openvino/samples/cpp/hello_query_device

Hello Query Device C++ Sample

This sample demonstrates how to execute an query Inference Engine devices, prints their metrics and default configuration values, using Query Device API feature.

Hello Query Device C++ sample application demonstrates how to use the following Inference Engine C++ API in applications:

Feature	API	Description
Available Devices	InferenceEngine::Core::GetAvailableDevices, InferenceEngine::Core::GetMetric, InferenceEngine::Core::GetConfig	Get available devices information and configuration for inference

Basic Inference Engine API is covered by Hello Classification C++ sample.

Options	Values
Supported devices	All
Other language realization	Python

How It Works

The sample queries all available Inference Engine devices, prints their supported metrics and plugin configuration parameters.

Building

To build the sample, please use instructions available at Build the Sample Applications section in Inference Engine Samples guide.

Running

To see quired information, run the following:

<path_to_sample>/hello_query_device -h
Usage : hello_query_device

Sample Output

The application prints all available devices with their supported metrics and default values for configuration parameters:

Available devices:
        Device: CPU
        Metrics:
                SUPPORTED_METRICS : [ AVAILABLE_DEVICES SUPPORTED_METRICS FULL_DEVICE_NAME OPTIMIZATION_CAPABILITIES SUPPORTED_CONFIG_KEYS RANGE_FOR_ASYNC_INFER_REQUESTS RANGE_FOR_STREAMS ]
                FULL_DEVICE_NAME : Intel(R) Core(TM) i5-8350U CPU @ 1.70GHz
                OPTIMIZATION_CAPABILITIES : [ FP32 FP16 INT8 BIN ]
                SUPPORTED_CONFIG_KEYS : [ CPU_BIND_THREAD CPU_THREADS_NUM CPU_THROUGHPUT_STREAMS DUMP_EXEC_GRAPH_AS_DOT DYN_BATCH_ENABLED DYN_BATCH_LIMIT ENFORCE_BF16 EXCLUSIVE_ASYNC_REQUESTS PERF_COUNT ]
                RANGE_FOR_ASYNC_INFER_REQUESTS : { 1, 1, 1 }
                RANGE_FOR_STREAMS : { 1, 8 }
        Default values for device configuration keys:
                CPU_BIND_THREAD : NUMA
                CPU_THREADS_NUM : 0
                CPU_THROUGHPUT_STREAMS : 1
                DUMP_EXEC_GRAPH_AS_DOT : ""
                DYN_BATCH_ENABLED : NO
                DYN_BATCH_LIMIT : 0
                ENFORCE_BF16 : NO
                EXCLUSIVE_ASYNC_REQUESTS : NO
                PERF_COUNT : NO

        Device: GPU
        Metrics:
                SUPPORTED_METRICS : [ AVAILABLE_DEVICES SUPPORTED_METRICS FULL_DEVICE_NAME OPTIMIZATION_CAPABILITIES SUPPORTED_CONFIG_KEYS RANGE_FOR_ASYNC_INFER_REQUESTS RANGE_FOR_STREAMS ]
                FULL_DEVICE_NAME : Intel(R) UHD Graphics 620 (iGPU)
                OPTIMIZATION_CAPABILITIES : [ FP32 BIN FP16 ]
                SUPPORTED_CONFIG_KEYS : [ CACHE_DIR CLDNN_ENABLE_FP16_FOR_QUANTIZED_MODELS CLDNN_GRAPH_DUMPS_DIR GPU_MAX_NUM_THREADS CLDNN_MEM_POOL CLDNN_NV12_TWO_INPUTS CLDNN_PLUGIN_PRIORITY CLDNN_PLUGIN_THROTTLE CLDNN_SOURCES_DUMPS_DIR GPU_ENABLE_LOOP_UNROLLING CONFIG_FILE DEVICE_ID DUMP_KERNELS DYN_BATCH_ENABLED EXCLUSIVE_ASYNC_REQUESTS GPU_THROUGHPUT_STREAMS PERF_COUNT TUNING_FILE TUNING_MODE ]
                RANGE_FOR_ASYNC_INFER_REQUESTS : { 1, 2, 1 }
                RANGE_FOR_STREAMS : { 1, 2 }
        Default values for device configuration keys:
                CACHE_DIR : ""
                CLDNN_ENABLE_FP16_FOR_QUANTIZED_MODELS : YES
                CLDNN_GRAPH_DUMPS_DIR : ""
                CLDNN_MEM_POOL : YES
                CLDNN_NV12_TWO_INPUTS : NO
                CLDNN_PLUGIN_PRIORITY : 0
                CLDNN_PLUGIN_THROTTLE : 0
                CLDNN_SOURCES_DUMPS_DIR : ""
                GPU_MAX_NUM_THREADS : 8
                GPU_ENABLE_LOOP_UNROLLING : YES
                CONFIG_FILE : ""
                DEVICE_ID : ""
                DUMP_KERNELS : NO
                DYN_BATCH_ENABLED : NO
                EXCLUSIVE_ASYNC_REQUESTS : NO
                GPU_THROUGHPUT_STREAMS : 1
                PERF_COUNT : NO
                TUNING_FILE : ""
                TUNING_MODE : TUNING_DISABLED

See Also

• Integrate the Inference Engine with Your Application
• Using Inference Engine Samples