comment fix (openvinotoolkit#22557)

xufang-lisa · Jan 31, 2024 · 4cf6f57 · 4cf6f57
1 parent ffb5270
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diff --git a/...tion/openvino_legacy_features/api_2_0_transition_guide/deployment_migration.rst b/...tion/openvino_legacy_features/api_2_0_transition_guide/deployment_migration.rst
@@ -5,8 +5,8 @@ Installation & Deployment
 
 
 .. meta::
-   :description: OpenVINO™ API 2.0 focuses on the use of development tools and 
-                 deployment of applications, it also simplifies migration from 
+   :description: OpenVINO™ API 2.0 focuses on the use of development tools and
+                 deployment of applications, it also simplifies migration from
                  different frameworks to OpenVINO.
 
 
@@ -18,7 +18,7 @@ One of the main concepts for OpenVINO™ API 2.0 is being "easy to use", which i
 * Development and deployment of OpenVINO-based applications.
 
 
-To accomplish that, the 2022.1 release OpenVINO introduced significant changes to the installation 
+To accomplish that, the 2022.1 release OpenVINO introduced significant changes to the installation
 and deployment processes. Further changes were implemented in 2023.1, aiming at making the installation
 process even simpler.
 
@@ -27,8 +27,8 @@ process even simpler.
    These instructions are largely deprecated and should be used for versions prior to 2023.1.
 
    The OpenVINO Development Tools package is being deprecated and will be discontinued entirely in 2025.
-   With this change, the OpenVINO Runtime package has become the default choice for installing the 
-   software. It now includes all components necessary to utilize OpenVINO's functionality. 
+   With this change, the OpenVINO Runtime package has become the default choice for installing the
+   software. It now includes all components necessary to utilize OpenVINO's functionality.
 
 
 
@@ -68,7 +68,7 @@ In OpenVINO 2022.1 and later, you can install the development tools only from a
 
 .. code-block:: sh
 
-   $ python3 -m pip install -r <INSTALL_DIR>/tools/requirements_tf.txt 
+   $ python3 -m pip install -r <INSTALL_DIR>/tools/requirements_tf.txt
 
 
 This will install all the development tools and additional components necessary to work with TensorFlow via the ``openvino-dev`` package (see **Step 4. Install the Package** on the `PyPI page <https://pypi.org/project/openvino-dev/>`__ for parameters of other frameworks).
@@ -81,7 +81,7 @@ Then, the tools can be used by commands like:
    $ pot -h
 
 
-Installation of any other dependencies is not required. For more details on the installation steps, see the 
+Installation of any other dependencies is not required. For more details on the installation steps, see the
 `Install OpenVINO Development Tools <https://docs.openvino.ai/2023.3/openvino_docs_install_guides_install_dev_tools.html>`__ prior to OpenVINO 2023.1.
 
 Interface Changes for Building C/C++ Applications
@@ -127,36 +127,36 @@ It is possible to build applications without the CMake interface by using: MSVC
 
    .. tab-item:: Include dirs
       :sync: include-dirs
-   
+
       .. code-block:: sh
-   
+
          <INSTALL_DIR>/deployment_tools/inference_engine/include
          <INSTALL_DIR>/deployment_tools/ngraph/include
-   
+
    .. tab-item:: Path to libs
       :sync: path-libs
-   
+
       .. code-block:: sh
-   
+
          <INSTALL_DIR>/deployment_tools/inference_engine/lib/intel64/Release
          <INSTALL_DIR>/deployment_tools/ngraph/lib/
-   
+
    .. tab-item:: Shared libs
       :sync: shared-libs
-   
+
       .. code-block:: sh
-   
+
          // UNIX systems
          inference_engine.so ngraph.so
-   
+
          // Windows
          inference_engine.dll ngraph.dll
-   
+
    .. tab-item:: (Windows) .lib files
       :sync: windows-lib-files
-   
+
       .. code-block:: sh
-   
+
          ngraph.lib
          inference_engine.lib
 
@@ -166,36 +166,36 @@ It is possible to build applications without the CMake interface by using: MSVC
 
    .. tab-item:: Include dirs
       :sync: include-dirs
-   
+
       .. code-block:: sh
-   
+
          <INSTALL_DIR>/runtime/include
-   
+
    .. tab-item:: Path to libs
       :sync: path-libs
-   
+
       .. code-block:: sh
-   
+
          <INSTALL_DIR>/runtime/lib/intel64/Release
-   
+
    .. tab-item:: Shared libs
       :sync: shared-libs
-   
+
       .. code-block:: sh
-   
+
          // UNIX systems
          openvino.so
-   
+
          // Windows
          openvino.dll
-   
+
    .. tab-item:: (Windows) .lib files
       :sync: windows-lib-files
-   
+
       .. code-block:: sh
-   
+
          openvino.lib
-   
+
 
 Clearer Library Structure for Deployment
 ########################################
@@ -219,31 +219,30 @@ Below are detailed comparisons of the library structure between OpenVINO 2022.1
   * ``openvino_onnx_frontend`` is used to read ONNX models instead of ``inference_engine_onnx_reader`` (with its dependencies).
   * ``openvino_paddle_frontend`` is added in 2022.1 to read PaddlePaddle models.
 
-<!-----
-Older versions of OpenVINO had several core libraries and plugin modules:
-- Core: ``inference_engine``, ``ngraph``, ``inference_engine_transformations``, ``inference_engine_lp_transformations``
-- Optional ``inference_engine_preproc`` preprocessing library (if ``InferenceEngine::PreProcessInfo::setColorFormat`` or ``InferenceEngine::PreProcessInfo::setResizeAlgorithm`` are used)
-- Plugin libraries:
- - ``MKLDNNPlugin`` for :doc:`CPU <openvino_docs_OV_UG_supported_plugins_CPU>` device
- - ``clDNNPlugin`` for :doc:`GPU <openvino_docs_OV_UG_supported_plugins_GPU>` device
- - ``MultiDevicePlugin`` for :doc:`Multi-device execution <openvino_docs_OV_UG_Running_on_multiple_devices>`
- - others
-- Plugins to read and convert a model:
- - ``inference_engine_ir_reader`` to read OpenVINO IR
- - ``inference_engine_onnx_reader`` (with its dependencies) to read ONNX models
-Now, the modularity is more clear:
-- A single core library with all the functionality ``openvino`` for C++ runtime
-- ``openvino_c`` with Inference Engine API C interface
-- **Deprecated** Optional ``openvino_gapi_preproc`` preprocessing library (if ``InferenceEngine::PreProcessInfo::setColorFormat`` or ``InferenceEngine::PreProcessInfo::setResizeAlgorithm`` are used)
- - Use :doc:`preprocessing capabilities of OpenVINO API 2.0 <openvino_2_0_preprocessing>`
-- Plugin libraries with clear names:
- - ``openvino_intel_cpu_plugin``
- - ``openvino_intel_gpu_plugin``
- - ``openvino_auto_plugin``
- - others
-- Plugins to read and convert models:
- - ``openvino_ir_frontend`` to read OpenVINO IR
- - ``openvino_onnx_frontend`` to read ONNX models
- - ``openvino_paddle_frontend`` to read Paddle models
----->
+
+.. Older versions of OpenVINO had several core libraries and plugin modules:
+.. - Core: ``inference_engine``, ``ngraph``, ``inference_engine_transformations``, ``inference_engine_lp_transformations``
+.. - Optional ``inference_engine_preproc`` preprocessing library (if ``InferenceEngine::PreProcessInfo::setColorFormat`` or ``InferenceEngine::PreProcessInfo::setResizeAlgorithm`` are used)
+.. - Plugin libraries:
+..  - ``MKLDNNPlugin`` for :doc:`CPU <openvino_docs_OV_UG_supported_plugins_CPU>` device
+..  - ``clDNNPlugin`` for :doc:`GPU <openvino_docs_OV_UG_supported_plugins_GPU>` device
+..  - ``MultiDevicePlugin`` for :doc:`Multi-device execution <openvino_docs_OV_UG_Running_on_multiple_devices>`
+..  - others
+.. - Plugins to read and convert a model:
+..  - ``inference_engine_ir_reader`` to read OpenVINO IR
+..  - ``inference_engine_onnx_reader`` (with its dependencies) to read ONNX models
+.. Now, the modularity is more clear:
+.. - A single core library with all the functionality ``openvino`` for C++ runtime
+.. - ``openvino_c`` with Inference Engine API C interface
+.. - **Deprecated** Optional ``openvino_gapi_preproc`` preprocessing library (if ``InferenceEngine::PreProcessInfo::setColorFormat`` or ``InferenceEngine::PreProcessInfo::setResizeAlgorithm`` are used)
+..  - Use :doc:`preprocessing capabilities of OpenVINO API 2.0 <openvino_2_0_preprocessing>`
+.. - Plugin libraries with clear names:
+..  - ``openvino_intel_cpu_plugin``
+..  - ``openvino_intel_gpu_plugin``
+..  - ``openvino_auto_plugin``
+..  - others
+.. - Plugins to read and convert models:
+..  - ``openvino_ir_frontend`` to read OpenVINO IR
+..  - ``openvino_onnx_frontend`` to read ONNX models
+..  - ``openvino_paddle_frontend`` to read Paddle models
 
diff --git a/...installing-openvino-windows-header/installing-openvino-from-archive-windows.rst b/...installing-openvino-windows-header/installing-openvino-from-archive-windows.rst
@@ -238,13 +238,3 @@ Additional Resources
 * Pre-trained deep learning models: :ref:`Overview of OpenVINO™ Toolkit Pre-Trained Models <model zoo>`
 * IoT libraries and code samples in the GitHUB repository: `Intel® IoT Developer Kit <https://github.com/intel-iot-devkit>`__
 
-<!---
-   To learn more about converting models from specific frameworks, go to:
-   * :ref:`Convert Your Caffe Model <convert model caffe>`
-   * :ref:`Convert Your TensorFlow Model <convert model tf>`
-   * :ref:`Convert Your TensorFlow Lite Model <convert model tfl>`
-   * :ref:`Convert Your Apache MXNet Model <convert model mxnet>`
-   * :ref:`Convert Your Kaldi Model <convert model kaldi>`
-   * :ref:`Convert Your ONNX Model <convert model onnx>`
---->
-