Throughput Benchmark Python* Sample

This sample demonstrates how to estimate performace of a model using Asynchronous Inference Request API in throughput mode. Unlike 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 benchmark_app 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.

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], [openvino.runtime.Core.compile_model], [openvino.runtime.InferRequest.get_tensor]

Common API to do inference: compile a model, configure input tensors

Asynchronous Infer

[openvino.runtime.AsyncInferQueue], [openvino.runtime.AsyncInferQueue.start_async], [openvino.runtime.AsyncInferQueue.wait_all], [openvino.runtime.InferRequest.results]

Do asynchronous inference

Model Operations

[openvino.runtime.CompiledModel.inputs]

Get inputs of a model

Tensor Operations

[openvino.runtime.Tensor.get_shape], [openvino.runtime.Tensor.data]

Get a tensor shape and its data.

Options

Values

Validated Models

alexnet, googlenet-v1, yolo-v3-tf , face-detection-0200

Model Format

OpenVINO™ toolkit Intermediate Representation (*.xml + *.bin), ONNX (*.onnx)

Supported devices

All

Other language realization

C++

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 Integration Steps section of “Integrate OpenVINO™ Runtime with Your Application” guide.

Running

python throughput_benchmark.py <path_to_model>

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

NOTES :

  • Before running the sample with a trained model, make sure the model is converted to the intermediate representation (IR) format (*.xml + *.bin) using the Model Optimizer tool.

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

Example

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

    python -m pip install openvino-dev[caffe]

  2. Download a pre-trained model using:

    omz_downloader --name googlenet-v1

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

    omz_converter --name googlenet-v1

  4. Perform benchmarking using the googlenet-v1 model on a CPU:

    python throughput_benchmark.py googlenet-v1.xml

Sample Output

The application outputs performance results.

[ INFO ] OpenVINO:
[ INFO ] Build ................................. <version>
[ 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