Integrate OpenVINO™ with Your Application#

Following these steps, you can implement a typical OpenVINO™ Runtime inference pipeline in your application. Before proceeding, make sure you have installed OpenVINO Runtime and set environment variables (run <INSTALL_DIR>/setupvars.sh for Linux, setupvars.ps1 for Windows PowerShell, or setupvars.bat for Windows CMD). Otherwise, the OpenVINO_DIR variable won’t be configured properly to pass find_package calls.

../../_images/IMPLEMENT_PIPELINE_with_API_C.svg

Step 1. Create OpenVINO Runtime Core#

Include the necessary files to work with OpenVINO™ Runtime and create OpenVINO™ Core to manage available devices and read model objects:

Python

import openvino as ov

core = ov.Core()

C++

#include <openvino/openvino.hpp>

ov::Core core;

#include <openvino/c/openvino.h>

ov_core_t* core = NULL;
ov_core_create(&core);

Step 3. Create an Inference Request#

ov::InferRequest class provides methods for model inference in OpenVINO™ Runtime. Create an infer request using the following code (see InferRequest documentation for more details):

Python

infer_request = compiled_model.create_infer_request()

C++

ov::InferRequest infer_request = compiled_model.create_infer_request();

ov_infer_request_t* infer_request = NULL;
ov_compiled_model_create_infer_request(compiled_model, &infer_request);

Step 4. Set Inputs#

You can use external memory to create ov::Tensor and use the ov::InferRequest::set_input_tensor method to put this tensor on the device:

Python

# Create tensor from external memory
input_tensor = ov.Tensor(array=memory, shared_memory=True)
# Set input tensor for model with one input
infer_request.set_input_tensor(input_tensor)

C++

// Get input port for model with one input
auto input_port = compiled_model.input();
// Create tensor from external memory
ov::Tensor input_tensor(input_port.get_element_type(), input_port.get_shape(), memory_ptr);
// Set input tensor for model with one input
infer_request.set_input_tensor(input_tensor);

// Get input port for model with one input
ov_output_const_port_t* input_port = NULL;
ov_compiled_model_input(compiled_model, &input_port);
// Get the input shape from input port
ov_shape_t input_shape;
ov_const_port_get_shape(input_port, &input_shape);
// Get the the type of input
ov_element_type_e input_type;
ov_port_get_element_type(input_port, &input_type);
// Create tensor from external memory
ov_tensor_t* tensor = NULL;
ov_tensor_create_from_host_ptr(input_type, input_shape, memory_ptr, &tensor);
// Set input tensor for model with one input
ov_infer_request_set_input_tensor(infer_request, tensor);

See additional materials to learn how to handle textual data as a model input.

Step 5. Start Inference#

OpenVINO™ Runtime supports inference in either synchronous or asynchronous mode. Using the Async API can improve application’s overall frame-rate: instead of waiting for inference to complete, the app can keep working on the host while the accelerator is busy. You can use ov::InferRequest::start_async to start model inference in the asynchronous mode and call ov::InferRequest::wait to wait for the inference results:

Python

infer_request.start_async()
infer_request.wait()

C++

infer_request.start_async();
infer_request.wait();

ov_infer_request_start_async(infer_request);
ov_infer_request_wait(infer_request);

This section demonstrates a simple pipeline. To get more information about other ways to perform inference, read the dedicated “Run inference” section.

Step 6. Process the Inference Results#

Go over the output tensors and process the inference results.

Python

# Get output tensor for model with one output
output = infer_request.get_output_tensor()
output_buffer = output.data
# output_buffer[] - accessing output tensor data

C++

// Get output tensor by tensor name
auto output = infer_request.get_tensor("tensor_name");
const float *output_buffer = output.data<const float>();
// output_buffer[] - accessing output tensor data

ov_tensor_t* output_tensor = NULL;
// Get output tensor by tensor index
ov_infer_request_get_output_tensor_by_index(infer_request, 0, &output_tensor);

See additional materials to learn how to handle textual data as a model output.

Step 7. Release the allocated objects (only for C)#

To avoid memory leak, applications developed with C API need to release the allocated objects in order.

ov_shape_free(&input_shape);
ov_tensor_free(output_tensor);
ov_output_const_port_free(input_port);
ov_tensor_free(tensor);
ov_infer_request_free(infer_request);
ov_compiled_model_free(compiled_model);
ov_model_free(model);
ov_core_free(core);

Step 8. Link and Build Your Application with OpenVINO™ Runtime (example)#

This step may differ for different projects. In this example, a C++ & C application is used, together with CMake for project configuration.

Create Structure for project:#

C++

project/
   ├── CMakeLists.txt  - CMake file to build
   ├── ...             - Additional folders like includes/
   └── src/            - source folder
       └── main.cpp
build/                  - build directory
   ...

project/
   ├── CMakeLists.txt  - CMake file to build
   ├── ...             - Additional folders like includes/
   └── src/            - source folder
       └── main.c
build/                  - build directory
   ...

Create Cmake Script#

For details on additional CMake build options, refer to the CMake page.

C++

cmake_minimum_required(VERSION 3.10)
set(CMAKE_CXX_STANDARD 11)

find_package(OpenVINO REQUIRED)

add_executable(${TARGET_NAME} src/main.cpp)

target_link_libraries(${TARGET_NAME} PRIVATE openvino::runtime)

cmake_minimum_required(VERSION 3.10)
set(CMAKE_CXX_STANDARD 11)

find_package(OpenVINO REQUIRED)

add_executable(${TARGET_NAME_C} src/main.c)

target_link_libraries(${TARGET_NAME_C} PRIVATE openvino::runtime::c)

C++ (PyPI)

cmake_minimum_required(VERSION 3.10)
set(CMAKE_CXX_STANDARD 11)

if(NOT CMAKE_CROSSCOMPILING)
    find_package(Python3 QUIET COMPONENTS Interpreter)
    if(Python3_Interpreter_FOUND)
        execute_process(
            COMMAND ${Python3_EXECUTABLE} -c "from openvino.utils import get_cmake_path; print(get_cmake_path(), end='')"
            OUTPUT_VARIABLE OpenVINO_DIR_PY
            ERROR_QUIET)
    endif()
endif()

find_package(OpenVINO REQUIRED PATHS "${OpenVINO_DIR_PY}")

add_executable(${TARGET_NAME_PY} src/main.cpp)

target_link_libraries(${TARGET_NAME_PY} PRIVATE openvino::runtime)

Build Project#

To build your project using CMake with the default build tools currently available on your machine, execute the following commands:

cd build/
cmake ../project
cmake --build .

Additional Resources#

OpenVINO™ Runtime API Tutorial
See the OpenVINO Samples page for specific examples of how OpenVINO pipelines are implemented for applications like image classification, text prediction, and many others.
Models in the OpenVINO IR format on Hugging Face.
OpenVINO™ Runtime Preprocessing
String Tensors
Using Encrypted Models with OpenVINO

Integrate OpenVINO™ with Your Application#

Step 1. Create OpenVINO Runtime Core#

Step 2. Compile the Model#

Step 3. Create an Inference Request#

Step 4. Set Inputs#

Step 5. Start Inference#

Step 6. Process the Inference Results#

Step 7. Release the allocated objects (only for C)#

Step 8. Link and Build Your Application with OpenVINO™ Runtime (example)#

Create Structure for project:#

Create Cmake Script#

Build Project#

Additional Resources#