16 lines
1.9 KiB
Markdown
16 lines
1.9 KiB
Markdown
# Benchmark C++ Tool
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This page demonstrates how to use the Benchmark C++ Tool to estimate deep learning inference performance on supported devices.
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> **NOTE**: This page describes usage of the C++ implementation of the Benchmark Tool. For the Python implementation, refer to the [Benchmark Python Tool](https://docs.openvino.ai/2025/get-started/learn-openvino/openvino-samples/benchmark-tool.html) page. The Python version is recommended for benchmarking models that will be used in Python applications, and the C++ version is recommended for benchmarking models that will be used in C++ applications. Both tools have a similar command interface and backend.
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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/benchmark-tool.html)
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## Requriements
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To use the C++ benchmark_app, you must first build it following the [Build the Sample Applications](https://docs.openvino.ai/2025/get-started/learn-openvino/openvino-samples.html) instructions and then set up paths and environment variables by following the [Get Ready for Running the Sample Applications](https://docs.openvino.ai/2025/get-started/learn-openvino/openvino-samples/get-started-demos.html) instructions. Navigate to the directory where the benchmark_app C++ sample binary was built.
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> **NOTE**: If you installed OpenVINO Runtime using PyPI or Anaconda Cloud, only the [Benchmark Python Tool](https://docs.openvino.ai/2025/get-started/learn-openvino/openvino-samples/benchmark-tool.html) is available, and you should follow the usage instructions on that page instead.
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The benchmarking application works with models in the OpenVINO IR, TensorFlow, TensorFlow Lite, PaddlePaddle, PyTorch and ONNX formats. If you need it, OpenVINO also allows you to [convert your models](https://docs.openvino.ai/2025/documentation/openvino-workflow/model-preparation/convert-model-to-ir.html).
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