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Docs: Add links to specific examples #11618

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May 9, 2022
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Docs: Add links to specific examples #11618

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Add links to specific examples
EdjeElectronics May 3, 2022
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Update docs/OV_Runtime_UG/integrate_with_your_application.md
kblaszczak-intel May 6, 2022
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16 changes: 10 additions & 6 deletions docs/OV_Runtime_UG/integrate_with_your_application.md
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@endsphinxdirective

> **NOTE**: Before start using OpenVINO™ Runtime, make sure you set all environment variables during the installation. If you did not, follow the instructions from the _Set the Environment Variables_ section in the installation guides:
> **NOTE**: Before start using OpenVINO™ Runtime, make sure you set all environment variables during the installation. To do so, follow the instructions from the _Set the Environment Variables_ section in the installation guides:
> * [For Windows* 10](../install_guides/installing-openvino-windows.md)
> * [For Linux*](../install_guides/installing-openvino-linux.md)
> * [For macOS*](../install_guides/installing-openvino-macos.md)
> * To build an open source version, use the [OpenVINO™ Runtime Build Instructions](https://github.com/openvinotoolkit/openvino/wiki/BuildingCode).

## Use OpenVINO™ Runtime API to Implement Inference Pipeline

This section provides step-by-step instructions to implement a typical inference pipeline with the OpenVINO™ Runtime C++ API:
This section provides step-by-step instructions to implement a typical inference pipeline with the OpenVINO™ Runtime C++ or Python API:

![ie_api_use_cpp]

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### Step 2. Compile the Model

`ov::CompiledModel` class represents a device specific compiled model. `ov::CompiledModel` allows you to get information inputs or output ports by a tensor name or index, this approach is aligned with the majority of frameworks.
`ov::CompiledModel` class represents a device specific compiled model. `ov::CompiledModel` allows you to get information inputs or output ports by a tensor name or index. This approach is aligned with the majority of frameworks.

Compile the model for a specific device using `ov::Core::compile_model()`:

Expand Down Expand Up @@ -185,7 +185,7 @@ You can use external memory to create `ov::Tensor` and use the `ov::InferRequest

### 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, because rather than wait 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:
OpenVINO™ Runtime supports inference in either synchronous or asynchronous mode. Using the Async API can improve application's overall frame-rate: rather than 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:

@sphinxtabset

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@endsphinxtabset

This section demonstrates a simple pipeline, to get more information about other ways to perform inference, read the dedicated ["Run inference" section](./ov_infer_request.md).
This section demonstrates a simple pipeline. To get more information about other ways to perform inference, read the dedicated ["Run inference" section](./ov_infer_request.md).

### Step 6. Process the Inference Results

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cmake ../project
cmake --build .
```
It's allowed to specify additional build options (e.g. to build CMake project on Windows with a specific build tools). Please refer to the [CMake page](https://cmake.org/cmake/help/latest/manual/cmake.1.html#manual:cmake(1)) for details.
You can also specify additional build options (e.g. to build CMake project on Windows with a specific build tools). Please refer to the [CMake page](https://cmake.org/cmake/help/latest/manual/cmake.1.html#manual:cmake(1)) for details.

## Run Your Application

Congratulations, you have made your first application with OpenVINO™ toolkit, now you may run it.

This page showed how to implement a typical inference pipeline with OpenVINO. See the [OpenVINO Samples](Samples_Overview.md) page or the [Open Model Zoo Demos](https://docs.openvino.ai/latest/omz_demos.html) page for specific examples of how OpenVINO pipelines are implemented for applications like image classification, text prediction, and many others.

## See also

- [OpenVINO™ Runtime Preprocessing](./preprocessing_overview.md)
- [Using Encrypted Models with OpenVINO™](./protecting_model_guide.md)
- [OpenVINO Samples](Samples_Overview.md)
- [Open Model Zoo Demos](https://docs.openvino.ai/latest/omz_demos.html)

[ie_api_flow_cpp]: img/BASIC_IE_API_workflow_Cpp.svg
[ie_api_use_cpp]: img/IMPLEMENT_PIPELINE_with_API_C.svg
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