Ryzen™ AI Tutorial

This folder contains the following files:

• classification.ipynb: Jupyter Notebook demonstrating how to grab a classificatioin model from torchvision and port it to run on the Ryzen AI Neural Processing Unit (NPU)
• classification_utils.py: This is a Python file contains some sub-functions.
• README.md: This file provides an overview of the folder's contents.
• requirements.txt: This file contains the necessary dependencies and packages required to run the code in this folder.
• data\: This folder conatains an input image with corresponding classes info.
• vaip_config.json: This is the default runtime configuration file. It can also be found in the ryzen-ai-sw installation package.

Before running this example, ensure that you have followed the Ryzen AI Installation instructions found here and that you have activated the conda environment created during installation.

1. Copy the vaip_config.json file to here from the Ryzen AI installation folder.
2. Launch the Jupyter Notebook.
3. Ensure that you've pointed the Jupyter Notebook to the correct Python environment. To do this, in the top-right corner of the notebook, click "Select Kernel" and provide the path to the conda environment.
4. Run all the cells in the notebook.

📌 NOTE: If you can't find the kernel from the top-right dropdown menu, please exit the notebook and install it as below:

python -m ipykernel install --user --name [CONDA_ENV_NAME]

To run the classification.py script, follow these steps:

Step 1: Ensure Environment Setup Before running the script, ensure you have activated the Ryzen AI conda environment

conda activate ryzen-ai-1.3.1

Step 2: Download the Dataset. To obtain the calibration dataset, visit the ImageNet dataset repository on Hugging Face. You need to register on Hugging Face and download the following file: val_images.tar.gz.

This file contains a subset of ImageNet images used specifically for calibration.

Once downloaded, move the file to your working directory and run the following commands to extract the dataset into the calib_data directory.

mkdir calib_data
tar -xzf calib_images.tar.gz -C calib_data

Step 3: Run the Quark-Based Example To execute the hello_world_quark.py script, use the following command:

python classification.py

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