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NEW LAUNCH! AWS DeepLens workshop: Building Computer Vision Applications - MCL212 - re:Invent 2017

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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.

NEW LAUNCH! AWS DeepLens workshop: Building Computer Vision Applications - MCL212 - re:Invent 2017

  1. 1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  2. 2. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. JYOTHI NOOKULA SUNIL MALLYA MEET THE TEAM ARAN KHANNA
  3. 3. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. AWS DEEPLENS IS NOT A VIDEO CAMERA… …IT’S THE WORLDS FIRST DEEP LEARNING ENABLED DEVELOPER KIT
  4. 4. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  5. 5. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. LAB #2: DEPOLYING A PROJECT LAB #1: ML OVERVIEW LAB #4: EXTENDING A PROJECT TODAY WE WILL COVER LAB #3: MODEL EDITING
  6. 6. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  7. 7. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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. 8. AI, ML, and DL Hardware and learning algorithms Fit a function to the data Fit a network structure to the data
  9. 9. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Model training Inference OVERVIEW OF DEEP LEARNING Data
  10. 10. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. DATA Annotate Preprocess Data split
  11. 11. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  12. 12. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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.
  13. 13. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  14. 14. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  15. 15. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. QUESTIONS?
  16. 16. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. LAB #2 D E P L O Y I N G A P R O J E C T
  17. 17. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  18. 18. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. FIVE MINS TO YOUR FIRST CV PROJECT LET’S GET STARTED! WAKE UP YOUR AWS DEEPLENS (wiggle the mouse)
  19. 19. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. LOG IN Password: Aws2017!
  20. 20. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. ACCESS DEEPLENS CONSOLE 1. Open QuickLinks File from the desktop. 2. Click Quick Link #1 labeled DeepLens Console. (console.aws.amazon.com/deeplens)
  21. 21. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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.
  22. 22. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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.
  23. 23. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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.
  24. 24. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. NAME PROJECT 1. Edit project name (e.g., Object- detection-MikeM). 2. Optional description (leave blank). 3. Click Create.
  25. 25. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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.
  26. 26. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 1. Choose your device. 2. Click Review. TARGET YOUR DEVICE
  27. 27. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. DEPLOY! • Click Deploy. • A note on costs …
  28. 28. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. WAIT FOR PROJECT TO BE DEPLOYED Blue banner = Deployment in progress Green banner = Deployment successful
  29. 29. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. WHILE WE WAIT… • Cloud to device • On the device
  30. 30. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. UNDER THE COVERS - CONSOLE
  31. 31. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. UNDER THE COVERS – DEVICE
  32. 32. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. For the Workshop: 1. Open QuickLinks (existing tab) 2. Cut & paste “mplayer” string into Terminal (Live or Project Stream) Console has instructions: VIEW OUTPUT
  33. 33. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. QUESTIONS?
  34. 34. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. LAB #3 M O D E L E D I T I N G
  35. 35. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  36. 36. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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 $
  37. 37. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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’.
  38. 38. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. LAB #3 – SELF PACED 1. Use QuickLinks file (open in Firefox). 2. Click on the GitHub link for the Lab 3 instructions! 40 minutes
  39. 39. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. TIME’S UP! QUESTIONS?
  40. 40. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  41. 41. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  42. 42. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. ABOUT LAMBDA’S ON DEEPLENS
  43. 43. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  44. 44. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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.
  45. 45. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. LAB #4 – SELF PACED 1. Use QuickLinks file (open in Firefox). 2. Click the GitHub link for the Lab 4 instructions! 15 minutes
  46. 46. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. TIME’S UP! QUESTIONS?
  47. 47. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. 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
  48. 48. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. WHAT WILL YOU BUILD WITH DEEPCAM?
  49. 49. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. THANK YOU!

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