[DOCS] Add legacy notes pot (#21449)

* add note to pot

* change header

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool.rst

@@ -1,6 +1,6 @@
 .. {#pot_introduction}
 
-(Deprecated) Post-training Quantization with POT
+[Deprecated] Post-training Quantization with POT
 ================================================
 
 
@@ -14,15 +14,15 @@
    API Reference <pot_compression_api_README>
    Command-line Interface <pot_compression_cli_README>
    Examples <pot_examples_description>
-   pot_docs_FrequentlyAskedQuestions
+   Post-training Optimization Tool FAQ <pot_docs_FrequentlyAskedQuestions>
    (Experimental) Protecting Model <pot_ranger_README>
 
 
 
 
-.. note:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
 
-For the needs of post-training optimization, OpenVINO&trade; provides a **Post-training Optimization Tool (POT)** 
+For the needs of post-training optimization, OpenVINO provides a **Post-training Optimization Tool (POT)** 
 which supports the **uniform integer quantization** method. This method allows moving from floating-point precision 
 to integer precision (for example, 8-bit) for weights and activations during inference time. It helps to reduce 
 the model size, memory footprint and latency, as well as improve the computational efficiency, using integer arithmetic. 

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/pot_api_reference.rst

@@ -1,7 +1,9 @@
 .. {#pot_compression_api_README}
 
-API Reference
-=============
+[Deprecated] API Reference
+=================================
+
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
 
 
 Post-training Optimization Tool API provides a full set of interfaces and helpers that allow users to implement a custom optimization pipeline for various types of DL models including cascaded or compound models. Below is a full specification of this API:

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/pot_cli.rst

@@ -1,7 +1,7 @@
 .. {#pot_compression_cli_README}
 
-Use Post-Training Optimization Tool Command-Line Interface (Model Zoo flow)
-===========================================================================
+[Deprecated] Use Post-Training Optimization Tool Command-Line Interface (Model Zoo flow)
+====================================================================================================
 
 
 .. toctree::
@@ -9,9 +9,10 @@ Use Post-Training Optimization Tool Command-Line Interface (Model Zoo flow)
    :hidden:
 
    Simplified Mode <pot_docs_simplified_mode>
-   pot_configs_README
+   Configuration File Description  <pot_configs_README>
 
 
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
 
 Introduction
 ####################

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/pot_cli/configuration_file_description.rst

@@ -1,7 +1,9 @@
 .. {#pot_configs_README}
 
-Configuration File Description
-==============================
+[Deprecated] Configuration File Description
+==============================================
+
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
 
 
 The tool is designed to work with the configuration file where all the parameters required for the optimization are specified. These parameters are organized as a dictionary and stored in

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/pot_cli/simplified_mode.rst

@@ -1,7 +1,9 @@
 .. {#pot_docs_simplified_mode}
 
-Optimization with Simplified Mode
-=================================
+[Deprecated] Optimization with Simplified Mode
+====================================================
+
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
 
 
 Introduction

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/pot_examples.rst

@@ -1,7 +1,7 @@
 .. {#pot_examples_description}
 
-Examples
-========
+[Deprecated] Examples
+========================
 
 
 .. toctree::
@@ -11,6 +11,7 @@ Examples
    API Examples <pot_example_README>
    Command-line Example <pot_configs_examples_README>
 
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
 
 This section provides a set of examples that demonstrate how to apply the post-training optimization methods to optimize various models from different domains. It contains optimization recipes for concrete models, that unnecessarily cover your case, but which should be sufficient to reuse these recipes to optimize custom models:
 

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/pot_examples/pot_api_examples.rst

@@ -1,7 +1,7 @@
 .. {#pot_example_README}
 
-Post-training Optimization Tool API Examples
-============================================
+[Deprecated] Post-training Optimization Tool API Examples
+===============================================================
 
 
 .. toctree::
@@ -15,6 +15,8 @@ Post-training Optimization Tool API Examples
    Quantizing 3D Segmentation Model <pot_example_3d_segmentation_README>
    Quantizing for GNA Device <pot_example_speech_README>
 
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
+
 
 The Post-training Optimization Tool contains multiple examples that demonstrate how to use its :doc:`API <pot_compression_api_README>` 
 to optimize DL models. All available examples can be found on `GitHub <https://github.com/openvinotoolkit/openvino/tree/master/tools/pot/openvino/tools/pot/api/samples>`__.

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/pot_examples/pot_api_examples/pot_example_3d_segmentation.rst

@@ -1,7 +1,9 @@
 .. {#pot_example_3d_segmentation_README}
 
-Quantizing 3D Segmentation Model
-================================
+[Deprecated] Quantizing 3D Segmentation Model
+================================================================
+
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
 
 
 This example demonstrates the use of the :doc:`Post-training Optimization Tool API <pot_compression_api_README>` for the task of quantizing a 3D segmentation model.

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/pot_examples/pot_api_examples/pot_example_classification.rst

@@ -1,7 +1,9 @@
 .. {#pot_example_classification_README}
 
-Quantizing Image Classification Model
-=====================================
+[Deprecated] Quantizing Image Classification Model
+========================================================
+
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
 
 
 This example demonstrates the use of the :doc:`Post-training Optimization Tool API <pot_compression_api_README>` for the task of quantizing a classification model.

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/pot_examples/pot_api_examples/pot_example_face_detection.rst

@@ -1,7 +1,9 @@
 .. {#pot_example_face_detection_README}
 
-Quantizing Cascaded Face detection Model
-========================================
+[Deprecated] Quantizing Cascaded Face detection Model
+============================================================
+
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
 
 
 This example demonstrates the use of the :doc:`Post-training Optimization Tool API <pot_compression_api_README>` for the task of quantizing a face detection model.

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/pot_examples/pot_api_examples/pot_example_object_detection.rst

@@ -1,7 +1,9 @@
 .. {#pot_example_object_detection_README}
 
-Quantizing Object Detection Model with Accuracy Control
-=======================================================
+[Deprecated] Quantizing Object Detection Model with Accuracy Control
+================================================================================
+
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
 
 
 This example demonstrates the use of the :doc:`Post-training Optimization Toolkit API <pot_compression_api_README>` to quantize an object detection model in the :doc:`accuracy-aware mode <accuracy_aware_README>`. The `MobileNetV1 FPN <https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/ssd_mobilenet_v1_fpn_coco>`__ model from TensorFlow for object detection task is used for this purpose. A custom ``DataLoader`` is created to load the `COCO <https://cocodataset.org/>`__ dataset for object detection task and the implementation of mAP COCO is used for the model evaluation. The code of the example is available on `GitHub <https://github.com/openvinotoolkit/openvino/tree/master/tools/pot/openvino/tools/pot/api/samples/object_detection>`__.

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/pot_examples/pot_api_examples/pot_example_segmentation.rst

@@ -1,7 +1,9 @@
 .. {#pot_example_segmentation_README}
 
-Quantizing Semantic Segmentation Model
-======================================
+[Deprecated] Quantizing Semantic Segmentation Model
+=============================================================
+
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
 
 
 This example demonstrates the use of the :doc:`Post-training Optimization Tool API <pot_compression_api_README>` for the task of quantizing a segmentation model.

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/pot_examples/pot_api_examples/pot_example_speech.rst

@@ -1,7 +1,9 @@
 .. {#pot_example_speech_README}
 
-Quantizing for GNA Device
-=========================
+[Deprecated] Quantizing for GNA Device
+=========================================
+
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
 
 
 This example demonstrates the use of the :doc:`Post-training Optimization Tool API <pot_compression_api_README>` for the task of quantizing a speech model for :doc:`GNA <openvino_docs_OV_UG_supported_plugins_GNA>` device. Quantization for GNA is different from CPU quantization due to device specifics: GNA supports quantized inputs in INT16 and INT32 (for activations) precision and quantized weights in INT8 and INT16 precision.

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/pot_examples/pot_cli_example.rst

@@ -1,7 +1,9 @@
 .. {#pot_configs_examples_README}
 
-End-to-end Command-line Interface Example
-=========================================
+[Deprecated] End-to-end Command-line Interface Example
+=========================================================
+
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
 
 
 This tutorial describes an example of running post-training quantization for the **MobileNet v2 model from PyTorch** framework, 

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/pot_faq.rst

@@ -1,10 +1,10 @@
 .. {#pot_docs_FrequentlyAskedQuestions}
 
-Post-training Optimization Tool FAQ
-===================================
+[Deprecated] Post-training Optimization Tool FAQ
+===========================================================
 
 
-.. note:: 
+.. danger:: 
 
    Post-training Optimization Tool has been deprecated since OpenVINO 2023.0. 
    :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for post-training quantization instead.

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/protecting_model.rst

@@ -1,8 +1,12 @@
 .. {#pot_ranger_README}
 
-Experimental: Protecting Deep Learning Model through Range Supervision ("RangeSupervision")
-===========================================================================================
+[Deprecated] Experimental: Protecting Deep Learning Model through Range Supervision ("RangeSupervision")
+================================================================================================================
 
+.. danger:: 
+
+   Post-training Optimization Tool has been deprecated since OpenVINO 2023.0. 
+   :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for post-training quantization instead.
 
 Introduction
 ####################

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/quantization_best_practices.rst

@@ -1,7 +1,7 @@
 .. {#pot_docs_BestPractices}
 
-Post-Training Quantization Best Practices
-=========================================
+[Deprecated] Post-Training Quantization Best Practices
+========================================================
 
 
 .. toctree::
@@ -10,6 +10,7 @@ Post-Training Quantization Best Practices
 
    Saturation Issue <pot_saturation_issue>
 
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
 
 The :doc:`Default Quantization <pot_default_quantization_usage>` of the Post-training Optimization Tool (POT) is
 the fastest and easiest way to get a quantized model. It requires only some unannotated representative dataset to be provided in most cases. Therefore, it is recommended to use it as a starting point when it comes to model optimization. However, it can lead to significant accuracy deviation in some cases. The purpose of this article is to provide tips to address this issue.

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/quantization_best_practices/saturation_issue.rst

@@ -1,7 +1,9 @@
 .. {#pot_saturation_issue}
 
-Saturation (overflow) Issue Workaround
-======================================
+[Deprecated] Saturation (overflow) Issue Workaround
+=======================================================
+
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
 
 
 Introduction

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/quantizing_models.rst

@@ -1,7 +1,7 @@
 .. {#pot_default_quantization_usage}
 
-Quantizing Models
-=================
+[Deprecated] Quantizing Models
+======================================
 
 
 
@@ -12,6 +12,8 @@ Quantizing Models
    DefaultQuantization Method <pot_compression_algorithms_quantization_default_README>
 
 
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
+
 This guide describes how to apply model quantization with the Default Quantization method without accuracy control, using an unannotated dataset. 
 To use this method, create a Python script using an API of Post-Training Optimization Tool (POT) and implement data preparation logic and quantization pipeline. 
 If you are not familiar with Python, try :doc:`command-line interface <pot_compression_cli_README>` of POT which is designed to quantize models from 

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/quantizing_models/default_quantization_algorithm.rst

@@ -1,7 +1,9 @@
 .. {#pot_compression_algorithms_quantization_default_README}
 
-DefaultQuantization Parameters
-==============================
+[Deprecated] DefaultQuantization Parameters
+========================================================
+
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
 
 
 The DefaultQuantization Algorithm is designed to perform fast and accurate quantization. It does not offer direct control over the accuracy metric itself but provides many options that can be used to improve it.

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/quantizing_models_with_accuracy.rst

@@ -1,7 +1,7 @@
 .. {#pot_accuracyaware_usage}
 
-Quantizing Models with Accuracy Control
-=======================================
+[Deprecated] Quantizing Models with Accuracy Control
+================================================================
 
 
 .. toctree::
@@ -10,6 +10,7 @@ Quantizing Models with Accuracy Control
 
    AccuracyAwareQuantization Method <accuracy_aware_README>
 
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
 
 The Accuracy-aware Quantization algorithm allows performing quantization while maintaining accuracy within a pre-defined range. Note that it should be used only if the :doc:`Default Quantization <pot_default_quantization_usage>` introduces a significant accuracy degradation. The reason for it not being the primary choice is its potential for performance degradation, due to some layers getting reverted to the original precision.
 

docs/articles_en/documentation/openvino_legacy_features/post_training_optimization_tool/quantizing_models_with_accuracy/accuracy_aware_algorithm.rst

@@ -1,7 +1,9 @@
 .. {#accuracy_aware_README}
 
-AccuracyAwareQuantization Parameters
-====================================
+[Deprecated] AccuracyAwareQuantization Parameters
+========================================================
+
+.. danger:: Post-training Optimization Tool is deprecated since OpenVINO 2023.0. :doc:`Neural Network Compression Framework (NNCF) <ptq_introduction>` is recommended for the post-training quantization instead.
 
 
 Introduction