[DOCS] menu restructuring 2025 pass 4 port 25.0

docs/articles_en/about-openvino.rst

@@ -40,7 +40,7 @@ OpenVINO Ecosystem
 Along with the primary components of model optimization and runtime, the toolkit also includes:
 
 * `Neural Network Compression Framework (NNCF) <https://github.com/openvinotoolkit/nncf>`__ - a tool for enhanced OpenVINO™ inference to get performance boost with minimal accuracy drop.
-* :doc:`Openvino Notebooks <learn-openvino/interactive-tutorials-python>`- Jupyter Python notebook, which demonstrate key features of the toolkit.
+* :doc:`Openvino Notebooks <get-started/learn-openvino/interactive-tutorials-python>`- Jupyter Python notebook, which demonstrate key features of the toolkit.
 * `OpenVINO Model Server <https://github.com/openvinotoolkit/model_server>`__ - a server that enables scalability via a serving microservice.
 * :doc:`OpenVINO Training Extensions  <documentation/openvino-ecosystem/openvino-training-extensions>` – a convenient environment to train Deep Learning models and convert them using the OpenVINO™ toolkit for optimized inference.
 * :doc:`Dataset Management Framework (Datumaro) <documentation/openvino-ecosystem/datumaro>` - a tool to build, transform, and analyze datasets.

docs/articles_en/about-openvino/performance-benchmarks/getting-performance-numbers.rst

@@ -128,7 +128,7 @@ General considerations
    When comparing OpenVINO Runtime performance with the framework or reference code,
    make sure that both versions are as similar as possible:
 
-   * Wrap the exact inference execution (for examples, see :doc:`Benchmark app <../../learn-openvino/openvino-samples/benchmark-tool>`).
+   * Wrap the exact inference execution (for examples, see :doc:`Benchmark app <../../get-started/learn-openvino/openvino-samples/benchmark-tool>`).
    * Do not include model loading time.
    * Ensure that the inputs are identical for OpenVINO Runtime and the framework. For example, watch out for random values that can be used to populate the inputs.
    * In situations when any user-side pre-processing should be tracked separately, consider :doc:`image pre-processing and conversion <../../openvino-workflow/running-inference/optimize-inference/optimize-preprocessing>`.
@@ -149,7 +149,7 @@ OpenVINO benchmarking (general)
 +++++++++++++++++++++++++++++++
 
 The default way of measuring OpenVINO performance is running a piece of code, referred to as
-:doc:`the benchmark tool <../../learn-openvino/openvino-samples/benchmark-tool>`.
+:doc:`the benchmark tool <../../get-started/learn-openvino/openvino-samples/benchmark-tool>`.
 For Python, it is part of the OpenVINO Runtime installation, while for C++, it is available as
 a code sample.
 
@@ -186,7 +186,7 @@ Internal Inference Performance Counters and Execution Graphs
 
 More detailed insights into inference performance breakdown can be achieved with device-specific
 performance counters and/or execution graphs.
-Both :doc:`C++ and Python <../../learn-openvino/openvino-samples/benchmark-tool>`
+Both :doc:`C++ and Python <../../get-started/learn-openvino/openvino-samples/benchmark-tool>`
 versions of the benchmark_app support a ``-pc`` command-line parameter that outputs an internal
 execution breakdown.
 

docs/articles_en/about-openvino/performance-benchmarks/performance-benchmarks-faq.rst

@@ -27,7 +27,7 @@ Performance Information F.A.Q.
 
    All of the performance benchmarks on traditional network models are generated using the
    open-source tool within the Intel® Distribution of OpenVINO™ toolkit
-   called :doc:`benchmark_app <../../learn-openvino/openvino-samples/benchmark-tool>`.
+   called :doc:`benchmark_app <../../get-started/learn-openvino/openvino-samples/benchmark-tool>`.
 
    For diffusers (Stable-Diffusion) and foundational models (aka LLMs) please use the OpenVINO GenAI
    opensource repo `OpenVINO GenAI tools/llm_bench <https://github.com/openvinotoolkit/openvino.genai/tree/master/tools/llm_bench>`__

docs/articles_en/documentation/openvino-extensibility.rst

@@ -187,6 +187,6 @@ See Also
 ########
 
 * :doc:`OpenVINO Transformations <openvino-extensibility/transformation-api>`
-* :doc:`Using OpenVINO Runtime Samples <../learn-openvino/openvino-samples>`
-* :doc:`Hello Shape Infer SSD sample <../learn-openvino/openvino-samples/hello-reshape-ssd>`
+* :doc:`Using OpenVINO Runtime Samples <../get-started/learn-openvino/openvino-samples>`
+* :doc:`Hello Shape Infer SSD sample <../get-started/learn-openvino/openvino-samples/hello-reshape-ssd>`
 

docs/articles_en/documentation/openvino-extensibility/openvino-plugin-library/advanced-guides/low-precision-transformations.rst

@@ -322,7 +322,7 @@ redistributed in the "Saved model" format, converted to a frozen graph using the
 Inference
 +++++++++
 
-The simplest way to infer the model and collect performance counters is :doc:`Benchmark Application <../../../../learn-openvino/openvino-samples/benchmark-tool>`.
+The simplest way to infer the model and collect performance counters is :doc:`Benchmark Application <../../../../get-started/learn-openvino/openvino-samples/benchmark-tool>`.
 
 .. code-block:: sh
 

docs/articles_en/documentation/openvino-security.rst

@@ -86,4 +86,4 @@ Additional Resources
 - Intel® Distribution of OpenVINO™ toolkit `home page <https://software.intel.com/en-us/openvino-toolkit>`__.
 - :doc:`Convert a Model <../openvino-workflow/model-preparation/convert-model-to-ir>`.
 - :doc:`OpenVINO™ Runtime User Guide <../openvino-workflow/running-inference>`.
-- For more information on Sample Applications, see the :doc:`OpenVINO Samples Overview <../learn-openvino/openvino-samples>`
+- For more information on Sample Applications, see the :doc:`OpenVINO Samples Overview <../get-started/learn-openvino/openvino-samples>`

docs/articles_en/get-started.rst

@@ -12,6 +12,7 @@ GET STARTED
    :hidden:
 
    Install OpenVINO <get-started/install-openvino>
+   Learn OpenVINO <get-started/learn-openvino>
    System Requirements <./about-openvino/release-notes-openvino/system-requirements>
 
 
@@ -67,7 +68,7 @@ Learn the basics of working with models and inference in OpenVINO. Begin with 
 Interactive Tutorials - Jupyter Notebooks
 -----------------------------------------
 
-Start with :doc:`interactive Python <learn-openvino/interactive-tutorials-python>` that show the basics of model inference, the OpenVINO API, how to convert models to OpenVINO format, and more.
+Start with :doc:`interactive Python <get-started/learn-openvino/interactive-tutorials-python>` that show the basics of model inference, the OpenVINO API, how to convert models to OpenVINO format, and more.
 
 * `Hello Image Classification <notebooks/hello-world-with-output.html>`__ - Load an image classification model in OpenVINO and use it to apply a label to an image
 * `OpenVINO Runtime API Tutorial <notebooks/openvino-api-with-output.html>`__ - Learn the basic Python API for working with models in OpenVINO
@@ -79,7 +80,7 @@ Start with :doc:`interactive Python <learn-openvino/interactive-tutorials-python
 OpenVINO Code Samples
 ---------------------
 
-View :doc:`sample code <learn-openvino/openvino-samples>` for various C++ and Python applications that can be used as a starting point for your own application. For C++ developers, step through the :doc:`Get Started with C++ Samples <learn-openvino/openvino-samples/get-started-demos>` to learn how to build and run an image classification program that uses OpenVINO’s C++ API.
+View :doc:`sample code <get-started/learn-openvino/openvino-samples>` for various C++ and Python applications that can be used as a starting point for your own application. For C++ developers, step through the :doc:`Get Started with C++ Samples <get-started/learn-openvino/openvino-samples/get-started-demos>` to learn how to build and run an image classification program that uses OpenVINO’s C++ API.
 
 .. _integrate-openvino:
 
@@ -120,7 +121,7 @@ Pipeline and model configuration features in OpenVINO Runtime allow you to easil
 * :doc:`Automatic Batching <openvino-workflow/running-inference/inference-devices-and-modes/automatic-batching>` performs on-the-fly grouping of inference requests to maximize utilization of the target hardware’s memory and processing cores.
 * :doc:`Performance Hints <openvino-workflow/running-inference/optimize-inference/high-level-performance-hints>` automatically adjust runtime parameters to prioritize for low latency or high throughput
 * :doc:`Dynamic Shapes <openvino-workflow/running-inference/dynamic-shapes>` reshapes models to accept arbitrarily-sized inputs, increasing flexibility for applications that encounter different data shapes
-* :doc:`Benchmark Tool <learn-openvino/openvino-samples/benchmark-tool>` characterizes model performance in various hardware and pipeline configurations
+* :doc:`Benchmark Tool <get-started/learn-openvino/openvino-samples/benchmark-tool>` characterizes model performance in various hardware and pipeline configurations
 
 .. _additional-about-openvino/additional-resources:
 

docs/articles_en/get-started/install-openvino/configurations.rst

@@ -15,6 +15,7 @@ Additional Configurations
    For GPU <configurations/configurations-intel-gpu>
    For NPU <configurations/configurations-intel-npu>
    GenAI Dependencies <configurations/genai-dependencies>
+   Troubleshooting Guide for OpenVINO™ Installation & Configuration <troubleshooting-install-config.html>
 
 For certain use cases, you may need to install additional software, to benefit from the full
 potential of OpenVINO™. Check the following list for components used in your workflow:

docs/articles_en/get-started/install-openvino/install-openvino-apt.rst

@@ -156,7 +156,7 @@ need to install additional components. Check the
 to see if your case needs any of them.
 
 With the APT distribution, you can build OpenVINO sample files, as explained in the
-:doc:`guide for OpenVINO sample applications <../../../learn-openvino/openvino-samples>`.
+:doc:`guide for OpenVINO sample applications <../../../get-started/learn-openvino/openvino-samples>`.
 For C++ and C, just run the ``build_samples.sh`` script:
 
 .. tab-set::
@@ -215,23 +215,23 @@ What's Next?
 Now that you've installed OpenVINO Runtime, you're ready to run your own machine learning applications!
 Learn more about how to integrate a model in OpenVINO applications by trying out the following tutorials:
 
-* Try the :doc:`C++ Quick Start Example <../../../learn-openvino/openvino-samples/get-started-demos>` for step-by-step
+* Try the :doc:`C++ Quick Start Example <../../../get-started/learn-openvino/openvino-samples/get-started-demos>` for step-by-step
   instructions on building and running a basic image classification C++ application.
 
   .. image:: https://user-images.githubusercontent.com/36741649/127170593-86976dc3-e5e4-40be-b0a6-206379cd7df5.jpg
      :width: 400
 
 * Visit the :ref:`Samples <code samples>` page for other C++ example applications to get you started with OpenVINO, such as:
 
-  * :doc:`Basic object detection with the Hello Reshape SSD C++ sample <../../../learn-openvino/openvino-samples/hello-reshape-ssd>`
-  * :doc:`Object classification sample <../../../learn-openvino/openvino-samples/hello-classification>`
+  * :doc:`Basic object detection with the Hello Reshape SSD C++ sample <../../../get-started/learn-openvino/openvino-samples/hello-reshape-ssd>`
+  * :doc:`Object classification sample <../../../get-started/learn-openvino/openvino-samples/hello-classification>`
 
 You can also try the following:
 
 * Learn more about :doc:`OpenVINO Workflow <../../../openvino-workflow>`.
 * To prepare your models for working with OpenVINO, see :doc:`Model Preparation <../../../openvino-workflow/model-preparation>`.
 * See pre-trained deep learning models on `Hugging Face <https://huggingface.co/OpenVINO>`__
 * Learn more about :doc:`Inference with OpenVINO Runtime <../../../openvino-workflow/running-inference>`.
-* See sample applications in :doc:`OpenVINO toolkit Samples Overview <../../../learn-openvino/openvino-samples>`.
+* See sample applications in :doc:`OpenVINO toolkit Samples Overview <../../../get-started/learn-openvino/openvino-samples>`.
 * Take a glance at the OpenVINO `product home page <https://software.intel.com/en-us/openvino-toolkit>`__ .
 

docs/articles_en/get-started/install-openvino/install-openvino-archive-linux.rst

@@ -224,7 +224,7 @@ Learn more about how to integrate a model in OpenVINO applications by trying out
       .. image:: https://user-images.githubusercontent.com/15709723/127752390-f6aa371f-31b5-4846-84b9-18dd4f662406.gif
          :width: 400
 
-      Visit the :doc:`Tutorials <../../../learn-openvino/interactive-tutorials-python>` page for more Jupyter Notebooks to get you started with OpenVINO, such as:
+      Visit the :doc:`Tutorials <../../../get-started/learn-openvino/interactive-tutorials-python>` page for more Jupyter Notebooks to get you started with OpenVINO, such as:
 
       * `OpenVINO Python API Tutorial <../../notebooks/openvino-api-with-output.html>`__
       * `Basic image classification program with Hello Image Classification <../../notebooks/hello-world-with-output.html>`__
@@ -234,16 +234,16 @@ Learn more about how to integrate a model in OpenVINO applications by trying out
    .. tab-item:: Get started with C++
       :sync: get-started-cpp
 
-      Try the :doc:`C++ Quick Start Example <../../../learn-openvino/openvino-samples/get-started-demos>` for step-by-step instructions
+      Try the :doc:`C++ Quick Start Example <../../../get-started/learn-openvino/openvino-samples/get-started-demos>` for step-by-step instructions
       on building and running a basic image classification C++ application.
 
       .. image:: https://user-images.githubusercontent.com/36741649/127170593-86976dc3-e5e4-40be-b0a6-206379cd7df5.jpg
          :width: 400
 
-      Visit the :doc:`Samples <../../../learn-openvino/openvino-samples>` page for other C++ example applications to get you started with OpenVINO, such as:
+      Visit the :doc:`Samples <../../../get-started/learn-openvino/openvino-samples>` page for other C++ example applications to get you started with OpenVINO, such as:
 
-      * :doc:`Basic object detection with the Hello Reshape SSD C++ sample <../../../learn-openvino/openvino-samples/hello-reshape-ssd>`
-      * :doc:`Object classification sample <../../../learn-openvino/openvino-samples/hello-classification>`
+      * :doc:`Basic object detection with the Hello Reshape SSD C++ sample <../../../get-started/learn-openvino/openvino-samples/hello-reshape-ssd>`
+      * :doc:`Object classification sample <../../../get-started/learn-openvino/openvino-samples/hello-classification>`
 
 
 
@@ -276,5 +276,5 @@ Additional Resources
 * :doc:`Troubleshooting Guide for OpenVINO Installation & Configuration <./configurations/troubleshooting-install-config>`
 * Converting models for use with OpenVINO™: :doc:`Convert a Model <../../../openvino-workflow/model-preparation>`
 * Writing your own OpenVINO™ applications: :doc:`OpenVINO™ Runtime User Guide <../../../openvino-workflow/running-inference>`
-* Sample applications: :doc:`OpenVINO™ Toolkit Samples Overview <../../../learn-openvino/openvino-samples>`
+* Sample applications: :doc:`OpenVINO™ Toolkit Samples Overview <../../../get-started/learn-openvino/openvino-samples>`
 * Pre-trained deep learning models on `Hugging Face <https://huggingface.co/OpenVINO>`__.

docs/articles_en/get-started/install-openvino/install-openvino-archive-macos.rst

@@ -145,7 +145,7 @@ Now that you've installed OpenVINO Runtime, you're ready to run your own machine
       .. image:: https://user-images.githubusercontent.com/15709723/127752390-f6aa371f-31b5-4846-84b9-18dd4f662406.gif
          :width: 400
 
-      Visit the :doc:`Tutorials <../../../learn-openvino/interactive-tutorials-python>` page for more Jupyter Notebooks to get you started with OpenVINO, such as:
+      Visit the :doc:`Tutorials <../../../get-started/learn-openvino/interactive-tutorials-python>` page for more Jupyter Notebooks to get you started with OpenVINO, such as:
 
       * `OpenVINO Python API Tutorial <../../notebooks/openvino-api-with-output.html>`__
       * `Basic image classification program with Hello Image Classification <../../notebooks/hello-world-with-output.html>`__
@@ -154,15 +154,15 @@ Now that you've installed OpenVINO Runtime, you're ready to run your own machine
    .. tab-item:: Get started with C++
       :sync: get-started-cpp
 
-      Try the :doc:`C++ Quick Start Example <../../../learn-openvino/openvino-samples/get-started-demos>` for step-by-step instructions on building and running a basic image classification C++ application.
+      Try the :doc:`C++ Quick Start Example <../../../get-started/learn-openvino/openvino-samples/get-started-demos>` for step-by-step instructions on building and running a basic image classification C++ application.
 
       .. image:: https://user-images.githubusercontent.com/36741649/127170593-86976dc3-e5e4-40be-b0a6-206379cd7df5.jpg
          :width: 400
 
       Visit the :ref:`Samples <code samples>` page for other C++ example applications to get you started with OpenVINO, such as:
 
-      * :doc:`Basic object detection with the Hello Reshape SSD C++ sample <../../../learn-openvino/openvino-samples/hello-reshape-ssd>`
-      * :doc:`Object classification sample <../../../learn-openvino/openvino-samples/hello-classification>`
+      * :doc:`Basic object detection with the Hello Reshape SSD C++ sample <../../../get-started/learn-openvino/openvino-samples/hello-reshape-ssd>`
+      * :doc:`Object classification sample <../../../get-started/learn-openvino/openvino-samples/hello-classification>`
 
 Uninstalling Intel® Distribution of OpenVINO™ Toolkit
 #####################################################
@@ -189,5 +189,5 @@ Additional Resources
 * :doc:`Troubleshooting Guide for OpenVINO Installation & Configuration <../install-openvino>`
 * :doc:`Convert models for use with OpenVINO™ <../../../openvino-workflow/model-preparation/convert-model-to-ir>`
 * :doc:`Write your own OpenVINO™ applications <../../../openvino-workflow/running-inference/integrate-openvino-with-your-application>`
-* Sample applications: :doc:`OpenVINO™ Toolkit Samples Overview <../../../learn-openvino/openvino-samples>`
+* Sample applications: :doc:`OpenVINO™ Toolkit Samples Overview <../../../get-started/learn-openvino/openvino-samples>`
 * Pre-trained deep learning models on `Hugging Face <https://huggingface.co/OpenVINO>`__