22 lines
1.9 KiB
Markdown
22 lines
1.9 KiB
Markdown
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# Bert Benchmark Python Sample
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This sample demonstrates how to estimate performance of a Bert model using Asynchronous Inference Request API. Unlike [demos](https://github.com/openvinotoolkit/open_model_zoo/tree/master/demos) this sample doesn't have configurable command line arguments. Feel free to modify sample's source code to try out different options.
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For more detailed information on how this sample works, check the dedicated [article](https://docs.openvino.ai/2025/get-started/learn-openvino/openvino-samples/bert-benchmark.html)
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The sample downloads a model and a tokenizer, export the model to onnx, reads the exported model and reshapes it to enforce dynamic input shapes, compiles the resulting model, downloads a dataset and runs benchmarking on the dataset.
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## Requirements
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The following Python API is used in the application:
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| Feature | API | Description |
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| -------------------------| ------------------------------------------------|----------------------------------------------|
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| OpenVINO API Version | [openvino.\_\_version__] | Get Openvino API version. |
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| Basic Infer Flow | [openvino.runtime.Core], | Common API to do inference: compile a model. |
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| | [openvino.runtime.Core.compile_model] | |
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| Asynchronous Infer | [openvino.runtime.AsyncInferQueue], | Do asynchronous inference. |
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| | [openvino.runtime.AsyncInferQueue.start_async], | |
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| | [openvino.runtime.AsyncInferQueue.wait_all] | |
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| Model Operations | [openvino.runtime.CompiledModel.inputs] | Get inputs of a model. |
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