Update pypi.org pages (openvinotoolkit#12170)

* fix references

* update links

* update the wording to be more clear

* add the error message about Visual studio back

* update links to static html links of 2022.2

docs/install_guides/pypi-openvino-dev.md

@@ -64,7 +64,7 @@ There are two options to install OpenVINO Development Tools:
 
 #### Installing Default Components
 
-To install [default components in the package](#whats-in-the-package), use the following command:
+To install the default components in the package (see the [What's in the Package](#whats-in-the-package) section of this article), use the following command:
 ```
 pip install openvino-dev
 ```
@@ -107,6 +107,8 @@ For example, to install and configure the components for working with TensorFlow
    ```
    If installation was successful, you will not see any error messages (no console output).
 
+<a name="whats-in-the-package"></a>
+
 ## What's in the Package?
 
 > **NOTE**: The openvino-dev package installs [OpenVINO™ Runtime](https://pypi.org/project/openvino) as a dependency, which is the engine that runs the deep learning model and includes a set of libraries for an easy inference integration into your applications.  
@@ -115,15 +117,15 @@ For example, to install and configure the components for working with TensorFlow
 
 | Component        | Console Script                                                                   | Description                                                                                                                                                                                                                                                                                                   |  
 |------------------|---------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [Model Optimizer](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) | `mo` |**Model Optimizer** imports, converts, and optimizes models that were trained in popular frameworks to a format usable by OpenVINO components. <br>Supported frameworks include Caffe\*, TensorFlow\*, MXNet\*, PaddlePaddle\*, and ONNX\*.                                               |
-| [Benchmark Tool](../../tools/benchmark_tool/README.md)| `benchmark_app` | **Benchmark Application** allows you to estimate deep learning inference performance on supported devices for synchronous and asynchronous modes.                                              |
-| [Accuracy Checker](@ref omz_tools_accuracy_checker) and <br> [Annotation Converter](@ref omz_tools_accuracy_checker_annotation_converters) | `accuracy_check` <br> `convert_annotation` |**Accuracy Checker**  is a deep learning accuracy validation tool that allows you to collect accuracy metrics against popular datasets. The main advantages of the tool are the flexibility of configuration and a set of supported datasets, preprocessing, postprocessing, and metrics. <br> **Annotation Converter** is a utility that prepares datasets for evaluation with Accuracy Checker.                                             |
-| [Post-Training Optimization Tool](../../tools/pot/docs/pot_introduction.md)| `pot` |**Post-Training Optimization Tool** allows you to optimize trained models with advanced capabilities, such as quantization and low-precision optimizations, without the need to retrain or fine-tune models.                                            |
-| [Model Downloader and other Open Model Zoo tools](@ref omz_tools_downloader)| `omz_downloader` <br> `omz_converter` <br> `omz_quantizer` <br> `omz_info_dumper`| **Model Downloader** is a tool for getting access to the collection of high-quality and extremely fast pre-trained deep learning [public](@ref omz_models_group_public) and [Intel](@ref omz_models_group_intel)-trained models. These free pre-trained models can be used to speed up the development and production deployment process without training your own models. The tool downloads model files from online sources and, if necessary, patches them to make them more usable with Model Optimizer. A number of additional tools are also provided to automate the process of working with downloaded models:<br> **Model Converter** is a tool for converting Open Model Zoo models that are stored in an original deep learning framework format into the OpenVINO Intermediate Representation (IR) using Model Optimizer. <br> **Model Quantizer** is a tool for automatic quantization of full-precision models in the IR format into low-precision versions using the Post-Training Optimization Tool. <br> **Model Information Dumper** is a helper utility for dumping information about the models to a stable, machine-readable format.                                          |      
+| [Model Optimizer](https://docs.openvino.ai/2022.2/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html) | `mo` |**Model Optimizer** imports, converts, and optimizes models that were trained in popular frameworks to a format usable by OpenVINO components. <br>Supported frameworks include Caffe\*, TensorFlow\*, MXNet\*, PaddlePaddle\*, and ONNX\*.                                               |
+| [Benchmark Tool](https://docs.openvino.ai/2022.2/openvino_inference_engine_tools_benchmark_tool_README.html)| `benchmark_app` | **Benchmark Application** allows you to estimate deep learning inference performance on supported devices for synchronous and asynchronous modes.                                              |
+| [Accuracy Checker](https://docs.openvino.ai/2022.2/omz_tools_accuracy_checker.html) and <br> [Annotation Converter](https://docs.openvino.ai/2022.2/omz_tools_accuracy_checker_annotation_converters.html) | `accuracy_check` <br> `convert_annotation` |**Accuracy Checker**  is a deep learning accuracy validation tool that allows you to collect accuracy metrics against popular datasets. The main advantages of the tool are the flexibility of configuration and a set of supported datasets, preprocessing, postprocessing, and metrics. <br> **Annotation Converter** is a utility that prepares datasets for evaluation with Accuracy Checker.                                             |
+| [Post-Training Optimization Tool](https://docs.openvino.ai/2022.2/pot_introduction.html)| `pot` |**Post-Training Optimization Tool** allows you to optimize trained models with advanced capabilities, such as quantization and low-precision optimizations, without the need to retrain or fine-tune models.                                            |
+| [Model Downloader and other Open Model Zoo tools](https://docs.openvino.ai/2022.2/omz_tools_downloader.html)| `omz_downloader` <br> `omz_converter` <br> `omz_quantizer` <br> `omz_info_dumper`| **Model Downloader** is a tool for getting access to the collection of high-quality and extremely fast pre-trained deep learning [public](@ref omz_models_group_public) and [Intel](@ref omz_models_group_intel)-trained models. These free pre-trained models can be used to speed up the development and production deployment process without training your own models. The tool downloads model files from online sources and, if necessary, patches them to make them more usable with Model Optimizer. A number of additional tools are also provided to automate the process of working with downloaded models:<br> **Model Converter** is a tool for converting Open Model Zoo models that are stored in an original deep learning framework format into the OpenVINO Intermediate Representation (IR) using Model Optimizer. <br> **Model Quantizer** is a tool for automatic quantization of full-precision models in the IR format into low-precision versions using the Post-Training Optimization Tool. <br> **Model Information Dumper** is a helper utility for dumping information about the models to a stable, machine-readable format.                                          |      
 
 ## Troubleshooting
 
-For general troubleshooting steps and issues, see [Troubleshooting Guide for OpenVINO Installation](./troubleshooting.md). The following sections also provide explanations to several error messages. 
+For general troubleshooting steps and issues, see [Troubleshooting Guide for OpenVINO Installation](https://docs.openvino.ai/2022.2/openvino_docs_get_started_guide_troubleshooting.html). The following sections also provide explanations to several error messages. 
 
 ### zsh: no matches found : openvino-dev[...]
 
@@ -146,9 +148,9 @@ To avoid such issues you can also disable globbing for PIP commands by defining
 alias pip='noglob pip'
 ```
 
-<!--### Error: Microsoft Visual C++ 14.0 is required. Get it with "Build Tools for Visual Studio"
+### Error: Microsoft Visual C++ 14.0 is required. Get it with "Build Tools for Visual Studio"
 
-On Windows* some dependencies may require compilation from source when installing. To resolve this issue, you need to install [Build Tools for Visual Studio* 2019](https://visualstudio.microsoft.com/downloads/#build-tools-for-visual-studio-2019) and repeat package installation.-->
+On Windows* some dependencies may require compilation from source when installing. To resolve this issue, you need to install [Build Tools for Visual Studio* 2019](https://visualstudio.microsoft.com/downloads/#build-tools-for-visual-studio-2019) and repeat package installation.
 
 ### ImportError: libpython3.7m.so.1.0: cannot open shared object file: No such file or directory
 
@@ -160,6 +162,7 @@ sudo apt-get install libpython3.7
 ## Additional Resources
 
 - [Intel® Distribution of OpenVINO™ toolkit](https://software.intel.com/en-us/openvino-toolkit)
+- [OpenVINO™ Documentation](https://docs.openvino.ai/)
 - [OpenVINO™ Notebooks](https://github.com/openvinotoolkit/openvino_notebooks)
 
 Copyright © 2018-2022 Intel Corporation

docs/install_guides/pypi-openvino-rt.md

@@ -2,7 +2,7 @@
 
 Intel® Distribution of OpenVINO™ toolkit is an open-source toolkit for optimizing and deploying AI inference. It can be used to develop applications and solutions based on deep learning tasks, such as: emulation of human vision, automatic speech recognition, natural language processing, recommendation systems, etc. It provides high-performance and rich deployment options, from edge to cloud.
 
-If you have already finished developing your models and converting them to the OpenVINO model format, you can install OpenVINO Runtime to deploy your applications on various devices. The [OpenVINO™ Runtime](../OV_Runtime_UG/openvino_intro.md) Python package includes a set of libraries for an easy inference integration with your products.
+If you have already finished developing your models and converting them to the OpenVINO model format, you can install OpenVINO Runtime to deploy your applications on various devices. The [OpenVINO™ Runtime](https://docs.openvino.ai/2022.2/openvino_docs_OV_UG_OV_Runtime_User_Guide.html) Python package includes a set of libraries for an easy inference integration with your products.
 
 ## System Requirements
 Before you start the installation, check the supported operating systems and required Python* versions. The complete list of supported hardware is available in the [Release Notes](https://www.intel.com/content/www/us/en/developer/articles/release-notes/openvino-relnotes.html).
@@ -76,11 +76,11 @@ If installation was successful, you will not see any error messages (no console
 
 ## Troubleshooting
 
-For general troubleshooting steps and issues, see [Troubleshooting Guide for OpenVINO Installation](./troubleshooting.md). The following sections also provide explanations to several error messages. 
+For general troubleshooting steps and issues, see [Troubleshooting Guide for OpenVINO Installation](https://docs.openvino.ai/2022.2/openvino_docs_get_started_guide_troubleshooting.html). The following sections also provide explanations to several error messages. 
 
-<!--### Error: Microsoft Visual C++ 14.0 is required. Get it with "Build Tools for Visual Studio"
+### Error: Microsoft Visual C++ 14.0 is required. Get it with "Build Tools for Visual Studio"
 
-On Windows* some dependencies may require compilation from source when installing. To resolve this issue, you need to install [Build Tools for Visual Studio* 2019](https://visualstudio.microsoft.com/downloads/#build-tools-for-visual-studio-2019) and repeat package installation.-->
+On Windows* some dependencies may require compilation from source when installing. To resolve this issue, you need to install [Build Tools for Visual Studio* 2019](https://visualstudio.microsoft.com/downloads/#build-tools-for-visual-studio-2019) and repeat package installation.
 
 ### ImportError: libpython3.7m.so.1.0: cannot open shared object file: No such file or directory
 
@@ -92,6 +92,7 @@ sudo apt-get install libpython3.7
 ## Additional Resources
 
 - [Intel® Distribution of OpenVINO™ toolkit](https://software.intel.com/en-us/openvino-toolkit)
+- [OpenVINO™ Documentation](https://docs.openvino.ai/)
 - [OpenVINO™ Notebooks](https://github.com/openvinotoolkit/openvino_notebooks)
 
 Copyright © 2018-2022 Intel Corporation