Here is a demonstration of a video stream in real-time using WebAssembly to apply a pre-trained food classification model to each frame of the video in order to determine if food is present in that frame. This article takes you through the tutorial steps of integrating WasmEdge into YoMo serverless, whicn can then be deployed to different AWS regions by YoMo, providing fast connection by Geo-distributed architecture.

This article is originally published by Yomo.

Youtube: YoMo x WasmEdge

Advantages of YoMo deployment:

  • ⚡️ 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

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

Steps to run

1. Clone This Repository

$ git clone

2. Install YoMo CLI

$ go install
$ yomo version
YoMo CLI version: v0.0.5

Or, you can download the pre-built binary tarball yomo-v0.0.5-x86_64-linux.tgz.

Details about YoMo CLI installation can be found here.

3. Install WasmEdge Dependencies

Install WasmEdge

$ wget
$ tar -xzf WasmEdge-0.8.0-manylinux2014_x86_64.tar.gz
$ sudo cp WasmEdge-0.8.0-Linux/include/wasmedge.h /usr/local/include
$ sudo cp WasmEdge-0.8.0-Linux/lib64/ /usr/local/lib
$ sudo ldconfig

Or, you can build from the source.

Install WasmEdge-tensorflow

Install tensorflow dependencies for manylinux2014 platform

$ wget
$ wget
$ sudo tar -C /usr/local/lib -xzf WasmEdge-tensorflow-deps-TF-0.8.0-manylinux2014_x86_64.tar.gz
$ sudo tar -C /usr/local/lib -xzf WasmEdge-tensorflow-deps-TFLite-0.8.0-manylinux2014_x86_64.tar.gz
$ sudo ln -sf /usr/local/lib/
$ sudo ln -sf /usr/local/lib/
$ sudo ln -sf /usr/local/lib/
$ sudo ln -sf /usr/local/lib/
$ sudo ldconfig

Install WasmEdge-tensorflow:

$ wget
$ wget
$ sudo tar -C /usr/local/ -xzf WasmEdge-tensorflow-0.8.0-manylinux2014_x86_64.tar.gz
$ sudo tar -C /usr/local/ -xzf WasmEdge-tensorflowlite-0.8.0-manylinux2014_x86_64.tar.gz
$ sudo ldconfig

Install WasmEdge-image:

$ wget
$ sudo tar -C /usr/local/ -xzf WasmEdge-image-0.8.0-manylinux2014_x86_64.tar.gz
$ sudo ldconfig

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

Download pre-trained TensorflowLitee model: lite-model_aiy_vision_classifier_food_V1_1.tflite, store to rust_mobilenet_foods/src:

$ wget '' -O ./rust_mobilenet_food/src/lite-model_aiy_vision_classifier_food_V1_1.tflite

Compile to wasm file:

Install rustc and cargo

$ curl --proto '=https' --tlsv1.2 -sSf | sh
$ export PATH=$PATH:$HOME/.cargo/bin
$ rustc --version

Set default rust version to 1.50.0: $ rustup default 1.50.0

Install rustwasmc

$ curl -sSf | sh
$ cd rust_mobilenet_food
$ rustwasmc build
# The output WASM will be `pkg/rust_mobilenet_food_lib_bg.wasm`.

Copy pkg/rust_mobilenet_food_lib_bg.wasm to flow directory:

$ cp pkg/rust_mobilenet_food_lib_bg.wasm ../.

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 vide: hot-dog.mp4, store to source directory, then run:

$ wget -P source ''
$ go run ./source/main.go ./source/hot-dog.mp4

8. Result


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