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Co-authored-by: p-wysocki <przemyslaw.wysocki@intel.com>

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README.md

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throughput_benchmark.py

Akup/cherry pick samples namespace update (#19478 )

2023-08-29 14:48:54 +02:00

README.md

Throughput Benchmark Python Sample

@sphinxdirective

.. meta:: :description: Learn how to estimate performance of a model using Asynchronous Inference Request (Python) API in throughput mode.

This sample demonstrates how to estimate performance of a model using Asynchronous Inference Request API in throughput mode. Unlike :doc:demos <omz_demos> this sample doesn't have other configurable command line arguments. Feel free to modify sample's source code to try out different options.

The reported results may deviate from what :doc:benchmark_app <openvino_inference_engine_tools_benchmark_tool_README> reports. One example is model input precision for computer vision tasks. benchmark_app sets uint8, while the sample uses default model precision which is usually float32.

.. tab-set::

.. tab-item:: Requirements

  +--------------------------------+------------------------------------------------------------------------------+
  | Options                        | Values                                                                       |
  +================================+==============================================================================+
  | Validated Models               | :doc:`alexnet <omz_models_model_alexnet>`,                                   |
  |                                | :doc:`googlenet-v1 <omz_models_model_googlenet_v1>`,                         |
  |                                | :doc:`yolo-v3-tf <omz_models_model_yolo_v3_tf>`,                             |
  |                                | :doc:`face-detection-0200 <omz_models_model_face_detection_0200>`            |
  +--------------------------------+------------------------------------------------------------------------------+
  | Model Format                   | OpenVINO™ toolkit Intermediate Representation                                |
  |                                | (\*.xml + \*.bin), ONNX (\*.onnx)                                            |
  +--------------------------------+------------------------------------------------------------------------------+
  | Supported devices              | :doc:`All <openvino_docs_OV_UG_supported_plugins_Supported_Devices>`         |
  +--------------------------------+------------------------------------------------------------------------------+
  | Other language realization     | :doc:`C++ <openvino_inference_engine_samples_throughput_benchmark_README>`   |
  +--------------------------------+------------------------------------------------------------------------------+

.. tab-item:: Python API

  The following Python API is used in the application:

  +--------------------------------+-------------------------------------------------+----------------------------------------------+
  | Feature                        | API                                             | Description                                  |
  +================================+=================================================+==============================================+
  | OpenVINO Runtime Version       | [openvino.runtime.get_version]                  | Get Openvino API version.                    |
  +--------------------------------+-------------------------------------------------+----------------------------------------------+
  | Basic Infer Flow               | [openvino.runtime.Core],                        | Common API to do inference: compile a model, |
  |                                | [openvino.runtime.Core.compile_model]           | configure input tensors.                     |
  |                                | [openvino.runtime.InferRequest.get_tensor]      |                                              |
  +--------------------------------+-------------------------------------------------+----------------------------------------------+
  | Asynchronous Infer             | [openvino.runtime.AsyncInferQueue],             | Do asynchronous inference.                   |
  |                                | [openvino.runtime.AsyncInferQueue.start_async], |                                              |
  |                                | [openvino.runtime.AsyncInferQueue.wait_all],    |                                              |
  |                                | [openvino.runtime.InferRequest.results]         |                                              |
  +--------------------------------+-------------------------------------------------+----------------------------------------------+
  | Model Operations               | [openvino.runtime.CompiledModel.inputs]         | Get inputs of a model.                       |
  +--------------------------------+-------------------------------------------------+----------------------------------------------+
  | Tensor Operations              | [openvino.runtime.Tensor.get_shape],            | Get a tensor shape and its data.             |
  |                                | [openvino.runtime.Tensor.data]                  |                                              |
  +--------------------------------+-------------------------------------------------+----------------------------------------------+

.. tab-item:: Sample Code

  .. doxygensnippet:: samples/python/benchmark/throughput_benchmark/throughput_benchmark.py
     :language: python

How It Works ####################

The sample compiles a model for a given device, randomly generates input data, performs asynchronous inference multiple times for a given number of seconds. Then processes and reports performance results.

You can see the explicit description of each sample step at :doc:Integration Steps <openvino_docs_OV_UG_Integrate_OV_with_your_application> section of "Integrate OpenVINO™ Runtime with Your Application" guide.

Running ####################

.. code-block:: sh

python throughput_benchmark.py <path_to_model>

To run the sample, you need to specify a model:

You can use :doc:public <omz_models_group_public> or :doc:Intel's <omz_models_group_intel> pre-trained models from the Open Model Zoo. The models can be downloaded using the :doc:Model Downloader <omz_tools_downloader>.

.. note::

Before running the sample with a trained model, make sure the model is converted to the intermediate representation (IR) format (*.xml + *.bin) using :doc:model conversion API <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>.

The sample accepts models in ONNX format (.onnx) that do not require preprocessing.

Example ++++++++++++++++++++

Install the openvino-dev Python package to use Open Model Zoo Tools:

.. code-block:: sh

python -m pip install openvino-dev[caffe]
Download a pre-trained model using:

.. code-block:: sh

omz_downloader --name googlenet-v1
If a model is not in the IR or ONNX format, it must be converted. You can do this using the model converter:

.. code-block:: sh

omz_converter --name googlenet-v1
Perform benchmarking using the googlenet-v1 model on a CPU:

.. code-block:: sh

python throughput_benchmark.py googlenet-v1.xml

Sample Output ####################

The application outputs performance results.

.. code-block:: sh

[ INFO ] OpenVINO: [ INFO ] Build ................................. [ INFO ] Count: 2817 iterations [ INFO ] Duration: 10012.65 ms [ INFO ] Latency: [ INFO ] Median: 13.80 ms [ INFO ] Average: 14.10 ms [ INFO ] Min: 8.35 ms [ INFO ] Max: 28.38 ms [ INFO ] Throughput: 281.34 FPS