#include int main() { ov::Core core; // Read a network in IR, PaddlePaddle, or ONNX format: std::shared_ptr model = core.read_model("sample.xml"); { //! [part4] // Example 1 ov::CompiledModel compiled_model0 = core.compile_model(model, "AUTO", ov::hint::model_priority(ov::hint::Priority::HIGH)); ov::CompiledModel compiled_model1 = core.compile_model(model, "AUTO", ov::hint::model_priority(ov::hint::Priority::MEDIUM)); ov::CompiledModel compiled_model2 = core.compile_model(model, "AUTO", ov::hint::model_priority(ov::hint::Priority::LOW)); /************ Assume that all the devices (CPU and GPUs) can support all the models. Result: compiled_model0 will use GPU.1, compiled_model1 will use GPU.0, compiled_model2 will use CPU. ************/ // Example 2 ov::CompiledModel compiled_model3 = core.compile_model(model, "AUTO", ov::hint::model_priority(ov::hint::Priority::LOW)); ov::CompiledModel compiled_model4 = core.compile_model(model, "AUTO", ov::hint::model_priority(ov::hint::Priority::MEDIUM)); ov::CompiledModel compiled_model5 = core.compile_model(model, "AUTO", ov::hint::model_priority(ov::hint::Priority::LOW)); /************ Assume that all the devices (CPU and GPUs) can support all the models. Result: compiled_model3 will use GPU.1, compiled_model4 will use GPU.1, compiled_model5 will use GPU.0. ************/ //! [part4] } return 0; }