Benchmark Application Demo

This topic demonstrates how to use the Benchmark Application to estimate deep learning inference performance on supported devices. Performance can be measured for two inference modes: synchronous and asynchronous.

NOTE: This topic describes usage of C++ implementation of the Benchmark Application. For the Python* implementation, refer to ./samples/python_samples/benchmark_app/ "Benchmark Application (Python*)"

How It Works

NOTE: To achieve benchmark results similar to the official published results, set CPU frequency to 2.9GHz and GPU frequency to 1GHz.

Upon the start-up, the application reads command-line parameters and loads a network and images to the Inference Engine plugin. The number of infer requests and execution approach depend on a mode defined with the -api command-line parameter.

Synchronous API

For synchronous mode, the primary metric is latency. The application creates one infer request and executes the Infer method. A number of executions is defined by one of the two values:

During the execution, the application collects two types of metrics:

Reported latency value is calculated as mean value of all collected latencies. Reported throughput value is a derivative from reported latency and additionally depends on batch size.

Asynchronous API

For asynchronous mode, the primary metric is throughput in frames per second (FPS). The application creates a certain number of infer requests and executes the StartAsync method. A number of infer is specified with the -nireq command-line parameter. A number of executions is defined by one of the two values:

The infer requests are executed asynchronously. Wait method is used to wait for previous execution to complete. The application measures all infer requests executions and reports the throughput metric based on batch size and total execution duration.


Running the application with the -h option yields the following usage message:

./benchmark_app -h
API version ............ <version>
Build .................. <number>
[ INFO ] Parsing input parameters
benchmark_app [OPTION]
-h Print a usage message
-i "<path>" Required. Path to a folder with images or to image files.
-m "<path>" Required. Path to an .xml file with a trained model.
-pp "<path>" Path to a plugin folder.
-api "<sync/async>" Required. Enable using sync/async API.
-d "<device>" Specify a target device to infer on: CPU, GPU, FPGA or MYRIAD. Use "-d HETERO:<comma separated devices list>" format to specify HETERO plugin. The application looks for a suitable plugin for the specified device.
-niter "<integer>" Optional. Number of iterations. If not specified, the number of iterations is calculated depending on a device.
-nireq "<integer>" Optional. Number of infer requests (default value is 2).
-l "<absolute_path>" Required for CPU custom layers. Absolute path to a shared library with the kernels implementations.
-c "<absolute_path>" Required for GPU custom kernels. Absolute path to an .xml file with the kernels description.
-b "<integer>" Optional. Batch size value. If not specified, the batch size value is determined from IR.

Running the application with the empty list of options yields the usage message given above and an error message.

You can run the application for one input layer four-dimensional models that support images as input, for example, public AlexNet and GoogLeNet models that can be downloaded with the OpenVINO Model Downloader.

NOTE: To run the application, the model should be first converted to the Inference Engine format (*.xml + *.bin)

using the Model Optimizer tool.

For example, to perform inference on CPU in the synchronous mode and get estimated performance metrics for AlexNet model, run the following command:

./benchmark_app -i <path_to_image>/inputImage.bmp -m <path_to_model>/alexnet_fp32.xml -d CPU -api sync

For the asynchronous mode:

./benchmark_app -i <path_to_image>/inputImage.bmp -m <path_to_model>/alexnet_fp32.xml -d CPU -api async

Demo Output

Application output depends on a used API. For synchronous API, the application outputs latency and throughput:

[ INFO ] Start inference synchronously (60000 ms duration)
[ INFO ] Latency: 37.91 ms
[ INFO ] Throughput: 52.7566 FPS

For asynchronous API, the application outputs only throughput:

[ INFO ] Start inference asynchronously (60000 ms duration, 2 inference requests in parallel)
[ INFO ] Throughput: 48.2031 FPS

See Also