comment fix (#22557)
This commit is contained in:
Maciej Smyk
GitHub
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@ -5,8 +5,8 @@ Installation & Deployment
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.. meta::
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:description: OpenVINO™ API 2.0 focuses on the use of development tools and
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deployment of applications, it also simplifies migration from
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:description: OpenVINO™ API 2.0 focuses on the use of development tools and
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deployment of applications, it also simplifies migration from
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different frameworks to OpenVINO.
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@ -18,7 +18,7 @@ One of the main concepts for OpenVINO™ API 2.0 is being "easy to use", which i
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* Development and deployment of OpenVINO-based applications.
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To accomplish that, the 2022.1 release OpenVINO introduced significant changes to the installation
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To accomplish that, the 2022.1 release OpenVINO introduced significant changes to the installation
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and deployment processes. Further changes were implemented in 2023.1, aiming at making the installation
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process even simpler.
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@ -27,8 +27,8 @@ process even simpler.
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These instructions are largely deprecated and should be used for versions prior to 2023.1.
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The OpenVINO Development Tools package is being deprecated and will be discontinued entirely in 2025.
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With this change, the OpenVINO Runtime package has become the default choice for installing the
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software. It now includes all components necessary to utilize OpenVINO's functionality.
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With this change, the OpenVINO Runtime package has become the default choice for installing the
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software. It now includes all components necessary to utilize OpenVINO's functionality.
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@ -68,7 +68,7 @@ In OpenVINO 2022.1 and later, you can install the development tools only from a
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.. code-block:: sh
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$ python3 -m pip install -r <INSTALL_DIR>/tools/requirements_tf.txt
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$ python3 -m pip install -r <INSTALL_DIR>/tools/requirements_tf.txt
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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).
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@ -81,7 +81,7 @@ Then, the tools can be used by commands like:
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$ pot -h
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Installation of any other dependencies is not required. For more details on the installation steps, see the
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Installation of any other dependencies is not required. For more details on the installation steps, see the
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`Install OpenVINO Development Tools <https://docs.openvino.ai/2023.3/openvino_docs_install_guides_install_dev_tools.html>`__ prior to OpenVINO 2023.1.
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Interface Changes for Building C/C++ Applications
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@ -127,36 +127,36 @@ It is possible to build applications without the CMake interface by using: MSVC
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.. tab-item:: Include dirs
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:sync: include-dirs
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.. code-block:: sh
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<INSTALL_DIR>/deployment_tools/inference_engine/include
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<INSTALL_DIR>/deployment_tools/ngraph/include
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.. tab-item:: Path to libs
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:sync: path-libs
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.. code-block:: sh
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<INSTALL_DIR>/deployment_tools/inference_engine/lib/intel64/Release
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<INSTALL_DIR>/deployment_tools/ngraph/lib/
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.. tab-item:: Shared libs
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:sync: shared-libs
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.. code-block:: sh
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// UNIX systems
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inference_engine.so ngraph.so
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// Windows
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inference_engine.dll ngraph.dll
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.. tab-item:: (Windows) .lib files
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:sync: windows-lib-files
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.. code-block:: sh
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ngraph.lib
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inference_engine.lib
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@ -166,36 +166,36 @@ It is possible to build applications without the CMake interface by using: MSVC
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.. tab-item:: Include dirs
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:sync: include-dirs
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.. code-block:: sh
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<INSTALL_DIR>/runtime/include
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.. tab-item:: Path to libs
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:sync: path-libs
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.. code-block:: sh
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<INSTALL_DIR>/runtime/lib/intel64/Release
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.. tab-item:: Shared libs
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:sync: shared-libs
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.. code-block:: sh
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// UNIX systems
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openvino.so
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// Windows
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openvino.dll
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.. tab-item:: (Windows) .lib files
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:sync: windows-lib-files
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.. code-block:: sh
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openvino.lib
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Clearer Library Structure for Deployment
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########################################
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@ -219,31 +219,30 @@ Below are detailed comparisons of the library structure between OpenVINO 2022.1
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* ``openvino_onnx_frontend`` is used to read ONNX models instead of ``inference_engine_onnx_reader`` (with its dependencies).
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* ``openvino_paddle_frontend`` is added in 2022.1 to read PaddlePaddle models.
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<!-----
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Older versions of OpenVINO had several core libraries and plugin modules:
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- Core: ``inference_engine``, ``ngraph``, ``inference_engine_transformations``, ``inference_engine_lp_transformations``
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- Optional ``inference_engine_preproc`` preprocessing library (if ``InferenceEngine::PreProcessInfo::setColorFormat`` or ``InferenceEngine::PreProcessInfo::setResizeAlgorithm`` are used)
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- Plugin libraries:
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- ``MKLDNNPlugin`` for :doc:`CPU <openvino_docs_OV_UG_supported_plugins_CPU>` device
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- ``clDNNPlugin`` for :doc:`GPU <openvino_docs_OV_UG_supported_plugins_GPU>` device
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- ``MultiDevicePlugin`` for :doc:`Multi-device execution <openvino_docs_OV_UG_Running_on_multiple_devices>`
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- others
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- Plugins to read and convert a model:
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- ``inference_engine_ir_reader`` to read OpenVINO IR
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- ``inference_engine_onnx_reader`` (with its dependencies) to read ONNX models
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Now, the modularity is more clear:
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- A single core library with all the functionality ``openvino`` for C++ runtime
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- ``openvino_c`` with Inference Engine API C interface
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- **Deprecated** Optional ``openvino_gapi_preproc`` preprocessing library (if ``InferenceEngine::PreProcessInfo::setColorFormat`` or ``InferenceEngine::PreProcessInfo::setResizeAlgorithm`` are used)
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- Use :doc:`preprocessing capabilities of OpenVINO API 2.0 <openvino_2_0_preprocessing>`
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- Plugin libraries with clear names:
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- ``openvino_intel_cpu_plugin``
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- ``openvino_intel_gpu_plugin``
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- ``openvino_auto_plugin``
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- others
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- Plugins to read and convert models:
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- ``openvino_ir_frontend`` to read OpenVINO IR
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- ``openvino_onnx_frontend`` to read ONNX models
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- ``openvino_paddle_frontend`` to read Paddle models
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---->
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.. Older versions of OpenVINO had several core libraries and plugin modules:
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.. - Core: ``inference_engine``, ``ngraph``, ``inference_engine_transformations``, ``inference_engine_lp_transformations``
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.. - Optional ``inference_engine_preproc`` preprocessing library (if ``InferenceEngine::PreProcessInfo::setColorFormat`` or ``InferenceEngine::PreProcessInfo::setResizeAlgorithm`` are used)
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.. - Plugin libraries:
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.. - ``MKLDNNPlugin`` for :doc:`CPU <openvino_docs_OV_UG_supported_plugins_CPU>` device
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.. - ``clDNNPlugin`` for :doc:`GPU <openvino_docs_OV_UG_supported_plugins_GPU>` device
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.. - ``MultiDevicePlugin`` for :doc:`Multi-device execution <openvino_docs_OV_UG_Running_on_multiple_devices>`
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.. - others
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.. - Plugins to read and convert a model:
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.. - ``inference_engine_ir_reader`` to read OpenVINO IR
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.. - ``inference_engine_onnx_reader`` (with its dependencies) to read ONNX models
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.. Now, the modularity is more clear:
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.. - A single core library with all the functionality ``openvino`` for C++ runtime
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.. - ``openvino_c`` with Inference Engine API C interface
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.. - **Deprecated** Optional ``openvino_gapi_preproc`` preprocessing library (if ``InferenceEngine::PreProcessInfo::setColorFormat`` or ``InferenceEngine::PreProcessInfo::setResizeAlgorithm`` are used)
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.. - Use :doc:`preprocessing capabilities of OpenVINO API 2.0 <openvino_2_0_preprocessing>`
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.. - Plugin libraries with clear names:
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.. - ``openvino_intel_cpu_plugin``
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.. - ``openvino_intel_gpu_plugin``
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.. - ``openvino_auto_plugin``
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.. - others
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.. - Plugins to read and convert models:
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.. - ``openvino_ir_frontend`` to read OpenVINO IR
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.. - ``openvino_onnx_frontend`` to read ONNX models
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.. - ``openvino_paddle_frontend`` to read Paddle models
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@ -238,13 +238,3 @@ Additional Resources
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* Pre-trained deep learning models: :ref:`Overview of OpenVINO™ Toolkit Pre-Trained Models <model zoo>`
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* IoT libraries and code samples in the GitHUB repository: `Intel® IoT Developer Kit <https://github.com/intel-iot-devkit>`__
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<!---
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To learn more about converting models from specific frameworks, go to:
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* :ref:`Convert Your Caffe Model <convert model caffe>`
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* :ref:`Convert Your TensorFlow Model <convert model tf>`
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* :ref:`Convert Your TensorFlow Lite Model <convert model tfl>`
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* :ref:`Convert Your Apache MXNet Model <convert model mxnet>`
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* :ref:`Convert Your Kaldi Model <convert model kaldi>`
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* :ref:`Convert Your ONNX Model <convert model onnx>`
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--->
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