In this workshop, developers have the opportunity to learn how to build and deploy computer vision models, such as face detection and object analysis using the AWS DeepLens deep learning enabled video camera. By working hands-on, developers of all skill levels can explore and build their own deep learning powered, computer-vision applications. Attendees can experiment with different sample projects for face detection, object recognition, neural style transfer, and other machine learning use cases using Apache MXNet.

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AWS re:INVENT
AWS DEEPLENS
B u i l d d e e p - l e a r n i n g p o w e r e d c o m p u t e r v i s i o n s k i l l s
November 29, 2017
Jyothi Nookula, Senior PTM, DeepCam
Sunil Mallya, Sr. AI Solutions Architect
Aran Khanna, AI Software Dev Engineer
MCL212

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JYOTHI NOOKULA SUNIL MALLYA
MEET THE TEAM
ARAN KHANNA

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AWS DEEPLENS
IS NOT A
VIDEO CAMERA…
…IT’S THE
WORLDS FIRST
DEEP LEARNING
ENABLED
DEVELOPER KIT

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GET STARTED WITH SAMPLE PROJECTS
ADD CUSTOM FUCTIONALITY
OR
CREATE YOUR OWN PROJECT
ARTISTIC STYLE
TRANSFER
OBJECT
DETECTION
FACE DETECTION /
RECOGNITION
HOT DOG / NOT
HOT DOG
CAT VS. DOG
ACTIVITY
DETECTION

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LAB #2: DEPOLYING
A PROJECT
LAB #1: ML OVERVIEW LAB #4:
EXTENDING A
PROJECT
TODAY WE WILL COVER
LAB #3: MODEL
EDITING

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LAB #1
M A C H I N E L E A R N I N G O V E R V I E W

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LAB #1: FOUNDATIONS OF ML
• Objective: Learn key elements of ML and how DeepLens works
• Time: 10 mins
• Steps:
1. Machine learning 2. About DeepLens

8. AI, ML, and DL
Hardware and
learning
algorithms Fit a function
to the data
Fit a network
structure to the data

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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 new data/image just like training set.
2. Feed image back to the trained model to get a predicted output.

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AWS DEEPLENS ARCHITECTURE
Video out
Data out
I N F E R E N C E
D E P L O Y P R O J E C T S
Manage device
Security
Console Project
Management
AWS Cloud
Intel: Model Optimizer
cIDNN and Driver

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DEEPLENS SPECIFICATIONS
• Intel Atom Processor
• Gen9 graphics
• Ubuntu OS- 16.04 LTS
• 100 GFLOPS performance
• Dual band 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
• clDNN Optimized for MXNet

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QUESTIONS?

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LAB #2
D E P L O Y I N G A P R O J E C T

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LAB #2: HOW TO DEPLOY A PROJECT
• Objective: Get started with a sample project
• Time: 30 mins
• STEPS:
1. FIND AND
SELECT A
PROJECT
2. CONFIGURE
3. DEPLOY &
TEST
4. HOW IT
WORKS

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FIVE MINS TO YOUR FIRST CV PROJECT
LET’S GET STARTED!
WAKE UP YOUR
AWS DEEPLENS
(wiggle the mouse)

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LOG IN
Password: Aws2017!

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ACCESS DEEPLENS CONSOLE
1. Open QuickLinks File from the desktop.
2. Click Quick Link #1 labeled DeepLens Console.
(console.aws.amazon.com/deeplens)

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LOGIN?
If the AWS DeepLens Console login was not saved …
1. Check the sticker on the side of your DeepLens.
2. Enter you account ID, username, and password.

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CREATE A PROJECT
• Log in to Console. Use the username
and password on the sticker on the
side of your device.
• This is the AWS DeepLens Console.
• Click the Create Project button.

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USE OBJECT DETECTION SAMPLE
1. Select Use a project
template.
2. Select Object Detection
from the sample project
templates.
3. Select Next at the bottom
of screen.

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NAME PROJECT
1. Edit project name
(e.g., Object-
detection-MikeM).
2. Optional description
(leave blank).
3. Click Create.

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DEPLOY PROJECT TO THE DEVICE
1. Find your project in
the list (the one you
just named).
2. Select the radio
button.
3. Click Deploy to
device.

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1. Choose your device.
2. Click Review.
TARGET YOUR DEVICE

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DEPLOY!
• Click Deploy.
• A note on costs …

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WAIT FOR PROJECT TO BE DEPLOYED
Blue banner = Deployment in
progress
Green banner = Deployment
successful

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WHILE WE WAIT…
• Cloud to device
• On the device

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UNDER THE COVERS - CONSOLE

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UNDER THE COVERS – DEVICE

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For the Workshop:
1. Open QuickLinks
(existing tab)
2. Cut & paste “mplayer”
string into Terminal (Live
or Project Stream)
Console has instructions:
VIEW OUTPUT

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QUESTIONS?

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LAB #3
M O D E L E D I T I N G

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LAB #3: MODEL EDITING
• Objective: You will learn how can use the existing object detection
model to create a hot dog detector using AWS SageMaker
• Time: 40 mins
• Steps:
1. ABOUT AWS
SAGEMAKER
2. SELF-PACED LAB

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End-to-end
machine learning
platform
Zero setup Flexible model
training
Pay by the
second
INTRODUCING AMAZON SAGEMAKER
The quickest and easiest way to get ML models from idea to production
$

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LAB DETAILS
The self-paced lab will include the following steps:
1. Import a prepared Jupyter Notebook into SageMaker.
2. The notebook walks through modifications to the squeezenet
object dection model, to turn it into a binary classifier.
3. Create an S3 bucket and export the updated model there.
4. Import model to a new DeepLens Project.
5. Deploy & test!
6. If time: Deploy the Sample Project ‘Hot dog recognition’.

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LAB #3 – SELF PACED
1. Use QuickLinks file (open in Firefox).
2. Click on the GitHub link for the Lab 3 instructions!
40 minutes

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TIME’S UP!
QUESTIONS?

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LAB #4
E X T E N D I N G A P R O J E C T V I A L A M B D A

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LAB #4: EXTENDING A PROJECT VIA LAMBDA
• Objective: Be able to extend the functionality of a project.
• Time: 15 mins
• Steps:
1. ABOUT LAMBDA
FUNCTIONS ON
DEEPLENS
2. SELF-PACED LAB

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ABOUT LAMBDA’S ON DEEPLENS

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LAB DETAILS
The self-paced lab includes the following steps, all performed in
the cloud.
Assumption: Default Hot dog recognition Project publishes model
results to an IoT Topic in the cloud via MQTT.
1. Create a Lambda function.
2. Create an IoT rule.
3. Integrate the IoT rule with AWS DeepLens.
4. Trigger the Lambda function when a hotdog is detected.

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LAB #4 – SELF PACED
1. Use QuickLinks file (open in Firefox).
2. Click the GitHub link for the Lab 4 instructions!
15 minutes

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TIME’S UP!
QUESTIONS?

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DREAM. LEARN. BUILD. WIN!
Announcing the AWS DeepLens Challenge Virtual Hackathon
Nov 29th – Jan 24th
$7,500 USD
2 tickets to re:Invent 2018
Special edition custom DeepLens
$5,000 USD
1 ticket to re:Invent 2018
Special edition custom DeepLens
$2,500 USD
Special edition custom DeepLens
PARTICIPANT PRIZE:
EVERY ELIGIBLE SUBMISSION GETS A CUSTOM SKIN FOR THEIR DEEPLENS WORTH $34.99 MSRP
SIGN UP NOW:
awsdeeplens.devpost.com

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WHAT WILL YOU BUILD WITH
DEEPCAM?

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THANK YOU!