In this workshop, learn how to build and deploy computer vision models using the AWS DeepLens deep learning-enabled video camera. Learn to build a machine learning model from scratch using Amazon SageMaker, and get hands-on experience with AWS DeepLens by extending that model to build an end-to-end AI application using Amazon Rekognition. Attendees also learn about use cases built by the community which integrate other AWS services and extend the functionality of AWS DeepLens.

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Mahendra Bairagi
Specialist Solutions Architect AI / ML
BDA201
AWS DeepLens Workshop: Building
Computer Vision Applications

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AWS DeepLens
is not a
video camera …
It’s the
world’s first
deep learning-enabled
developer kit

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Artificial intelligence at Amazon

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PLATFORM SERVICES
APPLICATION SERVICES
FRAMEWORKS & INTERFACES
Caffe2 CNTK
Apache
MXNet
PyTorch TensorFlow Chainer Keras Gluon
AWS Deep Learning AMIs
Amazon SageMaker
Amazon
Rekognition
Amazon
Transcribe
Amazon
Translate
Amazon Polly
Amazon
Comprehend
Amazon Lex
AWS
DeepLens
EDUCATION
The Amazon machine learning stack

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Get started with sample projects
ARTISTIC STYLE TRANSFER
OBJECT DETECTION FACE DETECTIONHOT DOG / NOT HOT DOG
CAT vs. DOG
ACTIVITY DETECTION
O r b u ild cu stom d eep learn in g mod els in th e c lou d u sin g
Amazon SageMaker
HEAD POSE DETECTION

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AWS DeepLens specifications
• Intel Atom processor
• Gen9 graphics
• Ubuntu OS- 16.04 LTS
• 100 GFLOPS performance
• Dualband Wi-Fi
• 8 GB RAM
• 16 GB storage (eMMC)
• 32 GB SD card
• 4 MP camera with MJPEG
• H.264 encoding at 1080p resolution
• 2 USB ports
• Micro HDMI
• Audio out
• AWS Greengrass preconfigured
• Intel clDNN optimized for MXNet

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3. Deploying a model to
AWS DeepLens
1. Machine learning
overview
4. Extending a
project
Today we will cover:
2. Training a model in
Amazon SageMaker
Amazon RekognitionAmazon S3
AWS Lambda

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1. Machine learning overview

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Model training Inference
Overview of deep learning
Data

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Data
Annotate Preprocess Data split

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Model training
• Define model architecture
• Input the annotated and cleaned data into the model
• Multiple iterations (epochs) to train the model
• Validate with held-back dataset
Large, annotated
dataset
Training set
Validation set
Training
Validate

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Inference
It’s where the magic happens!
1. Preprocess the new data or image just like a training set.
2. Feed image back to the trained model to get a predicted output.

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2. Training a model in Amazon SageMaker

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Lab #1: Training a model in Amazon SageMaker
• Objective: Learn how to build and train a face recognition model
• Time: 40 min.
• Steps:
1. About Amazon
SageMaker
2. Self-paced lab

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Amazon SageMaker
Fully managed
hosting with Auto
Scaling
One-click
deployment
Pre-built
notebooks for
common
problems
Built-in, high-
performance
algorithms
Hyperparameter
optimization
Build Train Deploy
One-click
training

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Lab details – Amazon SageMaker
The self-paced lab includes the following steps:
1. Import a prepared Jupyter Notebook into Amazon SageMaker.
2. The notebook walks you through building a face recognition model in
Amazon SageMaker.
3. Create an Amazon S3 bucket, and export the updated model there.

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Self-paced Lab – #1
1. Turn on AWS DeepLens. If it is turned on, access monitor and open Firefox.
2. Find the instruction manual here: https://github.com/fibbonnaci/DeepLens-
workshops
3. Access Hands-on Lab 1 tutorial.
40 minutes

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Important note!
Please ensure you stop the Amazon SageMaker notebook instance to avoid ongoing
charges to your AWS account.

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3. Deploying a model to AWS
DeepLens

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Under the covers: Console

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Under the covers: Device

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Lab #2: Deploying a model to AWS DeepLens
• Objective: Learn how to configure AWS DeepLens and deploy a model
• Time: 40 min.
• Steps:
1. Register and configure
AWS DeepLens
2. Deploy a model

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Follow along instructions
1. Find the instructions manual here: https://github.com/fibbonnaci/DeepLens-
workshops
2. Access Hands-on Lab 2: Register your AWS DeepLens device and deploy to
device

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Please note:
Keep your laptops closed!
We’ll use the AWS DeepLens
connected to the Ethernet and the
workshop monitor / keyboard /
pointer set up for device registration,
not your laptops.

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Register AWS DeepLens
1. Choose Register device.

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2. Provide a name for your device, for example, yourname-sf-AIDC.
3. Choose Next.
Register AWS DeepLens

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Set permissions
1. Choose Create roles.

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Set permissions
2. Choose Next.

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Download certificate
1. Choose Download certificate.
2. Choose Finish.

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Configure AWS DeepLens
1. Find the reset pin in the back of the AWS DeepLens device.
2. Use the provided pin to reset the device. You should hear a click.
3. The middle LED (Wi-Fi) will be blinking.
Before moving ahead …

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Run this step before moving ahead
sudo systemctl restart softap.service
1. Open Terminal, and run this command:

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Configure AWS DeepLens
1. Find the reset pin in the back of the AWS DeepLens device.
2. Use the provided pin to reset the device. You should hear a click.
3. The middle LED (Wi-Fi) will be blinking.
4. Choose complete the setup.

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First-time device registration
• You will see the updates available screen.
• Choose Install and Reboot. It will take 5 minutes for the updates to come through.
• After the updates are installed, the device will reboot automatically; you should
see the monitor go black and reboot the Linux kernel.
Note: This may take 5 minutes! Please be patient.

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First-time device registration (con’t.)
• After the reboot, open up your browser, the device will come back to the install and
reboot screen.
• Click Install and Reboot one more time and wait for your device to reboot.
• After the second reboot, open up the web browser again and remove
"#softwareUpdates" from the URL. You should now be on the attach/upload certificate
screen.

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5. For Network connection, choose Edit.
Configure AWS DeepLens

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6. Choose Use Ethernet.
Configure AWS DeepLens

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7. For Certificate, choose Edit.
Configure AWS DeepLens

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8. Upload the .zip file you downloaded during registration.
9. Choose Next.
Configure AWS DeepLens

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10. Download Streaming certificate.
Configure AWS DeepLens

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Important: If asked for a password, provide Aws2017! as the device password.
Choose Review.
Configure AWS DeepLens

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11. Choose Finish.
Configure AWS DeepLens

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Verify registration
1. Log in to the AWS DeepLens console.
https://console.aws.amazon.com/deepl
ens
This is the AWS DeepLens console.
2. Select Devices.

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Verify registration
3. You should see a green check mark and Registered under Registration
status.

44. Run this step before moving ahead
sudo systemctl restart greengrassd.service --no-block
1. Open Terminal, and run this command:

45. Now, it’s time to create a project
1. From the left navigation bar, choose
Projects.

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Now, it’s time to create a project
2. Choose Create new project.

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Use a face detection sample
3. Select Use a project template.
4. Select Face detection from
sample project templates.
5. Choose Next at the bottom of
screen.

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Create a project
6. Choose Create.
It will take a few minutes to
create the project.

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Deploy project to the device
7. Find your project in the
list (the one you just
named).
8. Choose the radio
button.
9. Choose Deploy to
device.

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10. Select your device.
11. Choose Review.
Target your device

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Deploy!
12. Choose Deploy.
A note on costs …

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Wait for the project to be deployed
Blue banner = Deployment in progress
Green banner = Deployment successful

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Let’s view the output
You can view the output over the terminal or in the browser. For this
workshop, we will view the output over the terminal.
1. Open Terminal on Ubuntu desktop (on the desktop, choose the
top left button and search for terminal).
2. Enter the following command:
mplayer -demuxer lavf -lavfdopts format=mjpeg:probesize=32 /tmp/results.mjpeg

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4. Extending a project:
Audience response tracking with AWS

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Winners of the DeepLens Hackathon
First place Second place Third place
ReadToMe
Created by Alex Schultz
ReadToMe is a deep learning-
enabled application that is able to
read books to kids. In this case,
reading Green Eggs and Ham, by
Dr. Seuss.
Dee
Created by Matthew Clark
Dee is a fun AWS DeepLens
interactive device for children. The
device asks children to answer
questions by showing a picture of
the answer.
SafeHaven
Created by Nathan Stone and
Peter McLean
SafeHaven uses Alexa and AWS
DeepLens to bring peace of
mind for vulnerable people and
their families.
View all 23 projects at: https://aws.amazon.com/deeplens/community-projects

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Amazon
Rekognition
image
Inference
Lambda
Amazon
S3 Bucket
Amazon
DynamoDB
Amazon
SageMaker
&
AWS DeepLens
console
Amazon
S3 Bucket
Recognize
emotions
Lambda
Training / validation data
Cropped face images
Cropped face
images
Detected
emotions
AWS DeepLens
Cloud
Amazon
CloudWatch
Detected
emotions

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Now, let’s see it in action.

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Are you ready for a challenge?
AWS DeepLens Challenge
• What is it: Eight themed challenges that will each run for two weeks, you have the opportunity to combine your ideas with the capability of AWS
DeepLens to create machine learning projects that can have a positive impact on the world.
• Why: These challenges will help you gain valuable machine learning experience within a fun, collaborative, and inspiring environment. You’ll be
making a positive impact on improving people’s lives and supporting non-profits that benefit our society.
• When: Challenges started on July 10 and will run though end of October 2018.
• Learn more: https://aws.amazon.com/deeplens/challenge/
B u i l d m a c h i n e l e a r n i n g p r o j e c t s u s i n g A W S D e e p L e n s a n d m a k e a d i f f e r e n c e !

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Questions?

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Thanks & wrap-up
Order on Amazon.com
Search: AWS DeepLens
See what others have built
aws.amazon.com/deeplens/community-projects
Request a workshop
Work with your AWS account
management team to request a
hands-on Amazon SageMaker &
AWS DeepLens workshop

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Submit session feedback
1. Tap the Schedule icon.
2. Select the session you
attended.
3. Tap Session Evaluation to
submit your feedback.

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Thank you!