Fixed a bug in input validation for torch.compile options (#21835 )

* Fixed bug in input validation for torch.compile options * Added default device if device is None * Addressed PR comments
Move Troubleshooting Reshape article to legacy section (#21837 )
2023-12-22 14:26:08 +04:00 · 2023-12-22 11:15:01 +01:00 · 2023-12-21 13:14:37 +01:00 · 2023-12-21 09:24:52 +00:00 · 2023-12-21 08:47:51 +01:00 · 2023-12-20 19:22:51 +04:00
312 changed files with 73 additions and 926 deletions
@@ -62,7 +62,7 @@ jobs:
          repository: 'openvinotoolkit/openvino_contrib'
          path: ${{ env.OPENVINO_CONTRIB_REPO }}
          submodules: 'true'
-          ref: 'master'
+          ref: 'releases/2023/3'

      #
      # Dependencies
@@ -90,7 +90,7 @@ jobs:
          repository: 'openvinotoolkit/openvino_contrib'
          path: ${{ env.OPENVINO_CONTRIB_REPO }}
          submodules: 'true'
-          ref: 'master'
+          ref: 'releases/2023/3'

      #
      # Print system info
@@ -521,7 +521,7 @@ jobs:
        with:
          repository: 'openvinotoolkit/openvino_contrib'
          path: ${{ env.OPENVINO_CONTRIB_REPO }}
-          ref: 'master'
+          ref: 'releases/2023/3'

      #
      # Dependencies
@@ -87,7 +87,7 @@ jobs:
          repository: 'openvinotoolkit/openvino_contrib'
          path: ${{ env.OPENVINO_CONTRIB_REPO }}
          submodules: 'true'
-          ref: 'master'
+          ref: 'releases/2023/3'

      #
      # Print system info
@@ -86,7 +86,7 @@ jobs:
          repository: 'openvinotoolkit/testdata'
          path: ${{ env.MODELS_PATH }}
          lfs: 'true'
-          ref: 'master'
+          ref: 'releases/2023/3'

      #
      # Print system info
@@ -269,7 +269,7 @@ jobs:
          repository: 'openvinotoolkit/testdata'
          path: ${{ env.MODELS_PATH }}
          lfs: 'true'
-          ref: 'master'
+          ref: 'releases/2023/3'

      - name: Download selective build statistics package
        uses: actions/download-artifact@v3
@@ -72,7 +72,7 @@ jobs:
        with:
          repository: 'openvinotoolkit/openvino_contrib'
          path: 'openvino_contrib'
-          ref: 'master'
+          ref: 'releases/2023/3'

      #
      # Print system info
@@ -77,7 +77,7 @@ jobs:
          repository: 'openvinotoolkit/testdata'
          path: 'testdata'
          lfs: 'true'
-          ref: 'master'
+          ref: 'releases/2023/3'

      #
      # Print system info
@@ -275,7 +275,7 @@ jobs:
          repository: 'openvinotoolkit/testdata'
          path: 'testdata'
          lfs: 'true'
-          ref: 'master'
+          ref: 'releases/2023/3'

      - name: Download selective build statistics package
        uses: actions/download-artifact@v3
@@ -1,5 +1,5 @@
 <div align="center">
-<img src="docs/img/openvino-logo-purple-black.png" width="400px">
+<img src="docs/sphinx_setup/_static/images/img/openvino-logo-purple-black.png" width="400px">

 [![PyPI Status](https://badge.fury.io/py/openvino.svg)](https://badge.fury.io/py/openvino)
 [![Anaconda Status](https://anaconda.org/conda-forge/openvino/badges/version.svg)](https://anaconda.org/conda-forge/openvino)
@@ -9,6 +9,7 @@ Legacy Conversion API
   :hidden:

   Setting Input Shapes <openvino_docs_MO_DG_prepare_model_convert_model_Converting_Model>
+   Troubleshooting Reshape Errors <troubleshooting_reshape_errors>
   Cutting Off Parts of a Model <openvino_docs_MO_DG_prepare_model_convert_model_Cutting_Model>
   Embedding Preprocessing Computation <openvino_docs_MO_DG_Additional_Optimization_Use_Cases>
   Compressing a Model to FP16 <openvino_docs_MO_DG_FP16_Compression>
@@ -1,7 +1,7 @@
 .. {#troubleshooting_reshape_errors}

-Troubleshooting Reshape Errors
-==============================
+[LEGACY] Troubleshooting Reshape Errors
+=======================================


 .. meta::
@@ -10,6 +10,10 @@ Troubleshooting Reshape Errors
                 normal shape propagation.


+.. danger::
+
+   The code described here has been **deprecated!** Do not use it to avoid working with a legacy solution. It will be kept for some time to ensure backwards compatibility, but **you should not use** it in contemporary applications.
+
 How To Avoid Shape Collision
 ############################

@@ -24,7 +28,7 @@ Model structure and logic should not change significantly after model reshaping.

 * The Global Pooling operation is commonly used to reduce output feature map of classification models output. Having the input of the shape *[N, C, H, W]*, Global Pooling returns the output of the shape *[N, C, 1, 1]*. Model architects usually express Global Pooling with the help of the ``Pooling`` operation with the fixed kernel size *[H, W]*. During spatial reshape, having the input of the shape *[N, C, H1, W1]*, ``Pooling`` with the fixed kernel size *[H, W]* returns the output of the shape *[N, C, H2, W2]*, where *H2* and *W2* are commonly not equal to *1*. It breaks the classification model structure. For example, the public `Inception family models from TensorFlow <https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models>`__ have this issue.

-* Changing the model input shape may significantly affect its accuracy. For example, Object Detection models from TensorFlow have resizing restrictions by design. To keep the model valid after the reshape, choose a new input shape that satisfies conditions listed in the ``pipeline.config`` file. For details, refer to the :ref:`Tensorflow Object Detection API models resizing techniques <custom-input-shape>`.
+* Changing the model input shape may significantly affect its accuracy. For example, Object Detection models from TensorFlow have resizing restrictions by design. To keep the model valid after the reshape, choose a new input shape that satisfies conditions listed in the ``pipeline.config`` file.

 .. _how-to-fix-non-reshape-able-model:

@@ -87,5 +87,4 @@ Additional Resources
 - Z. Chen, G. Li, and K. Pittabiraman, "A Low-cost Fault Corrector for Deep Neural Networks through Range Restriction", 2020. https://arxiv.org/abs/2003.13874
 - F. Geissler, Q. Syed, S. Roychowdhury,  A. Asgari, Y. Peng, A. Dhamasia, R. Graefe, K. Pattabiraman, and M. Paulitsch, "Towards a Safety Case for Hardware Fault Tolerance in Convolutional Neural Networks Using Activation Range Supervision", 2021. https://arxiv.org/abs/2108.07019

- @endsphinxdirective

@@ -36,7 +36,7 @@ Make sure you use the most recent supported driver for your hardware setup.

      The Intel® NPU driver for Windows is available through Windows Update but
      it may also be installed manually by downloading the 
-      `NPU driver package <https://www.intel.com/content/www/us/en/download-center/home.html>`__ and following the 
+      `NPU driver package <https://www.intel.com/content/www/us/en/download/794734/intel-npu-driver-windows.html>`__ and following the 
      `Windows driver installation guide <https://support.microsoft.com/en-us/windows/update-drivers-manually-in-windows-ec62f46c-ff14-c91d-eead-d7126dc1f7b6>`__.

      If a driver has already been installed you should be able to find 
@@ -136,6 +136,3 @@ For example, launch model conversion for the ONNX OCR model and specify a bounda
         ovc ocr.onnx --input data[1..3,150,200,1],seq_len[1..3]

 In practice, not every model is designed in a way that allows change of input shapes. An attempt to change the shape for such models may lead to an exception during model conversion, later in model inference, or even to wrong results of inference without explicit exception raised. A knowledge about model topology is required to set shapes appropriately.
-For more information about shape follow the :doc:`inference troubleshooting <troubleshooting_reshape_errors>`
-and :ref:`ways to relax shape inference flow <how-to-fix-non-reshape-able-model>` guides.
-
@@ -3,13 +3,6 @@
 Changing Input Shapes
 =====================

-
-.. toctree::
-   :maxdepth: 1
-   :hidden:
-
-   troubleshooting_reshape_errors
-
 .. meta::
   :description: OpenVINO™ allows changing model input shape during the runtime when the provided
                 input has a different size than the model's input shape.
@@ -22,8 +15,8 @@ The following instructions are for cases where you need to change the model inpu
 .. note::

   If you need to do this only once, prepare a model with updated shapes via
-   :doc:`model conversion API <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`.
-   For more information, refer to the :ref:`Specifying input_shape Parameter <when_to_specify_input_shapes>` article.
+   :doc:`Model Conversion API <openvino_docs_model_processing_introduction>`.
+   For more information, refer to the :doc:`Setting Input Shapes <openvino_docs_OV_Converter_UG_prepare_model_convert_model_Converting_Model>` article.


 The reshape method
@@ -165,7 +158,7 @@ You can find the usage scenarios of the ``reshape`` method in
 .. note::

   In some cases, models may not be ready to be reshaped. Therefore, a new input
-   shape cannot be set neither with :doc:`Model Optimizer <openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide>`
+   shape cannot be set neither with :doc:`Model Conversion API <openvino_docs_model_processing_introduction>`
   nor the ``reshape`` method.

 The set_batch method
@@ -192,8 +185,7 @@ To change the batch dimension of the model, :ref:`set the layout <declare_model_


 The ``set_batch`` method is a high-level API of the reshape functionality, so all
-information about the ``reshape`` method implications are applicable for ``set_batch``
-too, including the troubleshooting section.
+information about the ``reshape`` method implications are applicable for ``set_batch`` too.

 Once you set the input shape of the model, call the ``compile_model`` method to
 get a ``CompiledModel`` object for inference with updated shapes.
@@ -185,7 +185,7 @@ Overview of the [Linux workflow's](../../../../.github/workflows/linux.yml) `Pyt
 To understand which jobs have successfully passed, which are running and which have failed, check the following:
 * For Pull Requests:
  * Open a Pull Request and navigate to the bottom of the page, you will see the list of jobs that ran or are running for the latest commit:
-  ![check_results](../../../_static/images/ci/check_results.png)
+  ![check_results](../../../sphinx_setup/_static/images/ci/check_results.png)
 * For scheduled runs:
  * Navigate to the [OpenVINO Repository Actions](https://github.com/openvinotoolkit/openvino/actions)
  * Select the required workflow from the list on the left
@@ -196,13 +196,13 @@ To understand which jobs have successfully passed, which are running and which h

 To find artefacts for a pipeline, use the following steps:
 1. Open a Pull Request and navigate to the bottom of the page, you will see the list of jobs that ran or are running for the latest commit: 
-  ![check_results](../../../_static/images/ci/check_results.png)
+  ![check_results](../../../sphinx_setup/_static/images/ci/check_results.png)
 2. Click `Details` to see more information about a job
 3. Click `Summary` above the list of the jobs: 
-  ![jobs_list](../../../_static/images/ci/completed_job_list.png)
+  ![jobs_list](../../../sphinx_setup/_static/images/ci/completed_job_list.png)
 4. Scroll to the bottom of the page
 5. You will find the artefacts produced by **all the jobs in this pipeline**:
-  ![pipeline_artefacts](../../../_static/images/ci/pipeline_artefacts.png)
+  ![pipeline_artefacts](../../../sphinx_setup/_static/images/ci/pipeline_artefacts.png)
 6. Click on the artefact name to download it

 **NOTE**: artefacts are available only for the completed, i.e., successful or failed, pipelines.
@@ -211,7 +211,7 @@ To find artefacts for a pipeline, use the following steps:

 To find logs for a pipeline:
 1. Open a Pull Request and navigate to the bottom of the page, you will see the list of jobs that ran or are running for the latest commit:
-  ![check_results](../../../_static/images/ci/check_results.png)
+  ![check_results](../../../sphinx_setup/_static/images/ci/check_results.png)
 2. Click `Details` to see more information about a job
 3. Click on a step to see its logs

@@ -1,2 +0,0 @@
-
-> **NOTE**: This version is pre-release software and has not undergone full release validation or qualification. No support is offered on pre-release software and APIs/behavior are subject to change.  It should NOT be incorporated into any production software/solution and instead should be used only for early testing and integration while awaiting a final release version of this software.
@@ -1,9 +1,6 @@
 # OpenVINO™ Development Tools

-<!--- The note below is intended for master branch only for pre-release purpose. Remove it for official releases. --->
-> **NOTE**: This version is pre-release software and has not undergone full release validation or qualification. No support is offered on pre-release software and APIs/behavior are subject to change. It should NOT be incorporated into any production software/solution and instead should be used only for early testing and integration while awaiting a final release version of this software.
-
-> **NOTE**: OpenVINO™ Development Tools package has been deprecated and will be discontinued with 2025.0 release. To learn more, refer to the [OpenVINO Legacy Features and Components page](https://docs.openvino.ai/2023.2/openvino_legacy_features.html).
+> **NOTE**: OpenVINO™ Development Tools package has been deprecated and will be discontinued with 2025.0 release. To learn more, refer to the [OpenVINO Legacy Features and Components page](https://docs.openvino.ai/2023.3/openvino_legacy_features.html).

 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.

@@ -121,14 +118,14 @@ For example, to install and configure the components for working with TensorFlow

 | 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\*.                                               |                                         |
-| [Accuracy Checker](https://docs.openvino.ai/nightly/omz_tools_accuracy_checker.html) and <br> [Annotation Converter](https://docs.openvino.ai/nightly/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/nightly/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/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/2023.3/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\*.                                               |                                         |
+| [Accuracy Checker](https://docs.openvino.ai/2023.3/omz_tools_accuracy_checker.html) and <br> [Annotation Converter](https://docs.openvino.ai/2023.3/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/2023.3/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/2023.3/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

-For general troubleshooting steps and issues, see [Troubleshooting Guide for OpenVINO Installation](https://docs.openvino.ai/2023.2/openvino_docs_get_started_guide_troubleshooting.html). 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/2023.3/openvino_docs_get_started_guide_troubleshooting.html). The following sections also provide explanations to several error messages.

 ### Errors with Installing via PIP for Users in China

@@ -1,11 +1,8 @@
 # OpenVINO™ 

-<!--- The note below is intended for master branch only for pre-release purpose. Remove it for official releases. --->
-> **NOTE**: This version is pre-release software and has not undergone full release validation or qualification. No support is offered on pre-release software and APIs/behavior are subject to change. It should NOT be incorporated into any production software/solution and instead should be used only for early testing and integration while awaiting a final release version of this software.
-
 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™](https://docs.openvino.ai/2023.2/openvino_docs_OV_UG_OV_Runtime_User_Guide.html) Python package includes a set of libraries for an easy inference integration with your products.
+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™](https://docs.openvino.ai/2023.3/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

@@ -75,13 +72,13 @@ If installation was successful, you will see the list of available devices.

 | Component        | Content                                                                  | Description                                                                                                                                                                                                                                                                                                   |
 |------------------|---------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [OpenVINO Runtime](https://docs.openvino.ai/2023.2/openvino_docs_OV_UG_OV_Runtime_User_Guide.html) | `openvino package` |**OpenVINO Runtime**  is a set of C++ libraries with C and Python bindings providing a common API to deliver inference solutions on the platform of your choice. Use the OpenVINO Runtime API to read PyTorch\*, TensorFlow\*, TensorFlow Lite\*, ONNX\*, and PaddlePaddle\* models and execute them on preferred devices. OpenVINO Runtime uses a plugin architecture and includes the following plugins: [CPU](https://docs.openvino.ai/2023.2/openvino_docs_OV_UG_supported_plugins_CPU.html), [GPU](https://docs.openvino.ai/2023.2/openvino_docs_OV_UG_supported_plugins_GPU.html), [Auto Batch](https://docs.openvino.ai/2023.2/openvino_docs_OV_UG_Automatic_Batching.html), [Auto](https://docs.openvino.ai/2023.2/openvino_docs_OV_UG_supported_plugins_AUTO.html), [Hetero](https://docs.openvino.ai/2023.2/openvino_docs_OV_UG_Hetero_execution.html).       
-| [OpenVINO Model Converter (OVC)](https://docs.openvino.ai/2023.2/openvino_docs_model_processing_introduction.html#convert-a-model-in-cli-ovc) | `ovc` |**OpenVINO Model Converter**  converts models that were trained in popular frameworks to a format usable by OpenVINO components. <br>Supported frameworks include ONNX\*, TensorFlow\*, TensorFlow Lite\*, and PaddlePaddle\*.                                    |
-| [Benchmark Tool](https://docs.openvino.ai/2023.2/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.                                              |
+| [OpenVINO Runtime](https://docs.openvino.ai/2023.3/openvino_docs_OV_UG_OV_Runtime_User_Guide.html) | `openvino package` |**OpenVINO Runtime**  is a set of C++ libraries with C and Python bindings providing a common API to deliver inference solutions on the platform of your choice. Use the OpenVINO Runtime API to read PyTorch\*, TensorFlow\*, TensorFlow Lite\*, ONNX\*, and PaddlePaddle\* models and execute them on preferred devices. OpenVINO Runtime uses a plugin architecture and includes the following plugins: [CPU](https://docs.openvino.ai/2023.3/openvino_docs_OV_UG_supported_plugins_CPU.html), [GPU](https://docs.openvino.ai/2023.3/openvino_docs_OV_UG_supported_plugins_GPU.html), [Auto Batch](https://docs.openvino.ai/2023.3/openvino_docs_OV_UG_Automatic_Batching.html), [Auto](https://docs.openvino.ai/2023.3/openvino_docs_OV_UG_supported_plugins_AUTO.html), [Hetero](https://docs.openvino.ai/2023.3/openvino_docs_OV_UG_Hetero_execution.html).       
+| [OpenVINO Model Converter (OVC)](https://docs.openvino.ai/2023.3/openvino_docs_model_processing_introduction.html#convert-a-model-in-cli-ovc) | `ovc` |**OpenVINO Model Converter**  converts models that were trained in popular frameworks to a format usable by OpenVINO components. <br>Supported frameworks include ONNX\*, TensorFlow\*, TensorFlow Lite\*, and PaddlePaddle\*.                                    |
+| [Benchmark Tool](https://docs.openvino.ai/2023.3/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.                                              |

 ## Troubleshooting

-For general troubleshooting steps and issues, see [Troubleshooting Guide for OpenVINO Installation](https://docs.openvino.ai/2023.2/openvino_docs_get_started_guide_troubleshooting.html). 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/2023.3/openvino_docs_get_started_guide_troubleshooting.html). The following sections also provide explanations to several error messages. 

 ### Errors with Installing via PIP for Users in China

@@ -1,3 +0,0 @@
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--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Surya Siddharth Pemmaraju	8a5e6b6738	Fixed a bug in input validation for torch.compile options (#21835 ) * Fixed bug in input validation for torch.compile options * Added default device if device is None * Addressed PR comments	2023-12-22 14:26:08 +04:00
Sebastian Golebiewski	81b5895d24	Move Troubleshooting Reshape article to legacy section (#21837 )	2023-12-22 11:15:01 +01:00
Maciej Smyk	e37569ffd5	[DOCS] Removal of unused images + fixing image directives for 23.3 (#21818 ) * removal of unused images * removal of duplicate image files * removal of unused files + fix for image directive * removal of unused images * removal of unused images + fix for protecting_model.rst * overview document graph update	2023-12-21 13:14:37 +01:00
Karol Blaszczak	07fc4d0813	[DOCS] NPU update 23.3 (#21812 )	2023-12-21 09:24:52 +00:00
Tatiana Savina	7ef8faecf0	fix note and img (#21797 )	2023-12-21 08:47:51 +01:00
Ilya Lavrenov	53960d6018	Switched GHA branches to 2023.3 (#21793 )	2023-12-20 19:22:51 +04:00
				`@@ -1,2 +0,0 @@`

				`> NOTE: This version is pre-release software and has not undergone full release validation or qualification. No support is offered on pre-release software and APIs/behavior are subject to change. It should NOT be incorporated into any production software/solution and instead should be used only for early testing and integration while awaiting a final release version of this software.`