[DOCS] 2024.2 version update for 2024.2 (#24747)

Port from #24746 Docs version update to 2024.2
openvinotoolkit · May 29, 2024 · 00b6063 · 00b6063
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diff --git a/docs/articles_en/get-started/install-openvino/install-openvino-apt.rst b/docs/articles_en/get-started/install-openvino/install-openvino-apt.rst
@@ -155,7 +155,7 @@ Step 2: Install OpenVINO Runtime Using the APT Package Manager
          .. code-block:: sh
 
 
-            sudo apt install openvino-2024.0.0
+            sudo apt install openvino-2024.2.0
 
 .. note::
 
@@ -228,7 +228,7 @@ To uninstall OpenVINO Runtime via APT, run the following command based on your n
 
       .. code-block:: sh
 
-         sudo apt autoremove openvino-2024.0.0
+         sudo apt autoremove openvino-2024.2.0
 
 
 What's Next?

diff --git a/docs/articles_en/get-started/install-openvino/install-openvino-conan.rst b/docs/articles_en/get-started/install-openvino/install-openvino-conan.rst
@@ -52,7 +52,7 @@ Installing OpenVINO Runtime with Conan Package Manager
    .. code-block:: sh
 
       [requires]
-      openvino/2024.1.0
+      openvino/2024.2.0
       [generators]
       CMakeDeps
       CMakeToolchain

diff --git a/docs/articles_en/get-started/install-openvino/install-openvino-conda.rst b/docs/articles_en/get-started/install-openvino/install-openvino-conda.rst
@@ -64,7 +64,7 @@ Installing OpenVINO Runtime with Anaconda Package Manager
 
    .. code-block:: sh
 
-      conda install -c conda-forge openvino=2024.1.0
+      conda install -c conda-forge openvino=2024.2.0
 
 Congratulations! You've just Installed OpenVINO! For some use cases you may still
 need to install additional components. Check the description below, as well as the
@@ -115,7 +115,7 @@ with the proper OpenVINO version number:
 
 .. code-block:: sh
 
-   conda remove openvino=2024.1.0
+   conda remove openvino=2024.2.0
 
 What's Next?
 ############################################################

diff --git a/docs/articles_en/get-started/install-openvino/install-openvino-yum.rst b/docs/articles_en/get-started/install-openvino/install-openvino-yum.rst
@@ -128,7 +128,7 @@ Install OpenVINO Runtime
       .. code-block:: sh
 
 
-         sudo yum install openvino-2024.0.0
+         sudo yum install openvino-2024.2.0
 
 
 
@@ -199,7 +199,7 @@ To uninstall OpenVINO Runtime via YUM, run the following command based on your n
 
       .. code-block:: sh
 
-         sudo yum autoremove openvino-2024.0.0
+         sudo yum autoremove openvino-2024.2.0
 
 
 

diff --git a/docs/articles_en/get-started/install-openvino/install-openvino-zypper.rst b/docs/articles_en/get-started/install-openvino/install-openvino-zypper.rst
@@ -143,7 +143,7 @@ To uninstall OpenVINO Runtime via ZYPPER, run the following command based on you
 
       .. code-block:: sh
 
-         sudo zypper remove *openvino-2024.0.0*
+         sudo zypper remove *openvino-2024.2.0*
 
 
 

diff --git a/docs/articles_en/learn-openvino/openvino-samples/bert-benchmark.rst b/docs/articles_en/learn-openvino/openvino-samples/bert-benchmark.rst
@@ -9,7 +9,7 @@ Bert Benchmark Python Sample
 
 
 This sample demonstrates how to estimate performance of a Bert model using Asynchronous
-Inference Request API. Unlike `demos <https://docs.openvino.ai/nightly/omz_demos.html>`__ this sample does not have
+Inference Request API. Unlike `demos <https://docs.openvino.ai/2024/omz_demos.html>`__ this sample does not have
 configurable command line arguments. Feel free to modify sample's source code to
 try out different options.
 

diff --git a/docs/articles_en/learn-openvino/openvino-samples/get-started-demos.rst b/docs/articles_en/learn-openvino/openvino-samples/get-started-demos.rst
@@ -264,7 +264,7 @@ You need a model that is specific for your inference task. You can get it from o
 Convert the Model
 --------------------
 
-If Your model requires conversion, check the `article <https://docs.openvino.ai/2023.3/openvino_docs_../../get-started_../../get-started_demos.html>`__ for information how to do it.
+If Your model requires conversion, check the `article <https://docs.openvino.ai/2024/learn-openvino/openvino-samples/get-started-demos.html>`__ for information how to do it.
 
 .. _download-media:
 

diff --git a/...articles_en/learn-openvino/openvino-samples/hello-nv12-input-classification.rst b/...articles_en/learn-openvino/openvino-samples/hello-nv12-input-classification.rst
@@ -211,6 +211,6 @@ Additional Resources
 - :doc:`Get Started with Samples <get-started-demos>`
 - :doc:`Using OpenVINO Samples <../openvino-samples>`
 - :doc:`Convert a Model <../../documentation/legacy-features/transition-legacy-conversion-api/legacy-conversion-api>`
-- `API Reference <https://docs.openvino.ai/2023.2/api/api_reference.html>`__
+- `API Reference <https://docs.openvino.ai/2024/api/api_reference.html>`__
 - `Hello NV12 Input Classification C++ Sample on Github <https://github.com/openvinotoolkit/openvino/blob/master/samples/cpp/hello_nv12_input_classification/README.md>`__
 - `Hello NV12 Input Classification C Sample on Github <https://github.com/openvinotoolkit/openvino/blob/master/samples/c/hello_nv12_input_classification/README.md>`__
diff --git a/docs/articles_en/learn-openvino/openvino-samples/sync-benchmark.rst b/docs/articles_en/learn-openvino/openvino-samples/sync-benchmark.rst
@@ -11,7 +11,7 @@ Sync Benchmark Sample
 This sample demonstrates how to estimate performance of a model using Synchronous
 Inference Request API. It makes sense to use synchronous inference only in latency
 oriented scenarios. Models with static input shapes are supported. Unlike
-`demos <https://docs.openvino.ai/nightly/omz_demos.html>`__ this sample does not have other configurable command-line
+`demos <https://docs.openvino.ai/2024/omz_demos.html>`__ this sample does not have other configurable command-line
 arguments. Feel free to modify sample's source code to try out different options.
 Before using the sample, refer to the following requirements:
 

diff --git a/docs/articles_en/learn-openvino/openvino-samples/throughput-benchmark.rst b/docs/articles_en/learn-openvino/openvino-samples/throughput-benchmark.rst
@@ -9,7 +9,7 @@ Throughput Benchmark Sample
 
 
 This sample demonstrates how to estimate performance of a model using Asynchronous
-Inference Request API in throughput mode. Unlike `demos <https://docs.openvino.ai/nightly/omz_demos.html>`__ this sample
+Inference Request API in throughput mode. Unlike `demos <https://docs.openvino.ai/2024/omz_demos.html>`__ this sample
 does not have other configurable command-line arguments. Feel free to modify sample's
 source code to try out different options.
 

diff --git a/...penvino-workflow/running-inference/optimize-inference/general-optimizations.rst b/...penvino-workflow/running-inference/optimize-inference/general-optimizations.rst
@@ -62,7 +62,7 @@ Below are example-codes for the regular and async-based approaches to compare:
 
 
 The technique can be generalized to any available parallel slack. For example, you can do inference and simultaneously encode the resulting or previous frames or run further inference, like emotion detection on top of the face detection results.
-Refer to the `Object Detection C++ Demo <https://docs.openvino.ai/2023.3/omz_demos_object_detection_demo_cpp.html>`__ , `Object Detection Python Demo <https://docs.openvino.ai/2023.3/omz_demos_object_detection_demo_python.html>`__ (latency-oriented Async API showcase) and :doc:`Benchmark App Sample <../../../learn-openvino/openvino-samples/benchmark-tool>` for complete examples of the Async API in action.
+Refer to the `Object Detection C++ Demo <https://docs.openvino.ai/2024/omz_demos_object_detection_demo_cpp.html>`__ , `Object Detection Python Demo <https://docs.openvino.ai/2024/omz_demos_object_detection_demo_python.html>`__ (latency-oriented Async API showcase) and :doc:`Benchmark App Sample <../../../learn-openvino/openvino-samples/benchmark-tool>` for complete examples of the Async API in action.
 
 .. note::
 

diff --git a/docs/dev/pypi_publish/pypi-openvino-dev.md b/docs/dev/pypi_publish/pypi-openvino-dev.md
@@ -116,10 +116,10 @@ For example, to install and configure the components for working with TensorFlow
 
 **In addition, the openvino-dev package installs the following components by default:**
 
-| Component        | Console Script                                                                   | Description                                                                                                                                                                                                                                                                                                   |
+| Component        | Console Script                                                                  | Description                                                                                                                                                                                                                                                                                                   |
 |------------------|---------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [Legacy Model conversion API](https://docs.openvino.ai/nightly/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html) | `mo` |**Model conversion API** 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\*.                                               |                                         |
-| [Model Downloader and other Open Model Zoo tools](https://docs.openvino.ai/nightly/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 conversion API. 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 conversion API. <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.                                          |
+| [Legacy Model conversion API](https://docs.openvino.ai/2024/documentation/legacy-features/transition-legacy-conversion-api/legacy-conversion-api.html) | `mo` |**Model conversion API** 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\*.                                               |                                         |
+| [Model Downloader and other Open Model Zoo tools](https://docs.openvino.ai/2024/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 conversion API. 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 conversion API. <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
 

diff --git a/docs/home.rst b/docs/home.rst
@@ -1,5 +1,5 @@
 ============================
-OpenVINO 2024.1
+OpenVINO 2024.2
 ============================
 
 .. meta::

diff --git a/docs/sphinx_setup/conf.py b/docs/sphinx_setup/conf.py
@@ -18,7 +18,7 @@
 author = 'Intel®'
 
 language = 'en'
-version_name = 'nightly'
+version_name = '2024'
 
 # -- General configuration ---------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration

diff --git a/samples/cpp/benchmark/sync_benchmark/README.md b/samples/cpp/benchmark/sync_benchmark/README.md
@@ -8,14 +8,12 @@ For more detailed information on how this sample works, check the dedicated [art
 
 | Options                        | Values                                                                                                                   |
 | -------------------------------| -------------------------------------------------------------------------------------------------------------------------|
-| Validated Models               | [alexnet](https://docs.openvino.ai/nightly/omz_models_model_alexnet.html),                                               |
-|                                | [googlenet-v1](https://docs.openvino.ai/nightly/omz_models_model_googlenet_v1.html),                                     |
-|                                | [yolo-v3-tf](https://docs.openvino.ai/nightly/omz_models_model_yolo_v3_tf.html),                                         |
-|                                | [face-detection-0200](https://docs.openvino.ai/nightly/omz_models_model_face_detection_0200.html)                        |
+| Validated Models               | [yolo-v3-tf](https://docs.openvino.ai/2024/omz_models_model_yolo_v3_tf.html),                                            |
+|                                | [face-detection-0200](https://docs.openvino.ai/2024/omz_models_model_face_detection_0200.html)                           |
 | Model Format                   | OpenVINO™ toolkit Intermediate Representation                                                                            |
 |                                | (\*.xml + \*.bin), ONNX (\*.onnx)                                                                                        |
-| Supported devices              | [All](https://docs.openvino.ai/2024/about-openvino/compatibility-and-support/supported-devices.html)                      |
-| Other language realization     | [Python](https://docs.openvino.ai/2024/learn-openvino/openvino-samples/sync-benchmark.html)                                            |
+| Supported devices              | [All](https://docs.openvino.ai/2024/about-openvino/compatibility-and-support/supported-devices.html)                     |
+| Other language realization     | [Python](https://docs.openvino.ai/2024/learn-openvino/openvino-samples/sync-benchmark.html)                              |
 
 The following C++ API is used in the application:
 

diff --git a/samples/cpp/benchmark/throughput_benchmark/README.md b/samples/cpp/benchmark/throughput_benchmark/README.md
@@ -10,14 +10,12 @@ For more detailed information on how this sample works, check the dedicated [art
 
 | Options                     | Values                                                                                                                         |
 | ----------------------------| -------------------------------------------------------------------------------------------------------------------------------|
-| Validated Models            | [alexnet](https://docs.openvino.ai/nightly/omz_models_model_alexnet.html),                                                     |
-|                             | [googlenet-v1](https://docs.openvino.ai/nightly/omz_models_model_googlenet_v1.html),                                           |
-|                             | [yolo-v3-tf](https://docs.openvino.ai/nightly/omz_models_model_yolo_v3_tf.html),                                               |
-|                             | [face-detection-](https://docs.openvino.ai/nightly/omz_models_model_face_detection_0200.html)                                  |
+| Validated Models            | [yolo-v3-tf](https://docs.openvino.ai/2024/omz_models_model_yolo_v3_tf.html),                                                  |
+|                             | [face-detection-](https://docs.openvino.ai/2024/omz_models_model_face_detection_0200.html)                                     |
 | Model Format                | OpenVINO™ toolkit Intermediate Representation                                                                                  |
 |                             | (\*.xml + \*.bin), ONNX (\*.onnx)                                                                                              |
-| Supported devices           | [All](https://docs.openvino.ai/2024/about-openvino/compatibility-and-support/supported-devices.html)                            |
-| Other language realization  | [Python](https://docs.openvino.ai/2024/learn-openvino/openvino-samples/throughput-benchmark.html)                                            |
+| Supported devices           | [All](https://docs.openvino.ai/2024/about-openvino/compatibility-and-support/supported-devices.html)                           |
+| Other language realization  | [Python](https://docs.openvino.ai/2024/learn-openvino/openvino-samples/throughput-benchmark.html)                              |
 
 The following C++ API is used in the application:
 

diff --git a/samples/cpp/hello_reshape_ssd/README.md b/samples/cpp/hello_reshape_ssd/README.md
@@ -9,10 +9,10 @@ For more detailed information on how this sample works, check the dedicated [art
 
 | Options                     | Values                                                                                                                                   |
 | ----------------------------| -----------------------------------------------------------------------------------------------------------------------------------------|
-| Validated Models            | [person-detection-retail-0013](https://docs.openvino.ai/nightly/omz_models_model_person_detection_retail_0013.html)                      |
+| Validated Models            | [person-detection-retail-0013](https://docs.openvino.ai/2024/omz_models_model_person_detection_retail_0013.html)                         |
 | Model Format                | OpenVINO™ toolkit Intermediate Representation (\*.xml + \*.bin), ONNX (\*.onnx)                                                          |
-| Supported devices           | [All](https://docs.openvino.ai/2024/about-openvino/compatibility-and-support/supported-devices.html)                                      |
-| Other language realization  | [Python](https://docs.openvino.ai/2024/learn-openvino/openvino-samples/hello-reshape-ssd.html)               |
+| Supported devices           | [All](https://docs.openvino.ai/2024/about-openvino/compatibility-and-support/supported-devices.html)                                     |
+| Other language realization  | [Python](https://docs.openvino.ai/2024/learn-openvino/openvino-samples/hello-reshape-ssd.html)                                           |
 
 The following C++ API is used in the application:
 

diff --git a/src/frontends/tensorflow/README.md b/src/frontends/tensorflow/README.md
@@ -31,7 +31,6 @@ flowchart BT
 ```
 
 The MO tool and model conversion API now use the TensorFlow Frontend as the default path for conversion to IR.
-Known limitations of TF FE are described [here](https://docs.openvino.ai/nightly/openvino_docs_MO_DG_TensorFlow_Frontend.html).
 
 ## Key contacts