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fix headings (openvinotoolkit#15207)
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sgolebiewski-intel authored Jan 23, 2023
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64 changes: 47 additions & 17 deletions docs/install_guides/configurations-for-intel-gna.md
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# Configurations for Intel® Gaussian & Neural Accelerator (GNA) with OpenVINO™ {#openvino_docs_install_guides_configurations_for_intel_gna}

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.. note:: On platforms where Intel® GNA is not enabled in the BIOS, the driver cannot be installed, so the GNA plugin uses the software emulation mode only.

> **NOTE**: On platforms where Intel® GNA is not enabled in the BIOS, the driver cannot be installed, so the GNA plugin uses the software emulation mode only.
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### Drivers and Dependencies

Intel® GNA hardware requires a driver to be installed on the system.
## Drivers and Dependencies

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Intel® GNA hardware requires a driver to be installed on the system.

.. _gna guide:

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## Linux

### Prerequisites
Expand All @@ -21,31 +26,56 @@ Ensure that make, gcc, and Linux kernel headers are installed.

### Configuration steps

1. Download [Intel® GNA driver for Ubuntu Linux 18.04.3 LTS (with HWE Kernel version 5.4+)](https://storage.openvinotoolkit.org/drivers/gna/)
2. Run the sample_install.sh script provided in the installation package:
```sh
prompt$ ./scripts/sample_install.sh
```
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#. Download `Intel® GNA driver for Ubuntu Linux 18.04.3 LTS (with HWE Kernel version 5.4+) <https://storage.openvinotoolkit.org/drivers/gna/>`__
#. Run the sample_install.sh script provided in the installation package:

.. code-block:: sh

prompt$ ./scripts/sample_install.sh


You can also build and install the driver manually by using the following commands:
```sh
prompt$ cd src/
prompt$ make
prompt$ sudo insmod intel_gna.ko
```

.. code-block:: sh

prompt$ cd src/
prompt$ make
prompt$ sudo insmod intel_gna.ko


To unload the driver:
```sh
prompt$ sudo rmmod intel_gna
```

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.. code-block:: sh

prompt$ sudo rmmod intel_gna


.. _gna guide windows:

@endsphinxdirective


## Windows

Intel® GNA driver for Windows is available through Windows Update.

## What’s Next?

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Now you are ready to try out OpenVINO™. You can use the following tutorials to write your applications using Python and C++.

Developing in Python:
* `Start with tensorflow models with OpenVINO™ <https://docs.openvino.ai/nightly/notebooks/101-tensorflow-to-openvino-with-output.html>`_
* `Start with ONNX and PyTorch models with OpenVINO™ <https://docs.openvino.ai/nightly/notebooks/102-pytorch-onnx-to-openvino-with-output.html>`_
* `Start with PaddlePaddle models with OpenVINO™ <https://docs.openvino.ai/nightly/notebooks/103-paddle-onnx-to-openvino-classification-with-output.html>`_

Developing in C++:
* :doc:`Image Classification Async C++ Sample <openvino_inference_engine_samples_classification_sample_async_README>`
* :doc:`Hello Classification C++ Sample <openvino_inference_engine_samples_hello_classification_README>`
* :doc:`Hello Reshape SSD C++ Sample <openvino_inference_engine_samples_hello_reshape_ssd_README>`

@endsphinxdirective

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