Intel® Edge AI Performance Evaluation Toolkit User Guide

Intel® Edge AI Performance Evaluation Toolkit is an Edge AI customer enabling tool that has been designed to easily qualify and evaluate platform deep learning inference performance.

Components

It consists of scripts, configuration files, Intel Power and Thermal Analysis Tool (PTAT) workspace file and optimized OpenVINO INT8 IR model and brief explanation below,

• OS setup scripts - are used to setup container running environment on both Ubuntu Linux and Windows OS.

• OpenVINO POT quantization scripts and configuration files - are used to quantize OpenVINO FP32/FP16 IR models to INT8 by OpenVINO Post-Training Optimization Tool.

• Benchmark scripts and Intel PTAT workspace file - are used to benchmark optimized INT8 IR model and monitor system frequency and thermal condition to qualify system performance.

Supported HW

• Intel® Core™ i7-1165G7 Processor
• Intel® Core™ i7-1185G7E Processor
• Intel® Celeron® 6305E

License

Intel® Edge AI Performance Evaluation is licensed under Apache License Version 2.0. By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.

User Guide

Below are steps to get started for Ubuntu 20.04.4 and Windows 10 21H2

User Guide for Ubuntu 20.04

Install Ubuntu 20.04.4
https://ubuntu.com/tutorials/install-ubuntu-desktop#1-overview

Clone Intel® Edge AI Performance Evaluation Toolkit

sudo apt update
sudo apt upgrade
sudo apt install git
git clone https://github.com/intel/Intel-Edge-AI-Performance-Evaluation-Toolkit.git

Install docker utility by running

cd Intel-Edge-AI-Performance-Evaluation-Toolkit
bash tools/install_docker.sh

Reboot system.

Install Intel Power and Thermal Analysis Tool

Tool download link : Intel® Power And Thermal Analysis Tool

Loading Intel PTAT workspace file from ptat_workspace.xml

Run Benchmark and Quantization Scripts

1. Copy yolo-v4-tf FP16_INT8 IR model to Downloads folder in Home directory

cd Intel-Edge-AI-Performance-Evaluation-Toolkit
cp -ar openvino_models/ $HOME/Downloads

2. Run benchmark_app on yolo-v4-tf FP16_INT8 IR model on CPU

bash run_yolo-v4-tf-int8-cpu_benchmark.sh

3. Run benchmark_app on yolo-v4-tf FP16_INT8 IR model on GPU

bash run_yolo-v4-tf-int8-gpu_benchmark.sh

4. Run quantization on yolo-v3-tf FP16 IR model

bash quantize_yolo-v3-tf_int8.sh

User Guide for Windows 10

Install Windows 10 21H1

• Download Windows Insider Preview ISO (microsoft.com) and install

• Install required graphic driver (30.0.101.xxxx)

Download Intel® Edge AI Performance Evaluation Toolkit github link below

https://github.com/intel/Intel-Edge-AI-Performance-Evaluation-Toolkit/archive/refs/heads/main.zip

Extract to C:\Users\Public\Intel-Edge-AI-Performance-Evaluation-Toolkit.

Enable Hypher-V (Run as Administator in PowerShell)

Please refere to tools\enable-hyper-v.p1 and run below,

Enable-WindowsOptionalFeature -Online -FeatureName Microsoft-Hyper-V –All 

Press Y to reboot system.

Install WSL2 (Run as Administator in PowerShell) and Reboot

Please refere to tools\install_wsl2.p1 and run below,

wsl --install
Restart-Computer

After reboot, WSL will start automatically to install Ubuntu. Enter user name and password for WSL Ubuntu when prompt.

Install docker utility by running in WSL

cd /mnt/c/Users/Public/Intel-Edge-AI-Performance-Evaluation-Toolkit
bash tools/install_docker.sh

Reboot to activate docker settings.

Install Intel Power and Thermal Analysis Tool

Tool download link : Intel® Power And Thermal Analysis Tool

Launch Intel PTAT tool as administrator

Loading Intel PTAT workspace file from ptat_workspace.json

Run Benchmark and Quantization Scripts in WSL

1. Copy yolo-v4-tf FP16_INT8 IR model to Downloads folder in Home directory

cd /mnt/c/Users/Public/Intel-Edge-AI-Performance-Evaluation-Toolkit
mkdir $HOME/Downloads
cp -ar openvino_models/ $HOME/Downloads

2. Run benchmark_app on yolo-v4-tf FP16_INT8 IR model on CPU

bash run_yolo-v4-tf-int8-cpu_benchmark.sh

3. Run benchmark_app on yolo-v4-tf FP16_INT8 IR model on GPU

bash run_yolo-v4-tf-int8-gpu_benchmark.sh

4. Run quantization on yolo-v3-tf FP16 IR model

sudo apt install unzip
bash tools/download_coco_dataset.sh
bash quantize_yolo-v3-tf_int8.sh

How to contribute

See CONTRIBUTING for details. Thank you!

Get a support

Please report questions, issues and suggestions using:
GitHub* Issues