[DOCS] feature transition section (#19506)
* [DOCS] legacy features section * pass 2 of extensions * Apply suggestions from code review --------- Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>
This commit is contained in:
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Interactive Tutorials (Python) <tutorials>
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Sample Applications (Python & C++) <openvino_docs_OV_UG_Samples_Overview>
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OpenVINO API 2.0 Transition <openvino_2_0_transition_guide>
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This section will help you get a hands-on experience with OpenVINO even if you are just starting
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@ -13,7 +13,6 @@
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Supported_Model_Formats
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openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide
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omz_tools_downloader
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Every deep learning workflow begins with obtaining a model. You can choose to prepare a custom one, use a ready-made solution and adjust it to your needs, or even download and run a pre-trained network from an online database, such as `TensorFlow Hub <https://tfhub.dev/>`__, `Hugging Face <https://huggingface.co/>`__, or `Torchvision models <https://pytorch.org/hub/>`__.
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@ -3,7 +3,7 @@
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@sphinxdirective
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.. meta::
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:description: OpenVINO™ is an ecosystem of utilities that have advanced capabilities, which help develop deep learning solutions.
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:description: OpenVINO™ ecosystem offers various resources for developing deep learning solutions.
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.. toctree::
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ote_documentation
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datumaro_documentation
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ovsa_get_started
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openvino_docs_tuning_utilities
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OpenVINO™ is not just one tool. It is an expansive ecosystem of utilities, providing a comprehensive workflow for deep learning solution development. Learn more about each of them to reach the full potential of OpenVINO™ Toolkit.
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@ -60,39 +59,6 @@ More resources:
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* `GitHub <https://github.com/openvinotoolkit/datumaro>`__
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* `Documentation <https://openvinotoolkit.github.io/datumaro/stable/docs/get-started/introduction.html>`__
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**Compile Tool**
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Compile tool is now deprecated. If you need to compile a model for inference on a specific device, use the following script:
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.. tab-set::
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.. tab-item:: Python
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:sync: py
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.. doxygensnippet:: docs/snippets/export_compiled_model.py
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:language: python
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:fragment: [export_compiled_model]
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.. tab-item:: C++
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:sync: cpp
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.. doxygensnippet:: docs/snippets/export_compiled_model.cpp
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:language: cpp
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:fragment: [export_compiled_model]
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To learn which device supports the import / export functionality, see the :doc:`feature support matrix <openvino_docs_OV_UG_Working_with_devices>`.
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For more details on preprocessing steps, refer to the :doc:`Optimize Preprocessing <openvino_docs_OV_UG_Preprocessing_Overview>`. To compile the model with advanced preprocessing capabilities, refer to the :doc:`Use Case - Integrate and Save Preprocessing Steps Into OpenVINO IR <openvino_docs_OV_UG_Preprocess_Usecase_save>`, which shows how to have all the preprocessing in the compiled blob.
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**DL Workbench**
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A web-based tool for deploying deep learning models. Built on the core of OpenVINO and equipped with a graphics user interface, DL Workbench is a great way to explore the possibilities of the OpenVINO workflow, import, analyze, optimize, and build your pre-trained models. You can do all that by visiting `Intel® Developer Cloud <https://software.intel.com/content/www/us/en/develop/tools/devcloud.html>`__ and launching DL Workbench online.
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**OpenVINO™ integration with TensorFlow (OVTF)**
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OpenVINO™ Integration with TensorFlow will no longer be supported as of OpenVINO release 2023.0. As part of the 2023.0 release, OpenVINO will feature a significantly enhanced TensorFlow user experience within native OpenVINO without needing offline model conversions. :doc:`Learn more <openvino_docs_MO_DG_TensorFlow_Frontend>`.
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@endsphinxdirective
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139
docs/Documentation/openvino_legacy_features.md
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docs/Documentation/openvino_legacy_features.md
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# Legacy Features and Components {#openvino_legacy_features}
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@sphinxdirective
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.. toctree::
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:maxdepth: 1
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:hidden:
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OpenVINO Development Tools package <openvino_docs_install_guides_install_dev_tools>
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OpenVINO API 2.0 transition <openvino_2_0_transition_guide>
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Open Model ZOO <model_zoo>
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Apache MXNet, Caffe, and Kaldi <mxnet_caffe_kaldi>
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Post-training Optimization Tool <pot_introduction>
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Since OpenVINO has grown very rapidly in recent years, some of its features
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and components have been replaced by other solutions. Some of them are still
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supported to assure OpenVINO users are given enough time to adjust their projects,
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before the features are fully discontinued.
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This section will give you an overview of these major changes and tell you how
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you can proceed to get the best experience and results with the current OpenVINO
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offering.
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| **OpenVINO Development Tools Package**
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| *New solution:* OpenVINO Runtime includes all supported components
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| *Old solution:* discontinuation planned for OpenVINO 2025.0
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| OpenVINO Development Tools used to be the OpenVINO package with tools for
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advanced operations on models, such as Model conversion API, Benchmark Tool,
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Accuracy Checker, Annotation Converter, Post-Training Optimization Tool,
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and Open Model Zoo tools. Most of these tools have been either removed,
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replaced by other solutions, or moved to the OpenVINO Runtime package.
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| :doc:`See how to install Development Tools <openvino_docs_install_guides_install_dev_tools>`
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| **Model Optimizer**
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| *New solution:* Direct model support and OpenVINO Converter (OVC)
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| *Old solution:* Model Optimizer discontinuation planned for OpenVINO 2025.0
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|
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| Model Optimizer's role was largely reduced when all major model frameworks became
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supported directly. For the sole purpose of converting model files explicitly,
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it has been replaced with a more light-weight and efficient solution, the
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OpenVINO Converter (launched with OpenVINO 2023.1).
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.. :doc:`See how to use OVC <?????????>`
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| **Open Model ZOO**
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| *New solution:* users are encouraged to use public model repositories
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| *Old solution:* discontinuation planned for OpenVINO 2024.0
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| Open Model ZOO provided a collection of models prepared for use with OpenVINO,
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and a small set of tools enabling a level of automation for the process.
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Since the tools have been mostly replaced by other solutions and several
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other model repositories have recently grown in size and popularity,
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Open Model ZOO will no longer be maintained. You may still use its resources
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until they are fully removed.
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| :doc:`See the Open Model ZOO documentation <model_zoo>`
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| `Check the OMZ GitHub project <https://github.com/openvinotoolkit/open_model_zoo>`__
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| **Apache MXNet, Caffe, and Kaldi model formats**
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| *New solution:* conversion to ONNX via external tools
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| *Old solution:* model support will be discontinued with OpenVINO 2024.0
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| Since these three model formats proved to be far less popular among OpenVINO users
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than the remaining ones, their support has been discontinued. Converting them to the
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ONNX format is a possible way of retaining them in the OpenVINO-based pipeline.
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| :doc:`See the previous conversion instructions <mxnet_caffe_kaldi>`
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| :doc:`See the currently supported frameworks <Supported_Model_Formats>`
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| **Post-training Optimization Tool (POT)**
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| *New solution:* NNCF extended in OpenVINO 2023.0
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| *Old solution:* POT discontinuation planned for 2024
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| Neural Network Compression Framework (NNCF) now offers the same functionality as POT,
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apart from its original feature set. It is currently the default tool for performing
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both, post-training and quantization optimizations, while POT is considered deprecated.
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| :doc:`See the deprecated POT documentation <pot_introduction>`
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| :doc:`See how to use NNCF for model optimization <openvino_docs_model_optimization_guide>`
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| `Check the NNCF GitHub project, including documentation <https://github.com/openvinotoolkit/nncf>`__
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| **Old Inference API 1.0**
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| *New solution:* API 2.0 launched in OpenVINO 2022.1
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| *Old solution:* discontinuation planned for OpenVINO 2024.0
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| API 1.0 (Inference Engine and nGraph) is now deprecated. It can still be
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used but is not recommended. Its discontinuation is planned for 2024.
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| :doc:`See how to transition to API 2.0 <openvino_2_0_transition_guide>`
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| **Compile tool**
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| *New solution:* the tool is no longer needed
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| *Old solution:* deprecated in OpenVINO 2023.0
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|
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| Compile tool is now deprecated. If you need to compile a model for inference on
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a specific device, use the following script:
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.. tab-set::
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.. tab-item:: Python
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:sync: py
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.. doxygensnippet:: docs/snippets/export_compiled_model.py
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:language: python
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:fragment: [export_compiled_model]
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.. tab-item:: C++
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:sync: cpp
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.. doxygensnippet:: docs/snippets/export_compiled_model.cpp
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:language: cpp
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:fragment: [export_compiled_model]
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| :doc:`see which devices support import / export <openvino_docs_OV_UG_Working_with_devices>`
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| :doc:`Learn more on preprocessing steps <openvino_docs_OV_UG_Preprocessing_Overview>`
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| :doc:`See how to integrate and save preprocessing steps into OpenVINO IR <openvino_docs_OV_UG_Preprocess_Usecase_save>`
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| **DL Workbench**
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| *New solution:* DevCloud version
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| *Old solution:* local distribution discontinued in OpenVINO 2022.3
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| The stand-alone version of DL Workbench, a GUI tool for previewing and benchmarking
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deep learning models, has been discontinued. You can use its cloud version:
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| `Intel® Developer Cloud for the Edge <https://www.intel.com/content/www/us/en/developer/tools/devcloud/edge/overview.html>`__.
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| **OpenVINO™ integration with TensorFlow (OVTF)**
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| *New solution:* Direct model support and OpenVINO Converter (OVC)
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| *Old solution:* discontinued in OpenVINO 2023.0
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|
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| OpenVINO™ Integration with TensorFlow is longer supported, as OpenVINO now features a
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native TensorFlow support, significantly enhancing user experience with no need for
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explicit model conversion.
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| :doc:`Learn more <openvino_docs_MO_DG_TensorFlow_Frontend>`
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@endsphinxdirective
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# MX Net, Caffe, and Kaldi model formats {#mxnet_caffe_kaldi}
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@sphinxdirective
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.. toctree::
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:maxdepth: 1
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:hidden:
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openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_MxNet
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openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Caffe
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openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Kaldi
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openvino_docs_MO_DG_prepare_model_convert_model_mxnet_specific_Convert_GluonCV_Models
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openvino_docs_MO_DG_prepare_model_convert_model_mxnet_specific_Convert_Style_Transfer_From_MXNet
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openvino_docs_MO_DG_prepare_model_convert_model_kaldi_specific_Aspire_Tdnn_Model
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The following articles present the deprecated conversion method for MX Net, Caffe,
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and Kaldi model formats.
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:doc:`Apache MX Net conversion <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_MxNet>`
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:doc:`Caffe conversion <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Caffe>`
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:doc:`Kaldi conversion <openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Kaldi>`
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Here are three examples of conversion for particular models.
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:doc:`MXNet GluonCV conversion <openvino_docs_MO_DG_prepare_model_convert_model_mxnet_specific_Convert_GluonCV_Models>`
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:doc:`MXNet Style Transfer Model conversion <openvino_docs_MO_DG_prepare_model_convert_model_mxnet_specific_Convert_Style_Transfer_From_MXNet>`
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:doc:`Kaldi ASpIRE Chain TDNN Model conversion <openvino_docs_MO_DG_prepare_model_convert_model_kaldi_specific_Aspire_Tdnn_Model>`
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@endsphinxdirective
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@ -31,25 +31,26 @@
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openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_RCAN
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openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_RNNT
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openvino_docs_MO_DG_prepare_model_convert_model_pytorch_specific_Convert_YOLACT
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openvino_docs_MO_DG_prepare_model_convert_model_mxnet_specific_Convert_GluonCV_Models
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openvino_docs_MO_DG_prepare_model_convert_model_mxnet_specific_Convert_Style_Transfer_From_MXNet
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openvino_docs_MO_DG_prepare_model_convert_model_kaldi_specific_Aspire_Tdnn_Model
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.. meta::
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:description: Get to know conversion methods for specific TensorFlow, ONNX, PyTorch, MXNet, and Kaldi models.
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This section provides a set of tutorials that demonstrate conversion methods for specific
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TensorFlow, ONNX, PyTorch, MXNet, and Kaldi models, which does not necessarily cover your case.
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TensorFlow, ONNX, and PyTorch models. Note that these instructions do not cover all use
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cases and may not reflect your particular needs.
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Before studying the tutorials, try to convert the model out-of-the-box by specifying only the
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``--input_model`` parameter in the command line.
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.. warning::
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Note that OpenVINO support for Apache MXNet, Caffe, and Kaldi is currently being deprecated and will be removed entirely in the future.
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.. note::
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Apache MXNet, Caffe, and Kaldi are no longer directly supported by OpenVINO.
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They will remain available for some time, so make sure to transition to other
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frameworks before they are fully discontinued.
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You will find a collection of :doc:`Python tutorials <tutorials>` written for running on Jupyter notebooks
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that provide an introduction to the OpenVINO™ toolkit and explain how to use the Python API and tools for
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optimized deep learning inference.
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@endsphinxdirective
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@endsphinxdirective
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openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_PyTorch
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openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_TensorFlow_Lite
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openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Paddle
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openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_MxNet
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openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Caffe
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openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_Kaldi
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openvino_docs_MO_DG_prepare_model_convert_model_tutorials
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.. meta::
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@ -24,7 +24,7 @@ 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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OpenVINO Development Tools is being deprecated and will be discontinued entirely in 2025.
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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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@ -12,9 +12,10 @@
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:hidden:
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API Reference <api/api_reference>
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OpenVINO IR format and Operation Sets <openvino_ir>
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Tool Ecosystem <openvino_ecosystem>
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Legacy Features <openvino_legacy_features>
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OpenVINO Extensibility <openvino_docs_Extensibility_UG_Intro>
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OpenVINO IR format and Operation Sets <openvino_ir>
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Media Processing and CV Libraries <media_processing_cv_libraries>
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OpenVINO™ Security <openvino_docs_security_guide_introduction>
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@ -131,7 +131,6 @@ Feature Overview
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LEARN OPENVINO <learn_openvino>
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OPENVINO WORKFLOW <openvino_workflow>
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DOCUMENTATION <documentation>
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MODEL ZOO <model_zoo>
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RESOURCES <resources>
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RELEASE NOTES <release_notes>
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@ -14,7 +14,7 @@ OpenVINO Development Tools is a set of utilities that make it easy to develop an
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* Post-Training Optimization Tool
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* Model Downloader and other Open Model Zoo tools
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The instructions on this page show how to install OpenVINO Development Tools. If you are a Python developer, it only takes a few simple steps to install the tools with PyPI. If you are developing in C++, OpenVINO Runtime must be installed separately before installing OpenVINO Development Tools.
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The instructions on this page show how to install OpenVINO Development Tools. If you are a Python developer, it only takes a few simple steps to install the tools with PyPI. If you are developing in C/C++, OpenVINO Runtime must be installed separately before installing OpenVINO Development Tools.
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In both cases, Python 3.7 - 3.11 needs to be installed on your machine before starting.
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@ -31,10 +31,10 @@ If you are a Python developer, follow the steps in the :ref:`Installing OpenVINO
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.. _cpp_developers:
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For C++ Developers
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##################
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For C/C++ Developers
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#######################
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If you are a C++ developer, you must first install OpenVINO Runtime separately to set up the C++ libraries, sample code, and dependencies for building applications with OpenVINO. These files are not included with the PyPI distribution. See the :doc:`Selector Tool <openvino_docs_install_guides_overview>` page to install OpenVINO Runtime from an archive file for your operating system.
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If you are a C/C++ developer, you must first install OpenVINO Runtime separately to set up the C/C++ libraries, sample code, and dependencies for building applications with OpenVINO. These files are not included with the PyPI distribution. See the :doc:`Selector Tool <openvino_docs_install_guides_overview>` page to install OpenVINO Runtime from an archive file for your operating system.
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Once OpenVINO Runtime is installed, you may install OpenVINO Development Tools for access to tools like ``mo``, Model Downloader, Benchmark Tool, and other utilities that will help you optimize your model and develop your application. Follow the steps in the :ref:`Installing OpenVINO Development Tools <install_dev_tools>` section on this page to install it.
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@ -162,7 +162,7 @@ To verify the package is properly installed, run the command below (this may tak
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You will see the help message for ``mo`` if installation finished successfully. If you get an error, refer to the :doc:`Troubleshooting Guide <openvino_docs_get_started_guide_troubleshooting>` for possible solutions.
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Congratulations! You finished installing OpenVINO Development Tools with C++ capability. Now you can start exploring OpenVINO's functionality through example C++ applications. See the "What's Next?" section to learn more!
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Congratulations! You finished installing OpenVINO Development Tools with C/C++ capability. Now you can start exploring OpenVINO's functionality through example C/C++ applications. See the "What's Next?" section to learn more!
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What's Next?
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############
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@ -72,7 +72,7 @@ Intel® GNA driver for Windows is available through Windows Update.
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What’s Next?
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####################
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Now you are ready to try out OpenVINO™. You can use the following tutorials to write your applications using Python and C++.
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Now you are ready to try out OpenVINO™. You can use the following tutorials to write your applications using Python and C/C++.
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* Developing in Python:
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@ -80,7 +80,7 @@ Now you are ready to try out OpenVINO™. You can use the following tutorials to
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* `Start with ONNX and PyTorch models with OpenVINO™ <notebooks/102-pytorch-onnx-to-openvino-with-output.html>`__
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* `Start with PaddlePaddle models with OpenVINO™ <notebooks/103-paddle-to-openvino-classification-with-output.html>`__
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* Developing in C++:
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* Developing in C/C++:
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* :doc:`Image Classification Async C++ Sample <openvino_inference_engine_samples_classification_sample_async_README>`
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* :doc:`Hello Classification C++ Sample <openvino_inference_engine_samples_hello_classification_README>`
|
||||
|
||||
@ -53,7 +53,7 @@ Intel® NPU driver for Windows is available through Windows Update.
|
||||
What’s Next?
|
||||
####################
|
||||
|
||||
Now you are ready to try out OpenVINO™. You can use the following tutorials to write your applications using Python and C++.
|
||||
Now you are ready to try out OpenVINO™. You can use the following tutorials to write your applications using Python and C/C++.
|
||||
|
||||
* Developing in Python:
|
||||
|
||||
@ -61,7 +61,7 @@ Now you are ready to try out OpenVINO™. You can use the following tutorials to
|
||||
* `Start with ONNX and PyTorch models with OpenVINO™ <notebooks/102-pytorch-onnx-to-openvino-with-output.html>`__
|
||||
* `Start with PaddlePaddle models with OpenVINO™ <notebooks/103-paddle-to-openvino-classification-with-output.html>`__
|
||||
|
||||
* Developing in C++:
|
||||
* Developing in C/C++:
|
||||
|
||||
* :doc:`Image Classification Async C++ Sample <openvino_inference_engine_samples_classification_sample_async_README>`
|
||||
* :doc:`Hello Classification C++ Sample <openvino_inference_engine_samples_hello_classification_README>`
|
||||
|
||||
@ -17,26 +17,34 @@
|
||||
For GNA <openvino_docs_install_guides_configurations_for_intel_gna>
|
||||
|
||||
|
||||
For certain use cases, you may need to install additional software, to get the full
|
||||
potential of OpenVINO™. Check the following list for components pertaining to your
|
||||
workflow:
|
||||
|
||||
| **Open Computer Vision Library**
|
||||
| OpenCV is used to extend the capabilities of some models, for example enhance some of
|
||||
OpenVINO samples, when used as a dependency in compilation. To install OpenCV for OpenVINO, see the
|
||||
`instructions on GtHub <https://github.com/opencv/opencv/wiki/BuildOpenCV4OpenVINO>`__.
|
||||
For certain use cases, you may need to install additional software, to use the full
|
||||
potential of OpenVINO™. Check the following list for components for elements used in
|
||||
your workflow:
|
||||
|
||||
| **GPU drivers**
|
||||
| If you want to run inference on a GPU, make sure your GPU's drivers are properly installed.
|
||||
See the :doc:`guide on GPU configuration <openvino_docs_install_guides_configurations_for_intel_gpu>`
|
||||
for details.
|
||||
|
||||
| **NPU drivers**
|
||||
| Intel's Neural Processing Unit introduced with the Intel® Core™ Ultra generation of CPUs
|
||||
(formerly known as Meteor Lake), is a low-power solution for offloading neural network computation.
|
||||
If you want to run inference on an NPU, make sure your NPU's drivers are properly installed.
|
||||
See the :doc:`guide on NPU configuration <openvino_docs_install_guides_configurations_for_intel_npu>`
|
||||
for details.
|
||||
|
||||
| **GNA drivers**
|
||||
| If you want to run inference on a GNA (note that it is currently being deprecated and will no longer
|
||||
be supported beyond 2023.2), make sure your GPU's drivers are properly installed. See the
|
||||
:doc:`guide on GNA configuration <openvino_docs_install_guides_configurations_for_intel_gna>`
|
||||
for details.
|
||||
|
||||
| **Open Computer Vision Library**
|
||||
| OpenCV is used to extend the capabilities of some models, for example enhance some of
|
||||
OpenVINO samples, when used as a dependency in compilation. To install OpenCV for OpenVINO, see the
|
||||
`instructions on GtHub <https://github.com/opencv/opencv/wiki/BuildOpenCV4OpenVINO>`__.
|
||||
|
||||
|
||||
|
||||
@endsphinxdirective
|
||||
|
||||
|
||||
@ -10,7 +10,7 @@
|
||||
|
||||
Note that the APT distribution:
|
||||
|
||||
* offers both C++ and Python APIs
|
||||
* offers both C/C++ and Python APIs
|
||||
* does not offer support for GNA and NPU inference
|
||||
* additionally includes code samples
|
||||
* is dedicated to Linux users.
|
||||
|
||||
@ -10,9 +10,9 @@
|
||||
|
||||
Note that the `Homebrew <https://brew.sh/>`__ distribution:
|
||||
|
||||
* offers both C++ and Python APIs
|
||||
* offers both C/C++ and Python APIs
|
||||
* does not offer support for GNA and NPU inference
|
||||
* is dedicated to macOS users.
|
||||
* is dedicated to macOS and Linux users.
|
||||
|
||||
|
||||
.. tab-set::
|
||||
|
||||
@ -11,7 +11,7 @@
|
||||
|
||||
Note that the Conda Forge distribution:
|
||||
|
||||
* offers both C++ and Python APIs
|
||||
* offers both C/C++ and Python APIs
|
||||
* does not offer support for GNA and NPU inference
|
||||
* is dedicated to users of all major OSs: Windows, Linux, macOS.
|
||||
|
||||
|
||||
@ -11,7 +11,7 @@
|
||||
|
||||
Note that the Archive distribution:
|
||||
|
||||
* offers both C++ and Python APIs
|
||||
* offers both C/C++ and Python APIs
|
||||
* additionally includes code samples
|
||||
* is dedicated to users of all major OSs: Windows, Linux, macOS
|
||||
* may offer different hardware support under different operating systems
|
||||
@ -19,20 +19,17 @@
|
||||
|
||||
.. dropdown:: Inference Options
|
||||
|
||||
=================== ===== ===== ===== ===== ======== ============= ======== ========
|
||||
Operating System CPU GPU GNA NPU AUTO Auto-batch HETERO MULTI
|
||||
=================== ===== ===== ===== ===== ======== ============= ======== ========
|
||||
Debian9 armhf V n/a n/a n/a V V V n/a
|
||||
Debian9 arm64 V n/a n/a n/a V V V n/a
|
||||
CentOS7 x86_64 V V V n/a V V V V
|
||||
Ubuntu18 x86_64 V V V n/a V V V V
|
||||
Ubuntu20 x86_64 V V V V V V V V
|
||||
Ubuntu22 x86_64 V V V V V V V V
|
||||
RHEL8 x86_64 V V V n/a V V V V
|
||||
Windows x86_64 V V V V V V V V
|
||||
MacOS x86_64 V n/a n/a n/a V V V n/a
|
||||
MacOS arm64 V n/a n/a n/a V V V n/a
|
||||
=================== ===== ===== ===== ===== ======== ============= ======== ========
|
||||
=================== ===== ===== ===== =====
|
||||
Operating System CPU GPU GNA NPU
|
||||
=================== ===== ===== ===== =====
|
||||
Debian9 armhf V n/a n/a n/a
|
||||
Debian9 arm64 V n/a n/a n/a
|
||||
CentOS7 x86_64 V V n/a n/a
|
||||
Ubuntu18 x86_64 V V V n/a
|
||||
Ubuntu20 x86_64 V V V V
|
||||
Ubuntu22 x86_64 V V V V
|
||||
RHEL8 x86_64 V V V n/a
|
||||
=================== ===== ===== ===== =====
|
||||
|
||||
|
||||
|
||||
@ -228,13 +225,15 @@ Step 1: Download and Install the OpenVINO Core Components
|
||||
Unlink the previous link with ``sudo unlink openvino_2023``, and then re-run the command above.
|
||||
|
||||
|
||||
Congratulations, you have finished the installation! The ``/opt/intel/openvino_2023`` folder now contains
|
||||
the core components for OpenVINO. If you used a different path in Step 2, for example, ``/home/<USER>/intel/``,
|
||||
OpenVINO is now in ``/home/<USER>/intel/openvino_2023``. The path to the ``openvino_2023`` directory is
|
||||
also referred as ``<INSTALL_DIR>`` throughout the OpenVINO documentation.
|
||||
|
||||
|
||||
Congratulations, you have finished the installation! For some use cases you may still
|
||||
need to install additional components. Check the description below, as well as the
|
||||
:doc:`list of additional configurations <openvino_docs_install_guides_configurations_header>`
|
||||
to see if your case needs any of them.
|
||||
|
||||
The ``/opt/intel/openvino_2023`` folder now contains the core components for OpenVINO.
|
||||
If you used a different path in Step 2, for example, ``/home/<USER>/intel/``,
|
||||
OpenVINO is now in ``/home/<USER>/intel/openvino_2023``. The path to the ``openvino_2023``
|
||||
directory is also referred as ``<INSTALL_DIR>`` throughout the OpenVINO documentation.
|
||||
|
||||
|
||||
Step 2: Configure the Environment
|
||||
@ -263,6 +262,7 @@ The environment variables are set.
|
||||
|
||||
|
||||
|
||||
|
||||
What's Next?
|
||||
############################################################
|
||||
|
||||
|
||||
@ -11,28 +11,9 @@
|
||||
|
||||
Note that the Archive distribution:
|
||||
|
||||
* offers both C++ and Python APIs
|
||||
* offers both C/C++ and Python APIs
|
||||
* additionally includes code samples
|
||||
* is dedicated to users of all major OSs: Windows, Linux, macOS
|
||||
* may offer different hardware support under different operating systems
|
||||
(see the drop-down below for more details)
|
||||
|
||||
.. dropdown:: Inference Options
|
||||
|
||||
=================== ===== ===== ===== ===== ======== ============= ======== ========
|
||||
Operating System CPU GPU GNA NPU AUTO Auto-batch HETERO MULTI
|
||||
=================== ===== ===== ===== ===== ======== ============= ======== ========
|
||||
Debian9 armhf V n/a n/a n/a V V V n/a
|
||||
Debian9 arm64 V n/a n/a n/a V V V n/a
|
||||
CentOS7 x86_64 V V V n/a V V V V
|
||||
Ubuntu18 x86_64 V V V n/a V V V V
|
||||
Ubuntu20 x86_64 V V V V V V V V
|
||||
Ubuntu22 x86_64 V V V V V V V V
|
||||
RHEL8 x86_64 V V V n/a V V V V
|
||||
Windows x86_64 V V V V V V V V
|
||||
MacOS x86_64 V n/a n/a n/a V V V n/a
|
||||
MacOS arm64 V n/a n/a n/a V V V n/a
|
||||
=================== ===== ===== ===== ===== ======== ============= ======== ========
|
||||
|
||||
|
||||
.. tab-set::
|
||||
@ -126,10 +107,15 @@ Step 1: Install OpenVINO Core Components
|
||||
If you have already installed a previous release of OpenVINO 2023, a symbolic link to the ``openvino_2023`` folder may already exist. Unlink the previous link with ``sudo unlink openvino_2023``, and then re-run the command above.
|
||||
|
||||
|
||||
Congratulations, you have finished the installation! The ``/opt/intel/openvino_2023`` folder now contains
|
||||
the core components for OpenVINO. If you used a different path in Step 2, for example, ``/home/<USER>/intel/``,
|
||||
OpenVINO is now in ``/home/<USER>/intel/openvino_2023``. The path to the ``openvino_2023`` directory is
|
||||
also referred as ``<INSTALL_DIR>`` throughout the OpenVINO documentation.
|
||||
Congratulations, you have finished the installation! For some use cases you may still
|
||||
need to install additional components. Check the description below, as well as the
|
||||
:doc:`list of additional configurations <openvino_docs_install_guides_configurations_header>`
|
||||
to see if your case needs any of them.
|
||||
|
||||
The ``/opt/intel/openvino_2023`` folder now contains the core components for OpenVINO.
|
||||
If you used a different path in Step 2, for example, ``/home/<USER>/intel/``,
|
||||
OpenVINO is now in ``/home/<USER>/intel/openvino_2023``. The path to the ``openvino_2023``
|
||||
directory is also referred as ``<INSTALL_DIR>`` throughout the OpenVINO documentation.
|
||||
|
||||
|
||||
Step 2: Configure the Environment
|
||||
|
||||
@ -11,28 +11,9 @@
|
||||
|
||||
Note that the Archive distribution:
|
||||
|
||||
* offers both C++ and Python APIs
|
||||
* offers both C/C++ and Python APIs
|
||||
* additionally includes code samples
|
||||
* is dedicated to users of all major OSs: Windows, Linux, macOS
|
||||
* may offer different hardware support under different operating systems
|
||||
(see the drop-down below for more details)
|
||||
|
||||
.. dropdown:: Inference Options
|
||||
|
||||
=================== ===== ===== ===== ===== ======== ============= ======== ========
|
||||
Operating System CPU GPU GNA NPU AUTO Auto-batch HETERO MULTI
|
||||
=================== ===== ===== ===== ===== ======== ============= ======== ========
|
||||
Debian9 armhf V n/a n/a n/a V V V n/a
|
||||
Debian9 arm64 V n/a n/a n/a V V V n/a
|
||||
CentOS7 x86_64 V V V n/a V V V V
|
||||
Ubuntu18 x86_64 V V V n/a V V V V
|
||||
Ubuntu20 x86_64 V V V V V V V V
|
||||
Ubuntu22 x86_64 V V V V V V V V
|
||||
RHEL8 x86_64 V V V n/a V V V V
|
||||
Windows x86_64 V V V V V V V V
|
||||
MacOS x86_64 V n/a n/a n/a V V V n/a
|
||||
MacOS arm64 V n/a n/a n/a V V V n/a
|
||||
=================== ===== ===== ===== ===== ======== ============= ======== ========
|
||||
|
||||
|
||||
System Requirements
|
||||
@ -148,7 +129,17 @@ Step 1: Download and Install OpenVINO Core Components
|
||||
If you have already installed a previous release of OpenVINO 2022, a symbolic link to the ``openvino_2023`` folder may already exist. If you want to override it, navigate to the ``C:\Program Files (x86)\Intel`` folder and delete the existing linked folder before running the ``mklink`` command.
|
||||
|
||||
|
||||
Congratulations, you finished the installation! The ``C:\Program Files (x86)\Intel\openvino_2023`` folder now contains the core components for OpenVINO. If you used a different path in Step 1, you will find the ``openvino_2023`` folder there. The path to the ``openvino_2023`` directory is also referred as ``<INSTALL_DIR>`` throughout the OpenVINO documentation.
|
||||
Congratulations, you have finished the installation! For some use cases you may still
|
||||
need to install additional components. Check the description below, as well as the
|
||||
:doc:`list of additional configurations <openvino_docs_install_guides_configurations_header>`
|
||||
to see if your case needs any of them.
|
||||
|
||||
The ``C:\Program Files (x86)\Intel\openvino_2023`` folder now contains the core components for OpenVINO.
|
||||
If you used a different path in Step 1, you will find the ``openvino_2023`` folder there.
|
||||
The path to the ``openvino_2023`` directory is also referred as ``<INSTALL_DIR>``
|
||||
throughout the OpenVINO documentation.
|
||||
|
||||
|
||||
|
||||
.. _set-the-environment-variables-windows:
|
||||
|
||||
|
||||
@ -17,7 +17,7 @@
|
||||
Use APT <openvino_docs_install_guides_installing_openvino_apt>
|
||||
Use YUM <openvino_docs_install_guides_installing_openvino_yum>
|
||||
Use Conda Forge <openvino_docs_install_guides_installing_openvino_conda>
|
||||
Use VCPKG <openvino_docs_install_guides_installing_openvino_vcpkg>
|
||||
Use vcpkg <openvino_docs_install_guides_installing_openvino_vcpkg>
|
||||
Use Homebrew <openvino_docs_install_guides_installing_openvino_brew>
|
||||
Use Docker <openvino_docs_install_guides_installing_openvino_docker>
|
||||
|
||||
|
||||
@ -16,7 +16,7 @@
|
||||
Using Homebrew <openvino_docs_install_guides_installing_openvino_brew>
|
||||
From PyPI <openvino_docs_install_guides_installing_openvino_pip>
|
||||
Using Conda Forge <openvino_docs_install_guides_installing_openvino_conda>
|
||||
Use VCPKG <openvino_docs_install_guides_installing_openvino_vcpkg>
|
||||
Use vcpkg <openvino_docs_install_guides_installing_openvino_vcpkg>
|
||||
|
||||
|
||||
If you want to install OpenVINO™ Runtime on macOS, there are a few ways to accomplish this. We prepared following options for you:
|
||||
|
||||
@ -1,4 +1,4 @@
|
||||
# Install Intel® Distribution of OpenVINO™ Toolkit {#openvino_docs_install_guides_overview}
|
||||
# Install OpenVINO™ 2023.1 {#openvino_docs_install_guides_overview}
|
||||
|
||||
@sphinxdirective
|
||||
|
||||
@ -13,8 +13,7 @@
|
||||
|
||||
OpenVINO Runtime on Linux <openvino_docs_install_guides_installing_openvino_linux_header>
|
||||
OpenVINO Runtime on Windows <openvino_docs_install_guides_installing_openvino_windows_header>
|
||||
OpenVINO Runtime on macOS <openvino_docs_install_guides_installing_openvino_macos_header>
|
||||
OpenVINO Development Tools <openvino_docs_install_guides_install_dev_tools>
|
||||
OpenVINO Runtime on macOS <openvino_docs_install_guides_installing_openvino_macos_header>
|
||||
Create a Yocto Image <openvino_docs_install_guides_installing_openvino_yocto>
|
||||
|
||||
|
||||
@ -49,16 +48,12 @@
|
||||
.. dropdown:: Distribution Comparison for OpenVINO 2023.1
|
||||
|
||||
=============== ========== ====== ========= ======== ============ ==========
|
||||
Device Archives PyPI APT/YUM Conda Homebrew VCPKG
|
||||
Device Archives PyPI APT/YUM Conda Homebrew vcpkg
|
||||
=============== ========== ====== ========= ======== ============ ==========
|
||||
CPU V V V V V V
|
||||
GPU V V V V V V
|
||||
GNA V V V V V V
|
||||
NPU V V V V V V
|
||||
Auto V V V V V V
|
||||
Auto-Batch V V V V V V
|
||||
Hetero V n/a n/a n/a n/a n/a
|
||||
Multi V n/a n/a n/a n/a n/a
|
||||
GNA V n/a n/a n/a n/a n/a
|
||||
NPU V n/a n/a n/a n/a n/a
|
||||
=============== ========== ====== ========= ======== ============ ==========
|
||||
|
||||
| **Build OpenVINO from source**
|
||||
|
||||
@ -1,19 +1,18 @@
|
||||
# Install OpenVINO™ Runtime via VCPKG {#openvino_docs_install_guides_installing_openvino_vcpkg}
|
||||
# Install OpenVINO™ Runtime via vcpkg {#openvino_docs_install_guides_installing_openvino_vcpkg}
|
||||
|
||||
@sphinxdirective
|
||||
|
||||
.. meta::
|
||||
:description: Learn how to install OpenVINO™ Runtime on Windows, Linux, and macOS
|
||||
operating systems, using VCPKG.
|
||||
operating systems, using vcpkg.
|
||||
|
||||
.. note::
|
||||
|
||||
Note that the VCPKG distribution:
|
||||
Note that the vcpkg distribution:
|
||||
|
||||
* offers C++ API only
|
||||
* offers C/C++ API only
|
||||
* does not offer support for GNA and NPU inference
|
||||
* is dedicated to users of all major OSs: Windows, Linux, macOS.
|
||||
* may offer different hardware support under different operating systems.
|
||||
|
||||
.. tab-set::
|
||||
|
||||
@ -39,8 +38,8 @@
|
||||
Installing OpenVINO Runtime
|
||||
###########################
|
||||
|
||||
1. Make sure that you have installed VCPKG on your system. If not, follow the
|
||||
`VCPKG installation instructions <https://vcpkg.io/en/getting-started>`__.
|
||||
1. Make sure that you have installed vcpkg on your system. If not, follow the
|
||||
`vcpkg installation instructions <https://vcpkg.io/en/getting-started>`__.
|
||||
|
||||
|
||||
2. Install OpenVINO using the following terminal command:
|
||||
@ -49,29 +48,35 @@ Installing OpenVINO Runtime
|
||||
|
||||
vcpkg install openvino
|
||||
|
||||
VCPKG also enables you to install only selected components, by specifying them in the command.
|
||||
vcpkg also enables you to install only selected components, by specifying them in the command.
|
||||
See the list of `available features <https://vcpkg.link/ports/openvino>`__, for example:
|
||||
|
||||
.. code-block:: sh
|
||||
|
||||
vcpkg install openvino[cpu,ir]
|
||||
|
||||
Note that the VCPKG installation means building all packages and dependencies from source,
|
||||
Note that the vcpkg installation means building all packages and dependencies from source,
|
||||
which means the compiler stage will require additional time to complete the process.
|
||||
|
||||
After installation, you can use OpenVINO in your product by running:
|
||||
|
||||
.. code-block:: sh
|
||||
|
||||
find_package(OpenVINO)
|
||||
|
||||
.. code-block:: sh
|
||||
|
||||
cmake -B [build directory] -S . -DCMAKE_TOOLCHAIN_FILE=[path to vcpkg]/scripts/buildsystems/vcpkg.cmake
|
||||
|
||||
Congratulations! You've just Installed OpenVINO! For some use cases you may still
|
||||
need to install additional components. Check the
|
||||
:doc:`list of additional configurations <openvino_docs_install_guides_configurations_header>`
|
||||
to see if your case needs any of them.
|
||||
|
||||
|
||||
|
||||
|
||||
Uninstalling OpenVINO
|
||||
#####################
|
||||
|
||||
To uninstall OpenVINO via VCPKG, use the following command:
|
||||
To uninstall OpenVINO via vcpkg, use the following command:
|
||||
|
||||
.. code-block:: sh
|
||||
|
||||
|
||||
@ -15,7 +15,7 @@
|
||||
Use Archive <openvino_docs_install_guides_installing_openvino_from_archive_windows>
|
||||
Use PyPI <openvino_docs_install_guides_installing_openvino_pip>
|
||||
Use Conda Forge <openvino_docs_install_guides_installing_openvino_conda>
|
||||
Use VCPKG <openvino_docs_install_guides_installing_openvino_vcpkg>
|
||||
Use vcpkg <openvino_docs_install_guides_installing_openvino_vcpkg>
|
||||
Use Docker <openvino_docs_install_guides_installing_openvino_docker>
|
||||
|
||||
|
||||
|
||||
@ -10,7 +10,7 @@
|
||||
|
||||
Note that the YUM distribution:
|
||||
|
||||
* offers both C++ and Python APIs
|
||||
* offers C/C++ APIs only
|
||||
* does not offer support for GNA and NPU inference
|
||||
* additionally includes code samples
|
||||
* is dedicated to Linux users.
|
||||
@ -129,7 +129,9 @@ Run the following command:
|
||||
|
||||
yum list installed 'openvino*'
|
||||
|
||||
.. note::
|
||||
|
||||
You can additionally install Python API using one of the alternative methods (:doc:`conda <openvino_docs_install_guides_installing_openvino_conda>` or :doc:`pip <openvino_docs_install_guides_installing_openvino_pip>`).
|
||||
|
||||
Congratulations! You've just Installed OpenVINO! For some use cases you may still
|
||||
need to install additional components. Check the
|
||||
|
||||
@ -7,7 +7,6 @@
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:hidden:
|
||||
:caption: Pre-Trained Models
|
||||
|
||||
omz_models_group_intel
|
||||
omz_models_group_public
|
||||
@ -15,14 +14,15 @@
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:hidden:
|
||||
:caption: Demo Applications
|
||||
|
||||
omz_tools_downloader
|
||||
omz_tools_accuracy_checker
|
||||
omz_data_datasets
|
||||
omz_demos
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:hidden:
|
||||
:caption: Model API
|
||||
|
||||
omz_model_api_ovms_adapter
|
||||
|
||||
|
||||
@ -8,7 +8,6 @@
|
||||
|
||||
basic_quantization_flow
|
||||
quantization_w_accuracy_control
|
||||
pot_introduction
|
||||
|
||||
|
||||
Post-training model optimization is the process of applying special methods that transform the model into a more hardware-friendly representation without retraining or fine-tuning. The most popular and widely-spread method here is 8-bit post-training quantization because it is:
|
||||
|
||||
@ -1,15 +0,0 @@
|
||||
# Tuning Utilities {#openvino_docs_tuning_utilities}
|
||||
|
||||
@sphinxdirective
|
||||
|
||||
.. meta::
|
||||
:description: Get to know Accuracy Checker - a deep learning accuracy validation framework and other tuning utilities found in OpenVINO™ toolkit.
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 1
|
||||
:caption: Tuning Utilities
|
||||
|
||||
omz_tools_accuracy_checker
|
||||
omz_data_datasets
|
||||
|
||||
@endsphinxdirective
|
||||
Loading…
Reference in New Issue
Block a user