Edge computing in practice using IoT, Tensorflow and Google Cloud
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Overview about edge computing concepts, how to get started and build a project using those concepts.
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Edge computing in practice using IoT, Tensorflow and Google Cloud
- 1. EDGE COMPUTING IN PRACTICE USING IOT, TENSORFLOW AND GOOGLE CLOUD Alvaro Viebrantz Google Developer Expert for IoT and Product Engineer at Leverege aviebrantz.com @alvaroviebrantz
- 2. What will we cover ? What is Edge Computing ? Overview of a practical scenario. Show how to build it!
- 3. 3
- 4. Availability Scalability Maintenance Cost Savings 4
- 5. BUT WHAT IS EDGE COMPUTING ?
- 6. WHEN DOES IT MAKES SENSE ?
- 7. Privacy
- 9. Latency
- 10. Reliability
- 11. Costs
- 12. TALK IS CHEAP LET’S SEE IN PRACTICE
- 13. USING CAMERAS AS SENSORS
- 15. 15 Camera as sensors Our Edge Computing project • Use cheap Wifi Cameras • Local server receiving and processing images • Object detection using Machine Learning • Send processed data to the cloud • Provide a local and remote web interface to show the data
- 17. HARDWARE
- 18. 18 ESP32 Cam Raspberry Pi 3 10$ 35$
- 19. PROVIDE IMAGES OVER THE LOCAL NETWORK
- 20. 20 Camera Firmware github.com/alvarowolfx/gcloud-iot-edge-tensorflow/tree/master/esp32-camera-firmware • Connect to Wifi Network • Provide HTTP endpoint to acquire images • Read image from local camera (OV2640)
- 21. 21 Camera Discovery with mDNS Local Discovery Protocol • Find local cameras automatically on the local network {INSTANCE_NAME}.local/jpg • Service _camera and protocol _tcp
- 22. 22 Finding local cameras Testing it by searching locally using dns-sd cli • http://indoor-camera-ec5d.local/jpg • http://indoor-camera-60d8.local/jpg
- 23. 23
- 25. FETCH IMAGES, CLASSIFY AND SEND TO THE CLOUD
- 26. 26 ANY LANGUAGE CAN BE USED HERE
- 27. 27
- 28. DISCLAIMER I’M NOT A MACHINE LEARNING EXPERT
- 29. BUT MAYBE THIS IS BEST THING ABOUT THIS WHOLE PROJECT
- 30. tensorflow.org/js/models Pre-trained TensorFlow.js models that can be used in any project out of the box.
- 31. Classification Mobilenet model Object Detection CocoSSD model Cat, Dog, DuckCat Classification vs Object Detection
- 32. 32
- 35. DEMO - EDGE SERVER
- 36. !36
- 37. !37
- 38. !38 My Computer Raspberry Pi 3 TensorFlow.js Core 8 seconds per frame 45 seconds per frame TensorFlow.js Node 200 milliseconds per frame 1 second per frame Performance Use tfjs-node and tfjs-node-gpu if possible
- 39. CLOUD SETUP
- 45. INGESTING DATA
- 48. 48
- 49. STORING DATA
- 51. 51
- 52. 52 firebase deploy 🔥 🔥 🔥
- 53. !53
- 54. !54
- 55. !55
- 56. Article describing how to build this project bit.ly/gcloud-iot-edge
- 58. I love cats What if I could track my cats at home ?
- 59. Train custom object detection model First approach
- 60. Transfer Learning Using pre-trained models to train a custom model with smaller dataset
- 61. Building the dataset Usually the hardest part on any ML project
- 62. LOW ACCURACY ON MY MODEL 😭😿
- 63. Train a classification model Using the same dataset
- 64. Train a classification model Using the same dataset
- 65. Best of both worlds Use the pre-trained object detection model first them use the my custom classifier Detect Objects using CocoSSD Filter for cats and crop boxes Classify using custom model.
- 66. DEMO - CUSTOM MODEL
- 68. !68 Found a cat Found Jam
- 69. !69 Found a cat Found Berry
- 70. !70 Found two catsFound Jam Found Muffin Found Berry
- 71. FUTURE WORK
- 72. Edge Devices Better performance on the edge
- 73. Other Edge Devices Better performance on the edge Sipeed Maix Bit Sparkfun Edge
- 74. Face recognition Identify allowed person on a given place github.com/ageitgey/face_recognition
- 75. Pose estimation Posenet pre-trained model
- 76. WRAPPING UP
- 77. Wrapping up !77 Use device power on the edge Extract more info using Machine Learning Scalability, Flexibility and Ease of Usage Using both local and cloud computing
- 78. THANK YOU! AVIEBRANTZ.COM Alvaro Viebrantz Google Developer Expert for IoT and Product Engineer at Leverege aviebrantz.com @alvaroviebrantz