# Python Sample

The Python examples below show how to configure ONNX Runtime to use the QAic Execution Provider:

- Build a QAic provider options dictionary with the `config` (model settings YAML) and optional `aic_device_id`.
- Create a session with `QAicExecutionProvider` and the provider options.
- Load input tensor data from `.raw` and run inference.

## Load a model

import onnxruntime as ort
    
    # QAIC Configs
    aic_device_id = 0  # example device ID
    config_path = "/opt/qti-aic/integrations/qaic_onnxrt/tests/resnet50/resnet50.yaml"
    model_path = "/opt/qti-aic/integrations/qaic_onnxrt/tests/resnet50/resnet50-v1-12-batch.onnx"
    raw_path = "/opt/qti-aic/integrations/qaic_onnxrt/tests/resnet50/input_goldfish.raw"
    input_name = "data"
    
    qaic_provider_options = {
        "config": config_path,
        "device_id": str(aic_device_id),
    }
    
    providers = ["QAicExecutionProvider"]
    provider_options = [qaic_provider_options]
    
    sess_options = ort.SessionOptions()
    
    session = ort.InferenceSession(
        model_path,
        sess_options=sess_options,
        providers=providers,
        provider_options=provider_options,
    )
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## Run Inference

import numpy as np
    
    # Load raw input tensor from file
    
    input_data = np.fromfile(raw_path, dtype=np.float32)
    input_data = input_data.reshape(1, 3, 224, 224)  # example: NCHW for ResNet-50
    
    # Perform inference using ONNX Runtime
    outputs = session.run(
        None,
        {input_name: input_data},
    )
    
    # Print some info about outputs
    print("Number of outputs:", len(outputs))
    for idx, out in enumerate(outputs):
        print(f"Output {idx}: shape={out.shape}, dtype={out.dtype}")
        # Print first few elements
        flat = out.ravel()
        print("  first 10 values:", flat[:10])
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Last Published: Aug 25, 2026

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