[DOCS] 23.0 to 23.1 link update for master (openvinotoolkit#19584)

* 2023.1 link fix

* 2023.1 link fix

* 2023.1 link fix

* 2023.1 link fix

* 2023.1 link fix

Author: msmykx-intel

README.md

@@ -68,24 +68,24 @@ The OpenVINO™ Runtime can infer models on different hardware devices. This sec
     <tbody>
         <tr>
             <td rowspan=2>CPU</td>
-            <td> <a href="https://docs.openvino.ai/2023.0/openvino_docs_OV_UG_supported_plugins_CPU.html#doxid-openvino-docs-o-v-u-g-supported-plugins-c-p-u">Intel CPU</a></tb>
+            <td> <a href="https://docs.openvino.ai/2023.1/openvino_docs_OV_UG_supported_plugins_CPU.html#doxid-openvino-docs-o-v-u-g-supported-plugins-c-p-u">Intel CPU</a></tb>
             <td><b><i><a href="./src/plugins/intel_cpu">openvino_intel_cpu_plugin</a></i></b></td>
             <td>Intel Xeon with Intel® Advanced Vector Extensions 2 (Intel® AVX2), Intel® Advanced Vector Extensions 512 (Intel® AVX-512), and AVX512_BF16, Intel Core Processors with Intel AVX2, Intel Atom Processors with Intel® Streaming SIMD Extensions (Intel® SSE)</td>
         </tr>
         <tr>
-            <td> <a href="https://docs.openvino.ai/2023.0/openvino_docs_OV_UG_supported_plugins_CPU.html">ARM CPU</a></tb>
+            <td> <a href="https://docs.openvino.ai/2023.1/openvino_docs_OV_UG_supported_plugins_CPU.html">ARM CPU</a></tb>
             <td><b><i><a href="https://github.com/openvinotoolkit/openvino_contrib/tree/master/modules/arm_plugin">openvino_arm_cpu_plugin</a></i></b></td>
             <td>Raspberry Pi™ 4 Model B, Apple® Mac mini with M1 chip, NVIDIA® Jetson Nano™, Android™ devices
         </tr>
         <tr>
             <td>GPU</td>
-            <td><a href="https://docs.openvino.ai/2023.0/openvino_docs_OV_UG_supported_plugins_GPU.html#doxid-openvino-docs-o-v-u-g-supported-plugins-g-p-u">Intel GPU</a></td>
+            <td><a href="https://docs.openvino.ai/2023.1/openvino_docs_OV_UG_supported_plugins_GPU.html#doxid-openvino-docs-o-v-u-g-supported-plugins-g-p-u">Intel GPU</a></td>
             <td><b><i><a href="./src/plugins/intel_gpu">openvino_intel_gpu_plugin</a></i></b></td>
             <td>Intel Processor Graphics, including Intel HD Graphics and Intel Iris Graphics</td>
         </tr>
         <tr>
             <td>GNA</td>
-            <td><a href="https://docs.openvino.ai/2023.0/openvino_docs_OV_UG_supported_plugins_GNA.html#doxid-openvino-docs-o-v-u-g-supported-plugins-g-n-a">Intel GNA</a></td>
+            <td><a href="https://docs.openvino.ai/2023.1/openvino_docs_OV_UG_supported_plugins_GNA.html#doxid-openvino-docs-o-v-u-g-supported-plugins-g-n-a">Intel GNA</a></td>
             <td><b><i><a href="./src/plugins/intel_gna">openvino_intel_gna_plugin</a></i></b></td>
             <td>Intel Speech Enabling Developer Kit, Amazon Alexa* Premium Far-Field Developer Kit, Intel Pentium Silver J5005 Processor, Intel Pentium Silver N5000 Processor, Intel Celeron J4005 Processor, Intel Celeron J4105 Processor, Intel Celeron Processor N4100, Intel Celeron Processor N4000, Intel Core i3-8121U Processor, Intel Core i7-1065G7 Processor, Intel Core i7-1060G7 Processor, Intel Core i5-1035G4 Processor, Intel Core i5-1035G7 Processor, Intel Core i5-1035G1 Processor, Intel Core i5-1030G7 Processor, Intel Core i5-1030G4 Processor, Intel Core i3-1005G1 Processor, Intel Core i3-1000G1 Processor, Intel Core i3-1000G4 Processor</td>
         </tr>
@@ -103,22 +103,22 @@ OpenVINO™ Toolkit also contains several plugins which simplify loading models
     </thead>
     <tbody>
         <tr>
-            <td><a href="https://docs.openvino.ai/2023.0/openvino_docs_IE_DG_supported_plugins_AUTO.html#doxid-openvino-docs-i-e-d-g-supported-plugins-a-u-t-o">Auto</a></td>
+            <td><a href="https://docs.openvino.ai/2023.1/openvino_docs_OV_UG_supported_plugins_AUTO.html">Auto</a></td>
             <td><b><i><a href="./src/plugins/auto">openvino_auto_plugin</a></i></b></td>
             <td>Auto plugin enables selecting Intel device for inference automatically</td>
         </tr>
         <tr>
-            <td><a href="https://docs.openvino.ai/2023.0/openvino_docs_OV_UG_Automatic_Batching.html">Auto Batch</a></td>
+            <td><a href="https://docs.openvino.ai/2023.1/openvino_docs_OV_UG_Automatic_Batching.html">Auto Batch</a></td>
             <td><b><i><a href="./src/plugins/auto_batch">openvino_auto_batch_plugin</a></i></b></td>
             <td>Auto batch plugin performs on-the-fly automatic batching (i.e. grouping inference requests together) to improve device utilization, with no programming effort from the user</td>
         </tr>
         <tr>
-            <td><a href="https://docs.openvino.ai/2023.0/openvino_docs_OV_UG_Hetero_execution.html#doxid-openvino-docs-o-v-u-g-hetero-execution">Hetero</a></td>
+            <td><a href="https://docs.openvino.ai/2023.1/openvino_docs_OV_UG_Hetero_execution.html#doxid-openvino-docs-o-v-u-g-hetero-execution">Hetero</a></td>
             <td><b><i><a href="./src/plugins/hetero">openvino_hetero_plugin</a></i></b></td>
             <td>Heterogeneous execution enables automatic inference splitting between several devices</td>
         </tr>
         <tr>
-            <td><a href="https://docs.openvino.ai/2023.0/openvino_docs_OV_UG_Running_on_multiple_devices.html#doxid-openvino-docs-o-v-u-g-running-on-multiple-devices">Multi</a></td>
+            <td><a href="https://docs.openvino.ai/2023.1/openvino_docs_OV_UG_Running_on_multiple_devices.html#doxid-openvino-docs-o-v-u-g-running-on-multiple-devices">Multi</a></td>
             <td><b><i><a href="./src/plugins/auto">openvino_auto_plugin</a></i></b></td>
             <td>Multi plugin enables simultaneous inference of the same model on several devices in parallel</td>
         </tr>
@@ -155,10 +155,9 @@ The list of OpenVINO tutorials:
 ## System requirements
 
 The system requirements vary depending on platform and are available on dedicated pages:
-- [Linux](https://docs.openvino.ai/2023.0/openvino_docs_install_guides_installing_openvino_linux_header.html)
-- [Windows](https://docs.openvino.ai/2023.0/openvino_docs_install_guides_installing_openvino_windows_header.html)
-- [macOS](https://docs.openvino.ai/2023.0/openvino_docs_install_guides_installing_openvino_macos_header.html)
-- [Raspbian](https://docs.openvino.ai/2023.0/openvino_docs_install_guides_installing_openvino_raspbian.html)
+- [Linux](https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_linux_header.html)
+- [Windows](https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_windows_header.html)
+- [macOS](https://docs.openvino.ai/2023.1/openvino_docs_install_guides_installing_openvino_macos_header.html)
 
 ## How to build
 
@@ -196,7 +195,7 @@ Report questions, issues and suggestions, using:
 \* Other names and brands may be claimed as the property of others.
 
 [Open Model Zoo]:https://github.com/openvinotoolkit/open_model_zoo
-[OpenVINO™ Runtime]:https://docs.openvino.ai/2023.0/openvino_docs_OV_UG_OV_Runtime_User_Guide.html
-[Model Optimizer]:https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html
-[Post-Training Optimization Tool]:https://docs.openvino.ai/2023.0/pot_introduction.html
+[OpenVINO™ Runtime]:https://docs.openvino.ai/2023.1/openvino_docs_OV_UG_OV_Runtime_User_Guide.html
+[Model Optimizer]:https://docs.openvino.ai/2023.1/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html
+[Post-Training Optimization Tool]:https://docs.openvino.ai/2023.1/pot_introduction.html
 [Samples]:https://github.com/openvinotoolkit/openvino/tree/master/samples

docs/IE_PLUGIN_DG/Intro.md

@@ -94,7 +94,7 @@ Detailed Guides
 API References
 ##############
 
-*  `OpenVINO Plugin API <https://docs.openvino.ai/2023.0/groupov_dev_api.html>`__
-*  `OpenVINO Transformation API <https://docs.openvino.ai/2023.0/groupie_transformation_api.html>`__
+*  `OpenVINO Plugin API <https://docs.openvino.ai/2023.1/groupov_dev_api.html>`__
+*  `OpenVINO Transformation API <https://docs.openvino.ai/2023.1/groupie_transformation_api.html>`__
 
 @endsphinxdirective

docs/IE_PLUGIN_DG/dev_api_references.md

@@ -15,7 +15,7 @@
 
 The guides below provides extra API references needed for OpenVINO plugin development:
 
-*  `OpenVINO Plugin API <https://docs.openvino.ai/2023.0/groupov_dev_api.html>`__
-*  `OpenVINO Transformation API <https://docs.openvino.ai/2023.0/groupie_transformation_api.html>`__
+*  `OpenVINO Plugin API <https://docs.openvino.ai/2023.1/groupov_dev_api.html>`__
+*  `OpenVINO Transformation API <https://docs.openvino.ai/2023.1/groupie_transformation_api.html>`__
 
 @endsphinxdirective

docs/MO_DG/prepare_model/convert_model/tf_specific/Convert_RetinaNet_From_Tensorflow.md

@@ -10,7 +10,7 @@
 This tutorial explains how to convert a RetinaNet model to the Intermediate Representation (IR).
 
 `Public RetinaNet model <https://github.com/fizyr/keras-retinanet>`__ does not contain pretrained TensorFlow weights.
-To convert this model to the TensorFlow format, follow the `Reproduce Keras to TensorFlow Conversion tutorial <https://docs.openvino.ai/2023.0/omz_models_model_retinanet_tf.html>`__. 
+To convert this model to the TensorFlow format, follow the `Reproduce Keras to TensorFlow Conversion tutorial <https://docs.openvino.ai/2023.1/omz_models_model_retinanet_tf.html>`__. 
 
 After converting the model to TensorFlow format, run the following command:
 

docs/OV_Runtime_UG/integrate_with_your_application.md

@@ -437,9 +437,9 @@ To build your project using CMake with the default build tools currently availab
 Additional Resources
 ####################
 
-* See the :doc:`OpenVINO Samples <openvino_docs_OV_UG_Samples_Overview>` page or the `Open Model Zoo Demos <https://docs.openvino.ai/2023.0/omz_demos.html>`__ page for specific examples of how OpenVINO pipelines are implemented for applications like image classification, text prediction, and many others.
+* See the :doc:`OpenVINO Samples <openvino_docs_OV_UG_Samples_Overview>` page or the `Open Model Zoo Demos <https://docs.openvino.ai/2023.1/omz_demos.html>`__ page for specific examples of how OpenVINO pipelines are implemented for applications like image classification, text prediction, and many others.
 * :doc:`OpenVINO™ Runtime Preprocessing <openvino_docs_OV_UG_Preprocessing_Overview>`
 * :doc:`Using Encrypted Models with OpenVINO <openvino_docs_OV_UG_protecting_model_guide>`
-* `Open Model Zoo Demos <https://docs.openvino.ai/2023.0/omz_demos.html>`__
+* `Open Model Zoo Demos <https://docs.openvino.ai/2023.1/omz_demos.html>`__
 
 @endsphinxdirective

docs/OV_Runtime_UG/ov_dynamic_shapes.md

@@ -62,7 +62,7 @@ Model input dimensions can be specified as dynamic using the model.reshape metho
 
    Some models may already have dynamic shapes out of the box and do not require additional configuration. This can either be because it was generated with dynamic shapes from the source framework, or because it was converted with Model Conversion API to use dynamic shapes. For more information, see the Dynamic Dimensions “Out of the Box” section.
 
-The examples below show how to set dynamic dimensions with a model that has a static ``[1, 3, 224, 224]`` input shape (such as `mobilenet-v2 <https://docs.openvino.ai/2023.0/omz_models_model_mobilenet_v2.html>`__). The first example shows how to change the first dimension (batch size) to be dynamic. In the second example, the third and fourth dimensions (height and width) are set as dynamic.
+The examples below show how to set dynamic dimensions with a model that has a static ``[1, 3, 224, 224]`` input shape (such as `mobilenet-v2 <https://docs.openvino.ai/2023.1/omz_models_model_mobilenet_v2.html>`__). The first example shows how to change the first dimension (batch size) to be dynamic. In the second example, the third and fourth dimensions (height and width) are set as dynamic.
 
 .. tab-set::
 
@@ -175,7 +175,7 @@ The lower and/or upper bounds of a dynamic dimension can also be specified. They
     .. tab-item:: C
         :sync: c
         
-        The dimension bounds can be coded as arguments for `ov_dimension <https://docs.openvino.ai/2023.0/structov_dimension.html#doxid-structov-dimension>`__, as shown in these examples:
+        The dimension bounds can be coded as arguments for `ov_dimension <https://docs.openvino.ai/2023.1/structov_dimension.html#doxid-structov-dimension>`__, as shown in these examples:
 
         .. doxygensnippet:: docs/snippets/ov_dynamic_shapes.c
            :language: cpp

docs/OV_Runtime_UG/preprocessing_usecase_save.md

@@ -110,8 +110,8 @@ Additional Resources
 * :doc:`Layout API overview <openvino_docs_OV_UG_Layout_Overview>`
 * :doc:`Model Optimizer - Optimize Preprocessing Computation <openvino_docs_MO_DG_Additional_Optimization_Use_Cases>`
 * :doc:`Model Caching Overview <openvino_docs_OV_UG_Model_caching_overview>`
-* The `ov::preprocess::PrePostProcessor <https://docs.openvino.ai/2023.0/classov_1_1preprocess_1_1PrePostProcessor.html#doxid-classov-1-1preprocess-1-1-pre-post-processor>`__ C++ class documentation
-* The `ov::pass::Serialize <https://docs.openvino.ai/2023.0/classov_1_1pass_1_1Serialize.html#doxid-classov-1-1pass-1-1-serialize.html>`__ - pass to serialize model to XML/BIN
-* The `ov::set_batch <https://docs.openvino.ai/2023.0/namespaceov.html#doxid-namespaceov-1a3314e2ff91fcc9ffec05b1a77c37862b.html>`__ - update batch dimension for a given model
+* The `ov::preprocess::PrePostProcessor <https://docs.openvino.ai/2023.1/classov_1_1preprocess_1_1PrePostProcessor.html#doxid-classov-1-1preprocess-1-1-pre-post-processor>`__ C++ class documentation
+* The `ov::pass::Serialize <https://docs.openvino.ai/2023.1/classov_1_1pass_1_1Serialize.html#doxid-classov-1-1pass-1-1-serialize.html>`__ - pass to serialize model to XML/BIN
+* The `ov::set_batch <https://docs.openvino.ai/2023.1/namespaceov.html#doxid-namespaceov-1a3314e2ff91fcc9ffec05b1a77c37862b.html>`__ - update batch dimension for a given model
 
 @endsphinxdirective

docs/benchmarks/performance_benchmarks.md

@@ -13,7 +13,7 @@
 
    openvino_docs_performance_benchmarks_faq
    OpenVINO Accuracy <openvino_docs_performance_int8_vs_fp32>
-   Performance Data Spreadsheet (download xlsx) <https://docs.openvino.ai/2023.0/_static/benchmarks_files/OV-2023.0-Performance-Data.xlsx> 
+   Performance Data Spreadsheet (download xlsx) <https://docs.openvino.ai/2023.1/_static/benchmarks_files/OV-2023.0-Performance-Data.xlsx> 
    openvino_docs_MO_DG_Getting_Performance_Numbers
 
 

docs/dev/cmake_options_for_custom_compilation.md

@@ -189,8 +189,8 @@ In this case OpenVINO CMake scripts take `TBBROOT` environment variable into acc
 [pugixml]:https://pugixml.org/
 [ONNX]:https://onnx.ai/
 [protobuf]:https://github.com/protocolbuffers/protobuf
-[deployment manager]:https://docs.openvino.ai/2023.0/openvino_docs_install_guides_deployment_manager_tool.html
-[OpenVINO Runtime Introduction]:https://docs.openvino.ai/2023.0/openvino_docs_OV_UG_Integrate_OV_with_your_application.html
+[deployment manager]:https://docs.openvino.ai/2023.1/openvino_docs_install_guides_deployment_manager_tool.html
+[OpenVINO Runtime Introduction]:https://docs.openvino.ai/2023.1/openvino_docs_OV_UG_Integrate_OV_with_your_application.html
 [PDPD]:https://github.com/PaddlePaddle/Paddle
 [TensorFlow]:https://www.tensorflow.org/
 [TensorFlow Lite]:https://www.tensorflow.org/lite

docs/dev/debug_capabilities.md

@@ -2,7 +2,7 @@
 
 OpenVINO components provides different debug capabilities, to get more information please read:
 
-* [OpenVINO Model Debug Capabilities](https://docs.openvino.ai/2023.0/openvino_docs_OV_UG_Model_Representation.html#model-debug-capabilities)
+* [OpenVINO Model Debug Capabilities](https://docs.openvino.ai/2023.1/openvino_docs_OV_UG_Model_Representation.html#model-debug-capabilities)
 * [OpenVINO Pass Manager Debug Capabilities](#todo)
 
 ## See also

docs/gapi/face_beautification.md

@@ -24,8 +24,8 @@ This sample requires:
 * OpenCV 4.2 or higher built with `Intel® Distribution of OpenVINO™ Toolkit <https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit.html>`__ (building with `Intel® TBB <https://www.threadingbuildingblocks.org/intel-tbb-tutorial>`__ is a plus)
 * The following pre-trained models from the :doc:`Open Model Zoo <omz_models_group_intel>`
 
-  * `face-detection-adas-0001 <https://docs.openvino.ai/2023.0/omz_models_model_face_detection_adas_0001.html#doxid-omz-models-model-face-detection-adas-0001>`__
-  * `facial-landmarks-35-adas-0002 <https://docs.openvino.ai/2023.0/omz_models_model_facial_landmarks_35_adas_0002.html#doxid-omz-models-model-facial-landmarks-35-adas-0002>`__
+  * `face-detection-adas-0001 <https://docs.openvino.ai/2023.1/omz_models_model_face_detection_adas_0001.html#doxid-omz-models-model-face-detection-adas-0001>`__
+  * `facial-landmarks-35-adas-0002 <https://docs.openvino.ai/2023.1/omz_models_model_facial_landmarks_35_adas_0002.html#doxid-omz-models-model-facial-landmarks-35-adas-0002>`__
 
 To download the models from the Open Model Zoo, use the :doc:`Model Downloader <omz_tools_downloader>` tool.
 

docs/gapi/gapi_face_analytics_pipeline.md

@@ -24,9 +24,9 @@ This sample requires:
 * OpenCV 4.2 or higher built with `Intel® Distribution of OpenVINO™ Toolkit <https://software.intel.com/content/www/us/en/develop/tools/openvino-toolkit.html>`__ (building with `Intel® TBB <https://www.threadingbuildingblocks.org/intel-tbb-tutorial>`__ is a plus)
 * The following pre-trained models from the :doc:`Open Model Zoo <omz_models_group_intel>`
 
-  * `face-detection-adas-0001 <https://docs.openvino.ai/2023.0/omz_models_model_face_detection_adas_0001.html#doxid-omz-models-model-face-detection-adas-0001>`__
-  * `age-gender-recognition-retail-0013 <https://docs.openvino.ai/2023.0/omz_models_model_age_gender_recognition_retail_0013.html#doxid-omz-models-model-age-gender-recognition-retail-0013>`__
-  * `emotions-recognition-retail-0003 <https://docs.openvino.ai/2023.0/omz_models_model_emotions_recognition_retail_0003.html#doxid-omz-models-model-emotions-recognition-retail-0003>`__
+  * `face-detection-adas-0001 <https://docs.openvino.ai/2023.1/omz_models_model_face_detection_adas_0001.html#doxid-omz-models-model-face-detection-adas-0001>`__
+  * `age-gender-recognition-retail-0013 <https://docs.openvino.ai/2023.1/omz_models_model_age_gender_recognition_retail_0013.html#doxid-omz-models-model-age-gender-recognition-retail-0013>`__
+  * `emotions-recognition-retail-0003 <https://docs.openvino.ai/2023.1/omz_models_model_emotions_recognition_retail_0003.html#doxid-omz-models-model-emotions-recognition-retail-0003>`__
 
 To download the models from the Open Model Zoo, use the :doc:`Model Downloader <omz_tools_downloader>` tool.
 
@@ -42,7 +42,7 @@ Starting with version 4.2, OpenCV offers a solution to this problem. OpenCV G-AP
 Pipeline Overview
 #################
 
-Our sample application is based on `Interactive Face Detection <https://docs.openvino.ai/2023.0/omz_demos_interactive_face_detection_demo_cpp.html#doxid-omz-demos-interactive-face-detection-demo-cpp>`__ demo from Open Model Zoo. A simplified pipeline consists of the following steps:
+Our sample application is based on `Interactive Face Detection <https://docs.openvino.ai/2023.1/omz_demos_interactive_face_detection_demo_cpp.html#doxid-omz-demos-interactive-face-detection-demo-cpp>`__ demo from Open Model Zoo. A simplified pipeline consists of the following steps:
 
 1. Image acquisition and decode
 2. Detection with preprocessing