MCL309_Deep Learning on a Raspberry Pi

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AWS re:INVENT
MCL309
Deep Learning on a Raspberry Pi
J u l i e n S i m o n , P r i n c i p a l A I / M L E v a n g e l i s t , E M E A
@ j u l s i m o n
M C L 3 0 9
N o v e m b e r 2 7 , 2 0 1 7

What to expect from this workshop
• Welcome and housekeeping—5 minutes
• An introduction to the Apache MXNet API—20 minutes (slides)
• Training an MXNet model—10 minutes
• Setting up your Raspberry Pi—20 minutes
• Using pre-trained models—30 minutes
• Building an app with MXNet, Amazon Rekognition, and Amazon Polly—60 minutes
• Wrap-up—5 minutes

Welcome!
Matthieu Fuzellier, Sr. Consultant IoT, Professional Services (US)
Stephan Hadinger, Senior Manager, Solution Architecture (France)
Adrian Hornsby, Technical Evangelist (Nordics)
Rudy Krol, Solutions Architect (France)
Troy Larson, Sr. DevOps Cloud Architect, Professional Services (US)
Nicolas Malaval, Consultant, Professional Services (France)

An introduction to the Apache MXNet API

Apache MXNet: Open source l i brary for d eep l earni ng
Programmable Portable High performance
Near linear scaling
across hundreds of GPUs
Highly efficient
models for mobile
and IoT
Simple syntax,
multiple languages
Most open Best On AWS
Optimized for
deep learning on AWS
Accepted into the
Apache Incubator
mxnet.incubator.apache.org

Neural networks
x =
x11, x12, …. x1I
x21, x22, …. x2I
x…, x.., …. x.I
xm1, xm2, …. xmI
I features
m samples
y =
y1
y2
y…
ym
m labels
Total number of predictions
Accuracy =
Number of correct predictions

Training
Training data set Training
Trained
neural network
Number of epochs
Batch size
Learning rate
Hyper parameters

Validation
Validation data set Trained
neural network
Validation
accuracy
Prediction at
the end of
each epoch
Stop training when validation accuracy stops increasing
Saving parameters at the end of each epoch is a good idea ;)

The Apache MXNet API
• Storing and accessing data  multi-dimensional arrays: NDArray API
• Neural network (layers, weights, activation functions)  Symbol API
• Serving data during training and validation  Iterators
• Data + neural network = model  Module API

Let’s build a data set of 1000 samples with 100 features each and belonging to one of 10 categories.
We split it 80/20 for training and validation.
sample_count = 1000
train_count = 800
valid_count = sample_count - train_count
feature_count = 100
category_count = 10
X = mx.nd.uniform(low=0, high=1, shape=(sample_count,feature_count))
Y = mx.nd.empty((sample_count,))
for i in range(0,sample_count-1):
Y[i] = np.random.randint(0,category_count)
X_train = mx.nd.crop(X, begin=(0,0), end=(train_count,feature_count))
Y_train = Y[0:train_count]
X_valid = mx.nd.crop(X, begin=(train_count,0), end=(sample_count,feature_count))
Y_valid = Y[train_count:sample_count]
print(X.shape, Y.shape, X_train.shape, Y_train.shape, X_valid.shape, Y_valid.shape)
NDArray API example
h t t p s : / / m x n e t . i n c u b a t o r . a p a c h e . o r g / a p i / p y t h o n / n d a r r a y . h t m l

Let’s build a simple Multi-Layer Perceptron
1. Input layer
2. One hidden layer with 64 neurons,
using the Rectified Linear Unit activation function
3. Output layer with 10 neurons,
using the Softmax function to output probabilities
data = mx.sym.Variable('data')
fc1 = mx.sym.FullyConnected(data, name='fc1', num_hidden=64)
relu1 = mx.sym.Activation(fc1, name='relu1', act_type="relu")
fc2 = mx.sym.FullyConnected(relu1, name='fc2', num_hidden=category_count)
out = mx.sym.SoftmaxOutput(fc2, name='softmax')
mod = mx.mod.Module(out)
Symbol API example
h t t p s : / / m x n e t . i n c u b a t o r . a p a c h e . o r g / a p i / p y t h o n / s y m b o l . h t m l

• Training is performed batch by batch
• Iterators take care of this automatically 
• MXNet provides iterators for NDArrays, image files, and more
• You can also write your own
batch = 10
train_iter = mx.io.NDArrayIter(data=X_train,label=Y_train,batch_size=batch)
val_iter = mx.io.NDArrayIter(data=X_valid,label=Y_valid, batch_size=batch)
Iterator API example
h t t p s : / / m x n e t . i n c u b a t o r . a p a c h e . o r g / a p i / p y t h o n / i o . h t m l

• This is where the rubber meets the road
• Binding the data set to the network
• Setting initialization and optimization parameters
• Training the model
• Saving the trained model
nb_epochs=50
mod = mx.mod.Module(out)
mod.bind(data_shapes=train_iter.provide_data, label_shapes=train_iter.provide_label)
mod.init_params(initializer=mx.init.Xavier(magnitude=2.))
mod.init_optimizer(optimizer='sgd', optimizer_params=(('learning_rate', 0.1), ))
mod.fit(train_iter, num_epoch=nb_epochs)
mod.save_checkpoint(”myFirstModel", nb_epochs)
Module API example
h t t p s : / / m x n e t . i n c u b a t o r . a p a c h e . o r g / a p i / p y t h o n / m o d u l e . h t m l

$ python introduction.py
((1000L, 100L), (1000L,), (800L, 100L), (800L,), (200L, 100L), (200L,))
INFO:root:Epoch[0] Train-accuracy=0.093750
INFO:root:Epoch[0] Time cost=0.051
. . .
INFO:root:Epoch[49] Train-accuracy=0.995000
INFO:root:Epoch[49] Time cost=0.047
Validation accuracy: 0.12
• The model is able to perfectly learn our synthetic training set
(universal approximation theorem)
• Validation accuracy is what you need to look at
Putting it all together
0 1 _ i n t r o d u c t i o n . p y

Training an MXNet model

The MNIST data set
• 70,000 hand-written digits
• 28x28 grayscale images

You have three options
1. Install on your own machine
• Linux, MacOS, Docker—no Windows
• https://mxnet.incubator.apache.org/get_started/install.html
2. Start an Amazon EC2 instance using the Deep Learning AMI in your own account
• CPU or GPU, it’s up to you
• Scripts are provided: ec2_setup.sh and ec2_cleanup.sh
• Read them first!
3. Use the shared Amazon EC2 instance
• Instructions will be provided
• PLEASE work in your own folder: mkdir ~/firstname-lastname
Setting up your MXNet machine

Training models on MNIST
0 2 _ m n i s t /
• Download the data set
• Either from the web: get_mnist_from_web.sh
• Or from S3: get_mnist_from_s3.sh
• Train two models
• A Multi-Layer Perceptron: trainMlp.py
• The LeNet Convolutional Neural Network: trainLeNet.py
• How does training differ?
• Use each model to predict new digits: predict.py
• How does prediction differ?

Setting up your Raspberry Pi

Setting up the hardware & network
• Instructions will be provided
• Ask us for help if you’re blocked
• Please be a good neighbor: help out other participants if you can 

Installing MXNet
0 3 _ p i S e t u p /
• Install a pre-built version of MXNet: setup.sh
• For reference only (>> 1 hour):
build from source: build.sh
• Detailed installation instructions:
https://mxnet.incubator.apache.org/get_started/install.html

Using pre-trained models

Training MNIST on the Pi
0 2 _ m n i s t /
• Download the data set
• Either from the web: get_mnist_from_web.sh
• Or from Amazon Simple Storage Service: get_mnist_from_s3.sh
• Train two models
• A Multi-Layer Perceptron: trainMlp.py
• The LeNet Convolutional Neural Network: trainLeNet.py
• What’s the problem?
• Obviously, you wouldn’t want to train CNNs on a Raspberry Pi 
• Even on a powerful system, training is not an easy task, which leads us to…

Why pre-trained models make sense
• Training a complex model on a large data set can take days
• Finding the right settings is not easy
• A pre-trained model may be good enough to solve your problem
• You can also fine-tune it on your own data to improve accuracy
• Take a look at the MXNET model zoo
• https://mxnet.incubator.apache.org/model_zoo/
• Two files: model definition (JSON), model weights (.params)

Using pre-trained models
0 4 _ p i M o d e l s /
• Download three pre-trained models for image classification
• Either from the web: get_models_from_web.sh
• Or from Amazon S3: get_models_from_s3.sh
• Read the code in classify.py. Make sure you understand how to:
• Load a model
• Load a single image from file and turn it into an NDArray
• Build a ”fake” iterator with namedtuple
• Collect results on the output layer and find the top classes
• Run classify.py
• What happens and why? Fix the problem
• Try your own images

B u i l d i n g a n a p p w i t h M X N e t , A m a z o n R e k o g n i t i o n
a n d A m a z o n P o l l y

Connecting and testing the camera
Power down the Pi first from picamera import PiCamera
cam = PiCamera()
cam.resolution=(640,480)
cam.capture(”myimage.jpg”)
cam.close()
Copy the image on your laptop
and check that it’s correct
If not, you probably need to flip
the cable 
Your setup should look like this

Using a pre-trained MXNet model
0 5 _ p i M x n e t /
1. Add code to classify a picture taken by the camera
2. Take pictures and classify them:
• Pictures with a single object
• Pictures with nested objects (for example, a car on a book cover)
• Pictures with multiple objects
• Pictures of people
• What works well and what doesn’t? Any idea why?

Adding Amazon Polly
0 6 _ p i P o l l y /
• Amazon Polly is a text-to-speech service
https://aws.amazon.com/polly
• You can generate a sound file from a text string with a single API call
https://docs.aws.amazon.com/polly/latest/dg/API_SynthesizeSpeech.html
• Try the AWS Command Line Interface first: “aws polly…”
• Using information returned by the MXNet model, we build a text string
saying “I’m <score>% sure this is a <top category name>”
• Play it with Amazon Polly
• Try different voices

Adding Amazon Rekognition
0 7 _ p i R e k o g n i t i o n /
• Amazon Rekognition is an object and face detection service
https://aws.amazon.com/rekognition
• You can detect objects or faces with a single API call
https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html
https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectFaces.html
• Try the AWS CLI first: “aws rekognition …”
• Add code to run Amazon Rekognition on a picture
• Use the output to build a text message and play it with Amazon Polly

Wrap-up

• If you created an instance, don’t forget to terminate it (ec2_cleanup.sh)
• You can keep the hardware  Build cool stuff with it and let us know.
We’re always happy to share and retweet.
• You also get $25 credits  Don’t forget them on your way out.
• Please check out the other deep learning sessions (MCL322 *hint* *hint*)
• Thank you very much. Have a GREAT re:Invent!

Resources
https://aws.amazon.com/ai
https://aws.amazon.com/blogs/ai
https://mxnet.io
https://github.com/apache/incubator-mxnet
https://github.com/gluon-api
https://github.com/awslabs/sockeye
https://medium.com/@julsimon

Thank you!
J u l i e n S i m o n , P r i n c i p a l A I / M L E v a n g e l i s t , E M E A
@ j u l s i m o n

MCL309_Deep Learning on a Raspberry Pi

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