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getting_started

README.md

Ryzen™ AI iGPU Example

Getting started with iGPU

This is an example showing how to run the ResNet50 model from PyTorch on AMD's integrated GPU. We will use Olive to convert the model to ONNX format, convert it to FP16 precision, and optimize it. We will then use DirectML execution provider to run the model on the iGPU.

Activate Ryzen AI conda environment

Create a clone of the Ryzen AI installation conda environment to add required python packages

set RYZEN_AI_CONDA_ENV_NAME=ryzen-ai-<version>
conda create --name igpu-example --clone %RYZEN_AI_CONDA_ENV_NAME%
conda activate igpu-example

Set RyzenAI Environment variable

# Default location of RyzenAI software installation
set RYZEN_AI_INSTALLATION_PATH=<Path to RyzenAI Installation>

Install Olive

cd <RyzenAI-SW>\CNN-examples\iGPU\getting_started
python -m pip install -r requirements.txt

Install additional dependencies for the example

python -m olive.workflows.run --config resnet50_config.json --setup

Optimize the model using Olive

python -m olive.workflows.run --config resnet50_config.json

The optimized models will be available in ./torch_to_onnx-float16_conversion-perf_tuning/.

Run the generated model on the iGPU

Deployment in Python

python predict.py

NOTE: In predict.py, line 15, the iGPU device ID is enumerated as 0. For PCs with multiple GPUs, you may adjust the device_id to target a specific iGPU.

Deployment in C++

Prerequisites

  1. Visual Studio 2022 Community edition, ensure “Desktop Development with C++” is installed
  2. cmake (version >= 3.26)
  3. opencv (version=4.6.0) required for the resnet50 example

Install OpenCV from source

It is recommended to build OpenCV from the source code and use static build. The following instruction installs OpenCV in the location "C:\opencv" as an example, this can be changed by modifying CMAKE_PREFIX_PATH in the following cmake command. You may first change the directory to where you want to clone the OpenCV repository.

git clone https://github.com/opencv/opencv.git -b 4.6.0
cd opencv

cmake -DCMAKE_EXPORT_COMPILE_COMMANDS=ON -DBUILD_SHARED_LIBS=OFF -DCMAKE_POSITION_INDEPENDENT_CODE=ON -DCMAKE_CONFIGURATION_TYPES=Release -A x64 -T host=x64 -G "Visual Studio 17 2022" "-DCMAKE_INSTALL_PREFIX=C:\opencv" "-DCMAKE_PREFIX_PATH=C:\opencv" -DCMAKE_BUILD_TYPE=Release -DBUILD_opencv_python2=OFF -DBUILD_opencv_python3=OFF -DBUILD_WITH_STATIC_CRT=OFF -B build

cmake --build build --config Release
cmake --install build --config Release

The build files will be written to build\.

Run Olive-optimized ResNet50 model on the iGPU

Build the given ResNet50 C++ example:

cd cpp 
compile.bat "path/to/your/opencv/build"

Run inference:

run.bat