877325778 / yomo-wasmedge-tensorflow

This application demonstrates how to launch high-performance "serverless" functions from the YoMo framework to process streaming data. The functions are embedded in a WebAssembly VM, WasmEdge, for safety, security, portability, and manageability.

Streaming Image Recognition by WebAssembly

This project demonstrates how to process a video stream in real-time using WebAssembly and apply a pre-trained food classification model to each frame of the video in order to determine if food is present in that frame, all by integrating WasmEdge into YoMo serverless.

Open-source projects that we used:

• Serverless stream processing framework YoMo
• Integrate with WasmEdge to introduce WebAssembly, interop TensorFlow Lite model
• A deep learning model found on TensorFlow Hub; make sure to download TFLite (aiy/vision/classifier/food_V1), which was created by Google

Advantages:

• ⚡️ Low-latency: Streaming data processing applications can now be done in far edge data centers thanks to YoMo's highly efficient network services
• 🔐 Security: WasmEdge runs the data processing function in a WebAssembly sandbox for isolation, safety, and hot deployment
• 🚀 High Performance: Compared with popular containers, such as Docker, WasmEdge can be up to 100x faster at startup and have a much smaller footprint
• 🎯 Edge Devices: As WasmEdge consumes much less resources than Docker, it is now possible to run data processing applications on edge devices

Steps to run

1. Clone This Repository

$ git clone https://github.com/yomorun/yomo-wasmedge-tensorflow.git

2. Install YoMo CLI

$ go install github.com/yomorun/cli/yomo@latest
$ yomo version
YoMo CLI version: v0.1.3

Or, you can download the pre-built binary tarball yomo-v0.1.3-x86_64-linux.tar.gz.

Details about YoMo CLI installation can be found here.

3. Install WasmEdge Dependencies

Install WasmEdge with its Tensorflow and image processing extensions

wget -qO- https://raw.githubusercontent.com/WasmEdge/WasmEdge/master/utils/install.sh | bash -s -- -e all -p /usr/local

If you have any questions about installation, please refer to the official documentation. Currently, this project works on Linux machines only.

Install video and image processing dependencies

$ sudo apt-get update
$ sudo apt-get install -y ffmpeg libjpeg-dev libpng-dev

4. Write your Streaming Serverless function

Write app.go to integrate WasmEdge-tensorflow:

Get WasmEdge-go:

$ cd flow
$ go get -u github.com/second-state/WasmEdge-go/wasmedge

Download pre-trained TensorflowLitee model: rust_mobilenet_food_lib_bg.so, store to flow directory:

$ wget -P flow 'https://github.com/yomorun/yomo-wasmedge-tensorflow/releases/download/v0.2.0/rust_mobilenet_food_lib_bg.so'

5. Run YoMo Orchestrator Server

  $ yomo serve -c ./zipper/workflow.yaml

6. Run Streaming Serverless function

$ cd flow
$ go run --tags "tensorflow image" app.go

7. Demonstrate video stream

Download this demo video: hot-dog.mp4, store to source directory, then run：

$ wget -P source 'https://github.com/yomorun/yomo-wasmedge-tensorflow/releases/download/v0.2.0/hot-dog.mp4'
$ go run ./source/main.go ./source/hot-dog.mp4

8. Result