in to memory ready for distributed, parallel processing to be performed on them. With Hadoop's native Yarn resource scheduler we can decide to do these operations with 1 executor/2 cpu cores or 32 executors with 16 cores each.

Tip: The resource sizing of your Spark executors should be such that executors * cores per executor * n is equal to your batch size.

This example notebook reads the images using a helper function written by the BigDL team for the mnist dataset, mnist.read_data_sets. BigDL provides us the ImageFrame API to read image files. We can read individual files or folders of images. In a clustered environment we create distributed image frames which is an RDD of ImageFeature. ImageFeature represents one individual image, storing the image as well as label and other properties using key/value.

At the end of the introduction_to_mnist notebook we see that samples are created by zipping up the images RDD with the labels RDD. We perform a map of Sample.from_ndarray to end up with an RDD of samples. A sample consists of one or more tensors representing the image and one or more tensors representing the label. This sample of tensors are the structure we need to feed our data in to a neural network.

We can close this introduct_to_mnist notebook now and we will take a look at the cnn.ipynb notebook. The notebook does a good job of explaining what is going on at each step. We use our previously covered mnist helper functions to read the data, define a LeNet model, define our optimizer and train the model. I would encourage you to go through this example once and learn what is going on. After that, go ahead and click Kernel > Restart & Clear Output.

Now that we are back to where we started, let’s look at how we can add a new class to our model. Currently the model is being trained to predict 10 classes of numbers. What if, for example, I wanted the model to be able to recognize emojis also? It is actually really simple, and if you follow along we will go through a quick example of how to train this model to also recognize emoji for a total of 11 classes to categorize.

The Example

The first step is going to be to acquire a dataset of emojis and prepare them. Add a cell after cell [2]. In this cell we will put

git clone
pip install opencv-python


Then we can insert another cell and put:

import glob
from os import listdir
from os.path import isfile, join
from import *
import cv2

path = 'emoji-dataset/emoji_imgs_V5/'
images = [cv2.imread(file) for file in glob.glob("emoji-dataset/emoji_imgs_V5/*.png")]
emoji_train = images[1:1500]
emoji_test = images[1501:]
emoji_train_rdd = sc.parallelize(emoji_train)
emoji_test_rdd = sc.parallelize(emoji_test)

Here we are taking all of our emoji images and dividing them up, then turning those lists of numpy arrays in to RDDs. This isn’t the most efficient way to divide up our dataset but it will work for this example.

Currently these are color emoji images, and our model is defined for mnist which is grayscale. This means our model is set to accept images of shape 28,28,1.  RGB or BGR images will have a shape of 28,28,3. Since we have these images in an RDD though we can use cv2 to resize and grayscale the images. Go ahead and insert another cell to run:

emoji_train_rdd = x: (cv2.resize(x, (28, 28))))
emoji_train_rdd = x: (cv2.cvtColor(x, cv2.COLOR_BGR2GRAY)))
emoji_test_rdd = x: (cv2.resize(x, (28, 28))))
emoji_test_rdd = x: (cv2.cvtColor(x, cv2.COLOR_BGR2GRAY)))
emoji_train_rdd = x: Sample.from_ndarray(x, np.array(11)))
emoji_test_rdd = x: Sample.from_ndarray(x, np.array(11)))
train_data = train_data.union(emoji_train_rdd)
test_data = test_data.union(emoji_test_rdd)

This could easily be reduced to a function, however if you want to see the transformations step by step you can just insert the below code between each line. For example we can see after the resize our image is shape (28, 28, 3) after the resize but before grayscale, and (28, 28, 1) after.

single = emoji_train_rdd.take(1)

Output is 28,28,1.

Then, in our def build_model cell we just want to change the last line so it is
lenet_model = build_model(11) . After this the layers are built to accept 11 classes.


We can see the rest of the results by running through the example. Since our emoji dataset is so small compared to the handwritten number images we will go ahead and create a smaller rdd to predict against to ensure we see our emoji samples after the optimizer is setup and the model is trained. Insert a cell after our first set of predictions and put the following in it:

sm_test_rdd = test_data.take(8)
sm_test_rdd = sc.parallelize(sm_test_rdd)
predictions = trained_model.predict(sm_test_rdd)
imshow(np.column_stack([np.array(s.features[0].to_ndarray()).reshape(28,28) for s in sm_test_rdd.take(11)]),cmap='gray'); plt.axis('off')
print 'Ground Truth labels:'
print ', '.join(str(map_groundtruth_label(s.label.to_ndarray())) for s in sm_test_rdd.take(11))
print 'Predicted labels:'
print ', '.join(str(map_predict_label(s)) for s in predictions.take(11))

We should see that our model has correctly associated the label of “10” to our emoji images and something in the range of 0-9 for the remaining images. Congratulations! We have now trained our lenet model to do more than just recognize examples of 0-9. If we had multiple individual emojis we wanted to recognize instead of just “emoji” we would simply need to provide more specific labels such as “smiling” or “laughing” to the emojis and then define our model with our new number of classes.

The original cnn.ipynb notebook trained a lenet model with 10 classes. If we compare the visualization of the weights in the convolution layers we can see that even adding just a few more images and a new class has changed these a lot.

Original Model

11 Class Model

Thanks everyone, I hope you enjoyed this blog post. You can find more information about the Dell EMC Ready Solutions for AI at this url: