AMD Ryzen AI

AMD’s Ryzen™ AI family of laptop processors provide users with an integrated Neural Processing Unit (NPU) which offloads the host CPU and GPU from AI processing tasks. Ryzen™ AI software consists of the Vitis™ AI execution provider (EP) for ONNX Runtime combined with quantization tools and a pre-optimized model zoo. All of this is made possible based on Ryzen™ AI technology built on AMD XDNA™ architecture, purpose-built to run AI workloads efficiently and locally, offering a host of benefits for the developer innovating the next groundbreaking AI app.

Optimum-AMD provides easy interface for loading and inference of Hugging Face models on Ryzen AI accelerator.

Ryzen AI Environment setup

A Ryzen AI environment needs to be enabled to use this library. Please refer to Ryzen AI’s Installation and Runtime Setup.

Minimal working example

To load a model and run inference with RyzenAI, you can just replace your AutoModelForXxx class with the corresponding RyzenAIModelForXxx class.

import requests
from PIL import Image

from optimum.amd.ryzenai import RyzenAIModelForImageClassification
from transformers import AutoFeatureExtractor, pipeline

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

model_id = <path of the model>
model = RyzenAIModelForImageClassification.from_pretrained(model_id, vaip_config=<path to config file>)
feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
cls_pipe = pipeline("image-classification", model=model, feature_extractor=feature_extractor)
outputs = cls_pipe(image)