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[DOCS] Linkfix for Get Started section + removal of GPU drivers table in configurations for 2024 (#23187) 

* Fixed links in Get Started section of documentation
* Hid the table with GPU drivers used for validation. Current list
outdated, and the new one will be provided at later date.
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7
docs/articles_en/get-started.rst
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@ -68,7 +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 <learn-openvino/interactive-tutorials-python>` that show the basics of model inferencing, the OpenVINO API, how to convert models to OpenVINO format, and more.
Start with :doc:`interactive Python <learn-openvino/interactive-tutorials-python>` that show the basics of model inferencing, the OpenVINO API, how to convert models to OpenVINO format, and more.
* `Hello Image Classification <notebooks/001-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/002-openvino-api-with-output.html>`__ - Learn the basic Python API for working with models in OpenVINO
@ -101,9 +101,8 @@ Model Compression and Quantization
Use OpenVINO’s model compression tools to reduce your model’s latency and memory footprint while maintaining good accuracy.
* Tutorial - `OpenVINO Post-Training Model Quantization <notebooks/111-yolov5-quantization-migration-with-output.html>`__
* Tutorial - `Quantization-Aware Training in TensorFlow with OpenVINO NNCF <notebooks/305-tensorflow-quantization-aware-training-with-output.html>`__
* Tutorial - `Quantization-Aware Training in PyTorch with NNCF <notebooks/302-pytorch-quantization-aware-training-with-output.html>`__
* Tutorial - `Quantization-Aware Training in TensorFlow with OpenVINO NNCF <notebooks/305-tensorflow-quantization-aware-training-with-output>`__
* Tutorial - `Quantization-Aware Training in PyTorch with NNCF <notebooks/302-pytorch-quantization-aware-training-with-output>`__
* :doc:`Model Optimization Guide <openvino-workflow/model-optimization>`
Automated Device Configuration

37
docs/articles_en/get-started/configurations/configurations-intel-gpu.rst
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@ -22,7 +22,7 @@ To use a GPU device for OpenVINO inference, you must install OpenCL runtime pack
If you are using a discrete GPU (for example Arc 770), you must also be using a supported Linux kernel as per `documentation. <https://dgpu-docs.intel.com/driver/kernel-driver-types.html>`__
- For Arc GPU, kernel 6.2 or higher is recommended.
- For Max and Flex GPU, or Arc with kernel version lower than 6.2, you must also install the ``intel-i915-dkms`` and ``xpu-smi`` kernel modules as described in the installation ../../documentation for `Max/Flex <https://dgpu-docs.intel.com/driver/installation.html>`__ or `Arc. <https://dgpu-docs.intel.com/driver/client/overview.html>`__
- For Max and Flex GPU, or Arc with kernel version lower than 6.2, you must also install the ``intel-i915-dkms`` and ``xpu-smi`` kernel modules as described in the installation documentation for `Max/Flex <https://dgpu-docs.intel.com/driver/installation.html>`__ or `Arc. <https://dgpu-docs.intel.com/driver/client/overview.html>`__
Below are the instructions on how to install the OpenCL packages on supported Linux distributions. These instructions install the `Intel(R) Graphics Compute Runtime for oneAPI Level Zero and OpenCL(TM) Driver <https://github.com/intel/compute-runtime/releases/tag/23.22.26516.18>`__ and its dependencies:
@ -128,25 +128,28 @@ Below are the required steps to make it work with OpenVINO:
Additional Resources
####################
The following Intel® Graphics Driver versions were used during OpenVINO's internal validation:
.. The following Intel® Graphics Driver versions were used during OpenVINO's internal validation:
+------------------+-------------------------------------------------------------------------------------------+
| Operation System | Driver version |
+==================+===========================================================================================+
| Ubuntu 22.04 | `22.43.24595.30 <https://github.com/intel/compute-runtime/releases/tag/22.43.24595.30>`__ |
+------------------+-------------------------------------------------------------------------------------------+
| Ubuntu 20.04 | `22.35.24055 <https://github.com/intel/compute-runtime/releases/tag/22.35.24055>`__ |
+------------------+-------------------------------------------------------------------------------------------+
| Ubuntu 18.04 | `21.38.21026 <https://github.com/intel/compute-runtime/releases/tag/21.38.21026>`__ |
+------------------+-------------------------------------------------------------------------------------------+
| CentOS 7 | `19.41.14441 <https://github.com/intel/compute-runtime/releases/tag/19.41.14441>`__ |
+------------------+-------------------------------------------------------------------------------------------+
| RHEL 8 | `22.28.23726 <https://github.com/intel/compute-runtime/releases/tag/22.28.23726>`__ |
+------------------+-------------------------------------------------------------------------------------------+
.. <The table below is out of date and we do not have an updated list of drivers used for validation as of 2024.0 release date.>
.. <The table will be updated when an updated list of drivers is available>
.. +------------------+-------------------------------------------------------------------------------------------+
.. | Operation System | Driver version |
.. +==================+===========================================================================================+
.. | Ubuntu 22.04 | `22.43.24595.30 <https://github.com/intel/compute-runtime/releases/tag/22.43.24595.30>`__ |
.. +------------------+-------------------------------------------------------------------------------------------+
.. | Ubuntu 20.04 | `22.35.24055 <https://github.com/intel/compute-runtime/releases/tag/22.35.24055>`__ |
.. +------------------+-------------------------------------------------------------------------------------------+
.. | Ubuntu 18.04 | `21.38.21026 <https://github.com/intel/compute-runtime/releases/tag/21.38.21026>`__ |
.. +------------------+-------------------------------------------------------------------------------------------+
.. | CentOS 7 | `19.41.14441 <https://github.com/intel/compute-runtime/releases/tag/19.41.14441>`__ |
.. +------------------+-------------------------------------------------------------------------------------------+
.. | RHEL 8 | `22.28.23726 <https://github.com/intel/compute-runtime/releases/tag/22.28.23726>`__ |
.. +------------------+-------------------------------------------------------------------------------------------+
What’s Next?
############
.. What’s Next?
.. ############
* :doc:`GPU Device <../../openvino-workflow/running-inference/inference-devices-and-modes/gpu-device>`
* :doc:`Install Intel® Distribution of OpenVINO™ toolkit from a Docker Image <../install-openvino/install-openvino-archive-linux>`

10
docs/articles_en/get-started/install-openvino/install-openvino-apt.rst
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@ -228,9 +228,9 @@ 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 ../../../learn-openvino/interactive-tutorials-python:
Learn more about how to integrate a model in OpenVINO applications by trying out the following tutorials:
* Try the `C++ Quick Start Example <../../../learn-openvino/openvino-samples/get-started-demos.html>`_ for step-by-step
* 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.
.. image:: https://user-images.githubusercontent.com/36741649/127170593-86976dc3-e5e4-40be-b0a6-206379cd7df5.jpg
@ -238,8 +238,8 @@ Learn more about how to integrate a model in OpenVINO applications by trying out
* Visit the :ref:`Samples <code samples>` page for other C++ example applications to get you started with OpenVINO, such as:
* `Basic object detection with the Hello Reshape SSD C++ sample <../../../learn-openvino/openvino-samples/hello-reshape-ssd.html>`_
* `Object classification sample <../../../learn-openvino/openvino-samples/hello-classification.html>`_
* :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>`
You can also try the following:
@ -248,7 +248,7 @@ You can also try the following:
* See pre-trained deep learning models in our :doc:`Open Model Zoo <../../../documentation/legacy-features/model-zoo>`.
* 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>`.
* Take a glance at the OpenVINO product home page: https://software.intel.com/en-us/openvino-toolkit.
* Take a glance at the OpenVINO `product home page <https://software.intel.com/en-us/openvino-toolkit>`__ .

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docs/articles_en/get-started/install-openvino/install-openvino-archive-linux.rst
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@ -276,14 +276,14 @@ 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 ../../../learn-openvino/interactive-tutorials-python.
Learn more about how to integrate a model in OpenVINO applications by trying out the following tutorials.
.. tab-set::
.. tab-item:: Get started with Python
:sync: get-started-py
Try the `Python Quick Start Example <../../../notebooks/201-vision-monodepth-with-output.html>`_
Try the `Python Quick Start Example <../../notebooks/201-vision-monodepth-with-output.html>`__
to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.
.. image:: https://user-images.githubusercontent.com/15709723/127752390-f6aa371f-31b5-4846-84b9-18dd4f662406.gif
@ -291,9 +291,9 @@ Learn more about how to integrate a model in OpenVINO applications by trying out
Visit the :doc:`Tutorials <../../../learn-openvino/interactive-tutorials-python>` page for more Jupyter Notebooks to get you started with OpenVINO, such as:
* `OpenVINO Python API Tutorial <../../../notebooks/002-openvino-api-with-output.html>`__
* `Basic image classification program with Hello Image Classification <../../../notebooks/001-hello-world-with-output.html>`__
* `Convert a PyTorch model and use it for image background removal <../../../notebooks/205-vision-background-removal-with-output.html>`__
* `OpenVINO Python API Tutorial <../../notebooks/002-openvino-api-with-output.html>`__
* `Basic image classification program with Hello Image Classification <../../notebooks/001-hello-world-with-output.html>`__
* `Convert a PyTorch model and use it for image background removal <../../notebooks/205-vision-background-removal-with-output.html>`__
.. tab-item:: Get started with C++
@ -307,8 +307,8 @@ Learn more about how to integrate a model in OpenVINO applications by trying out
Visit the :doc:`Samples <../../../learn-openvino/openvino-samples>` page for other C++ example applications to get you started with OpenVINO, such as:
* `Basic object detection with the Hello Reshape SSD C++ sample <../../../learn-openvino/openvino-samples/hello-reshape-ssd.html>`__
* `Object classification sample <../../../learn-openvino/openvino-samples/hello-classification.html>`__
* :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>`

22
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@ -149,36 +149,36 @@ If you have more than one OpenVINO™ version on your machine, you can easily sw
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 ../../../learn-openvino/interactive-tutorials-python.
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.
.. tab-set::
.. tab-item:: Get started with Python
:sync: get-started-py
Try the `Python Quick Start Example <../../../notebooks/201-vision-monodepth-with-output.html>`__ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.
Try the `Python Quick Start Example <../../notebooks/201-vision-monodepth-with-output.html>`__ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.
.. image:: https://user-images.githubusercontent.com/15709723/127752390-f6aa371f-31b5-4846-84b9-18dd4f662406.gif
:width: 400
Visit the :ref:`Tutorials <../../../learn-openvino/interactive-tutorials-python>` page for more Jupyter Notebooks to get you started with OpenVINO, such as:
Visit the :doc:`Tutorials <../../../learn-openvino/interactive-tutorials-python>` page for more Jupyter Notebooks to get you started with OpenVINO, such as:
* `OpenVINO Python API Tutorial <notebooks/002-openvino-api-with-output.html>`__
* `Basic image classification program with Hello Image Classification <notebooks/001-hello-world-with-output.html>`__
* `Convert a PyTorch model and use it for image background removal <notebooks/205-vision-background-removal-with-output.html>`__
* `OpenVINO Python API Tutorial <../../notebooks/002-openvino-api-with-output.html>`__
* `Basic image classification program with Hello Image Classification <../../notebooks/001-hello-world-with-output.html>`__
* `Convert a PyTorch model and use it for image background removal <../../notebooks/205-vision-background-removal-with-output.html>`__
.. tab-item:: Get started with C++
:sync: get-started-cpp
Try the `C++ Quick Start Example <../../../learn-openvino/openvino-samples/get-started-demos.html>`_ for step-by-step instructions on building and running a basic image classification C++ application.
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.
.. 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:
* `Basic object detection with the Hello Reshape SSD C++ sample <../../../learn-openvino/openvino-samples/hello-reshape-ssd.html>`_
* `Object classification sample <../../../learn-openvino/openvino-samples/hello-classification.html>`_
* :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>`
Uninstalling Intel® Distribution of OpenVINO™ Toolkit
#####################################################
@ -205,8 +205,8 @@ Additional Resources
* :doc:`Troubleshooting Guide for OpenVINO Installation & Configuration <../install-openvino>`
* Converting models for use with OpenVINO™: :ref:`Model Optimizer User Guide <deep learning model optimizer>`
* Writing your own OpenVINO™ applications: :ref:`OpenVINO™ Runtime User Guide <deep learning openvino runtime>`
* Sample applications: :ref:`OpenVINO™ Toolkit Samples Overview <code samples>`
* Pre-trained deep learning models: :ref:`Overview of OpenVINO™ Toolkit Pre-Trained Models <model zoo>`
* Sample applications: :doc:`OpenVINO™ Toolkit Samples Overview <../../../learn-openvino/openvino-samples>`
* Pre-trained deep learning models: :doc:`Overview of OpenVINO™ Toolkit Pre-Trained Models <../../../documentation/legacy-features/model-zoo>`
* IoT libraries and code samples in the GitHUB repository: `Intel® IoT Developer Kit <https://github.com/intel-iot-devkit>`__

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@ -52,7 +52,7 @@ System Requirements
.. important::
When installing Python, make sure you click the option **Add Python 3.x to PATH** to `add Python <https://docs.python.org/3/using/windows.html#installation-steps>`__ to your `PATH` environment variable.
When installing Python, make sure you click the option **Add Python 3.x to PATH** to `add Python <https://docs.python.org/3/using/windows.html#installation-steps>`__ to your `PATH` environment variable.
@ -139,7 +139,7 @@ to see if your case needs any of them.
The ``C:\Program Files (x86)\Intel\openvino_2023`` folder now contains the core components for OpenVINO.
If you used a different path in Step 1, you will find the ``openvino_2023`` folder there.
The path to the ``openvino_2023`` directory is also referred as ``<INSTALL_DIR>``
throughout the OpenVINO ../../../documentation.
throughout the OpenVINO documentation.
@ -187,36 +187,36 @@ You must update several environment variables before you can compile and run Ope
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 ../../../learn-openvino/interactive-tutorials-python.
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.
.. tab-set::
.. tab-item:: Get started with Python
:sync: get-started-py
Try the `Python Quick Start Example <../../../notebooks/201-vision-monodepth-with-output.html>`__ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.
Try the `Python Quick Start Example <../../notebooks/201-vision-monodepth-with-output.html>`__ to estimate depth in a scene using an OpenVINO monodepth model in a Jupyter Notebook inside your web browser.
.. image:: https://user-images.githubusercontent.com/15709723/127752390-f6aa371f-31b5-4846-84b9-18dd4f662406.gif
:width: 400
Visit the :ref:`Tutorials <notebook ../../../learn-openvino/interactive-tutorials-python>` page for more Jupyter Notebooks to get you started with OpenVINO, such as:
Visit the :doc:`Tutorials <../../../learn-openvino/interactive-tutorials-python>` page for more Jupyter Notebooks to get you started with OpenVINO, such as:
* `OpenVINO Python API Tutorial <notebooks/002-openvino-api-with-output.html>`__
* `Basic image classification program with Hello Image Classification <notebooks/001-hello-world-with-output.html>`__
* `Convert a PyTorch model and use it for image background removal <notebooks/205-vision-background-removal-with-output.html>`__
* `OpenVINO Python API Tutorial <../../notebooks/002-openvino-api-with-output.html>`__
* `Basic image classification program with Hello Image Classification <../../notebooks/001-hello-world-with-output.html>`__
* `Convert a PyTorch model and use it for image background removal <../../notebooks/205-vision-background-removal-with-output.html>`__
.. tab-item:: Get started with C++
:sync: get-started-cpp
Try the `C++ Quick Start Example <../../../learn-openvino/openvino-samples/get-started-demos.html>`_ for step-by-step instructions on building and running a basic image classification C++ application.
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.
.. 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:
* `Basic object detection with the Hello Reshape SSD C++ sample <../../../learn-openvino/openvino-samples/hello-reshape-ssd.html>`_
* `Object classification sample <../../../learn-openvino/openvino-samples/hello-classification.html>`_
* :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>`
.. _uninstall-from-windows:
@ -250,7 +250,7 @@ Additional Resources
* :doc:`Troubleshooting Guide for OpenVINO Installation & Configuration <../install-openvino>`
* Converting models for use with OpenVINO™: :ref:`Model Optimizer Developer Guide <deep learning model optimizer>`
* Writing your own OpenVINO™ applications: :ref:`OpenVINO™ Runtime User Guide <deep learning openvino runtime>`
* Sample applications: :ref:`OpenVINO™ Toolkit Samples Overview <code samples>`
* Pre-trained deep learning models: :ref:`Overview of OpenVINO™ Toolkit Pre-Trained Models <model zoo>`
* Sample applications: :doc:`OpenVINO™ Toolkit Samples Overview <../../../learn-openvino/openvino-samples>`
* Pre-trained deep learning models: :doc:`Overview of OpenVINO™ Toolkit Pre-Trained Models <../../../documentation/legacy-features/model-zoo>`
* IoT libraries and code samples in the GitHUB repository: `Intel® IoT Developer Kit <https://github.com/intel-iot-devkit>`__

2
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@ -99,7 +99,7 @@ Now that you've installed OpenVINO Runtime, you can try the following things:
* See pre-trained deep learning models in our :doc:`Open Model Zoo <../../../documentation/legacy-features/model-zoo>`.
* 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>`.
* Check out the OpenVINO product home page: https://software.intel.com/en-us/openvino-toolkit.
* Check out the OpenVINO `product home page <https://software.intel.com/en-us/openvino-toolkit>`__.

14
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@ -23,20 +23,20 @@ Install OpenVINO™ Runtime from Conan Package Manager
.. tab-item:: System Requirements
:sync: system-requirements
Full requirement listing is available in:
:doc:`System Requirements Page <../../../about-openvino/system-requirements>`
| Full requirement listing is available in:
| :doc:`System Requirements Page <../../../about-openvino/system-requirements>`
.. tab-item:: Processor Notes
:sync: processor-notes
To see if your processor includes the integrated graphics technology and supports iGPU inference, refer to:
`Product Specifications <https://ark.intel.com/content/www/us/en/ark.html>`__
| To see if your processor includes the integrated graphics technology and supports iGPU inference, refer to:
| `Product Specifications <https://ark.intel.com/content/www/us/en/ark.html>`__
.. tab-item:: Software
:sync: software
There are many ways to work with Conan Package Manager. Before you proceed, learn more about it on the
`Conan distribution page <https://conan.io/downloads>`__
| There are many ways to work with Conan Package Manager. Before you proceed, learn more about it on the
| `Conan distribution page <https://conan.io/downloads>`__
Installing OpenVINO Runtime with Conan Package Manager
############################################################
@ -93,6 +93,6 @@ Additional Resources
* To prepare your models for working with OpenVINO, see :doc:`Model Preparation <../../../openvino-workflow/model-preparation>`.
* 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>`.
* Check out the OpenVINO product `home page <https://software.intel.com/en-us/openvino-toolkit>`__.
* Check out the OpenVINO `product home page <https://software.intel.com/en-us/openvino-toolkit>`__.

10
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@ -37,8 +37,8 @@ Install OpenVINO™ Runtime from Conda Forge
.. tab-item:: Software
:sync: software
There are many ways to work with Conda. Before you proceed, learn more about it on the
`Anaconda distribution page <https://www.anaconda.com/products/individual/>`__
| There are many ways to work with Conda. Before you proceed, learn more about it on the
| `Anaconda distribution page <https://www.anaconda.com/products/individual/>`__
Installing OpenVINO Runtime with Anaconda Package Manager
@ -110,7 +110,7 @@ What's Next?
############################################################
Now that you've installed OpenVINO Runtime, you are ready to run your own machine learning applications!
To learn more about how to integrate a model in OpenVINO applications, try out some ../../../learn-openvino/interactive-tutorials-python and sample applications.
To learn more about how to integrate a model in OpenVINO applications, try out some tutorials and sample applications.
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.
@ -120,8 +120,8 @@ on building and running a basic image classification C++ application.
Visit the :doc:`Samples <../../../learn-openvino/openvino-samples>` page for other C++ example applications to get you started with OpenVINO, such as:
* `Basic object detection with the Hello Reshape SSD C++ sample <../../../learn-openvino/openvino-samples/hello-reshape-ssd.html>`__
* `Object classification sample <../../../learn-openvino/openvino-samples/hello-classification.html>`__
* :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>`

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@ -40,7 +40,7 @@ To start using Dockerfiles the following conditions must be met:
.. note::
OpenVINO's `Docker <https://docs.docker.com/>`__ and :doc:`Bare Metal <../install-openvino>`
distributions are identical, so the ../../../documentation applies to both.
distributions are identical, so the documentation applies to both.
.. note::

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@ -130,7 +130,7 @@ to see if your case needs any of them.
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 ../../../learn-openvino/interactive-tutorials-python.
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.
.. image:: https://user-images.githubusercontent.com/15709723/127752390-f6aa371f-31b5-4846-84b9-18dd4f662406.gif
:width: 400
@ -151,6 +151,6 @@ Visit the :doc:`Tutorials <../../../learn-openvino/interactive-tutorials-python>
Additional Resources
####################
- Intel® Distribution of OpenVINO™ toolkit home page: https://software.intel.com/en-us/openvino-toolkit
- Intel® Distribution of OpenVINO™ `toolkit home page <https://software.intel.com/en-us/openvino-toolkit>`__
- For IoT Libraries & Code Samples, see `Intel® IoT Developer Kit <https://github.com/intel-iot-devkit>`__.

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docs/articles_en/get-started/install-openvino/install-openvino-vcpkg.rst
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@ -104,7 +104,7 @@ Now that you've installed OpenVINO Runtime, you can try the following things:
* See pre-trained deep learning models in our :doc:`Open Model Zoo <../../../documentation/legacy-features/model-zoo>`.
* 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>`.
* Check out the OpenVINO product home page: https://software.intel.com/en-us/openvino-toolkit.
* Check out the OpenVINO `product home page <https://software.intel.com/en-us/openvino-toolkit>`__ .

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docs/articles_en/get-started/install-openvino/install-openvino-yum.rst
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@ -208,9 +208,9 @@ 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 ../../../learn-openvino/interactive-tutorials-python:
Learn more about how to integrate a model in OpenVINO applications by trying out the following tutorials:
* Try the `C++ Quick Start Example <../../../learn-openvino/openvino-samples/get-started-demos.html>`_
* 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.
.. image:: https://user-images.githubusercontent.com/36741649/127170593-86976dc3-e5e4-40be-b0a6-206379cd7df5.jpg
@ -218,8 +218,8 @@ Learn more about how to integrate a model in OpenVINO applications by trying out
* Visit the :ref:`Samples <code samples>` page for other C++ example applications to get you started with OpenVINO, such as:
* `Basic object detection with the Hello Reshape SSD C++ sample <../../../learn-openvino/openvino-samples/hello-reshape-ssd.html>`_
* `Object classification sample <../../../learn-openvino/openvino-samples/hello-classification.html>`_
* :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>`
You can also try the following things:
@ -228,7 +228,7 @@ You can also try the following things:
* See pre-trained deep learning models in our :doc:`Open Model Zoo <../../../documentation/legacy-features/model-zoo>`.
* 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>`.
* Take a glance at the OpenVINO product home page: https://software.intel.com/en-us/openvino-toolkit.
* Take a glance at the OpenVINO `product home page <https://software.intel.com/en-us/openvino-toolkit>`__ .