[DOCS] Fixing reference to PyTorch conversion for 23.3 (#22393)
* Fixing reference to PyTorch conversion * Fixing formatting in Release Notes * Removing Openvino Supported Models article
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Sebastian Golebiewski
GitHub
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@ -8,7 +8,6 @@ Compatibility and Support
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:maxdepth: 1
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:hidden:
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openvino_supported_models
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openvino_docs_OV_UG_supported_plugins_Supported_Devices
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openvino_resources_supported_operations
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openvino_resources_supported_operations_frontend
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@ -16,9 +15,7 @@ Compatibility and Support
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:doc:`Supported Devices <openvino_docs_OV_UG_supported_plugins_Supported_Devices>` - compatibility information for supported hardware accelerators.
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:doc:`Supported Models <openvino_supported_models>` - a table of models officially supported by OpenVINO.
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:doc:`Supported Operations <openvino_resources_supported_operations>` - a listing of framework layers supported by OpenVINO.
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:doc:`Supported Operations <openvino_resources_supported_operations>` - a listing of framework layers supported by OpenVINO.
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:doc:`Supported Operations <openvino_resources_supported_operations_frontend>` - a listing of layers supported by OpenVINO inference devices.
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@ -1,36 +0,0 @@
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.. {#openvino_supported_models}
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Supported Models
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================
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.. meta::
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:description: Check the list of officially supported models in Intel®
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Distribution of OpenVINO™ toolkit.
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The OpenVINO team continues the effort to support as many models out-of-the-box as possible.
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Based on our research and user feedback, we prioritize the most common models and test them
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before every release. These models are considered officially supported.
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.. button-link:: _static/download/OV_2023_models_supported.pdf
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:color: primary
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:outline:
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:material-regular:`download;1.5em` Click for supported models [PDF]
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The list is based on release 2023.0, as of June 01, 2023
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| Note that the list provided here does not include all models supported by OpenVINO.
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| If your model is not included but is similar to those that are, it is still very likely to work.
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If your model fails to execute properly there are a few options available:
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* You can create a GitHub request for the operation(s) that are missing. These requests are reviewed regularly. You will be informed if and how the request will be accommodated. Additionally, your request may trigger a reply from someone in the community who can help.
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* As OpenVINO™ is open source you can enhance it with your own contribution to the GitHub repository. To learn more, see the articles on :doc:`OpenVINO Extensibility <openvino_docs_Extensibility_UG_Intro>`.
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@ -69,8 +69,7 @@ Support Change and Deprecation Notices
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* Starting in release 2023.3 OpenVINO will no longer support Python 3.7 due to the Python
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community discontinuing support. Update to a newer version (currently 3.8-3.11) to avoid
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interruptions.
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* All ONNX Frontend legacy API (known as ONNX_IMPORTER_API) will no longer be available in
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2024.0 release.
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* All ONNX Frontend legacy API (known as ONNX_IMPORTER_API) will no longer be available in 2024.0 release.
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* ``PerfomanceMode.UNDEFINED`` property as part of the OpenVINO Python API will be
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discontinued in the 2024.0 release.
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@ -246,11 +245,9 @@ OpenVINO™ Runtime
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* Create ov.Tensor from empty numpy arrays.
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* Constants from empty numpy arrays.
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* Autogenerated get/set methods for Node attributes.
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* Inference functions (InferRequest.infer/start_async, CompiledModel.__call__ etc.)
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support OVDict as the input.
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* Inference functions (``InferRequest.infer/start_async``, ``CompiledModel.__call__`` etc.) support OVDict as the input.
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* PILLOW interpolation modes bindings.
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(`PR #21188 <https://github.com/openvinotoolkit/openvino/pull/21188>`__
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external contribution: @meetpatel0963)
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(`PR #21188 <https://github.com/openvinotoolkit/openvino/pull/21188>`__ external contribution: @meetpatel0963)
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* Torchvision to :doc:`OpenVINO preprocessing <openvino_docs_OV_UG_string_tensors>`
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converter documentation has been added to OpenVINO docs.
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@ -5,8 +5,8 @@ GET STARTED
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.. meta::
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:description: Learn how to install Intel® Distribution of OpenVINO™ toolkit
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on Windows, macOS, and Linux operating systems, using various
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:description: Learn how to install Intel® Distribution of OpenVINO™ toolkit
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on Windows, macOS, and Linux operating systems, using various
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installation methods.
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.. toctree::
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@ -28,7 +28,7 @@ GET STARTED
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<a href="openvino_docs_install_guides_overview.html" >Install OpenVINO</a>
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<a href="#learn-openvino" >Learn OpenVINO</a>
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</p>
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<div style="clear:both;"> </div>
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<div style="clear:both;"> </div>
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.. _quick-start-example:
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@ -44,16 +44,16 @@ Try out OpenVINO's capabilities with this `quick start example <notebooks/201-vi
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2. Install OpenVINO
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###################
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See the :doc:`installation overview page <openvino_docs_install_guides_overview>` for options to install OpenVINO and set up a development environment on your device.
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.. _get-started-tutorials:
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3. Learn OpenVINO
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#################
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OpenVINO provides a wide array of examples and documentation showing how to work with models, run inference, and deploy applications. Step through the sections below to learn the basics of OpenVINO and explore its advanced optimization features. For further details, visit :doc:`OpenVINO documentation <documentation>`.
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OpenVINO users of all experience levels can try `Intel® DevCloud <https://www.intel.com/content/www/us/en/developer/tools/devcloud/edge/overview.html>`__ , a free web-based service for developing, testing, and running OpenVINO applications on an online cluster of the latest Intel® hardware.
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.. _openvino-basics:
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@ -62,7 +62,7 @@ OpenVINO Basics
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+++++++++++++++
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Learn the basics of working with models and inference in OpenVINO. Begin with “Hello World” Interactive Tutorials that show how to prepare models, run inference, and retrieve results using the OpenVINO API. Then, explore other examples from the Open Model Zoo and OpenVINO Code Samples that can be adapted for your own application.
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.. _interactive-tutorials:
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Interactive Tutorials - Jupyter Notebooks
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@ -81,7 +81,7 @@ OpenVINO Code Samples
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---------------------
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View :doc:`sample code <openvino_docs_OV_UG_Samples_Overview>` for various C++ and Python applications that can be used as a starting point for your own application. For C++ developers, step through the :doc:`Get Started with C++ Samples <openvino_docs_get_started_get_started_demos>` to learn how to build and run an image classification program that uses OpenVINO’s C++ API.
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.. _integrate-openvino:
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Integrate OpenVINO With Your Application
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@ -124,13 +124,12 @@ Pipeline and model configuration features in OpenVINO Runtime allow you to easil
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* :doc:`Performance Hints <openvino_docs_OV_UG_Performance_Hints>` automatically adjust runtime parameters to prioritize for low latency or high throughput
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* :doc:`Dynamic Shapes <openvino_docs_OV_UG_DynamicShapes>` reshapes models to accept arbitrarily-sized inputs, increasing flexibility for applications that encounter different data shapes
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* :doc:`Benchmark Tool <openvino_sample_benchmark_tool>` characterizes model performance in various hardware and pipeline configurations
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.. _additional-resources:
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Additional Resources
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====================
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* `OpenVINO Success Stories <https://www.intel.com/content/www/us/en/internet-of-things/ai-in-production/success-stories.html>`__ - See how Intel partners have successfully used OpenVINO in production applications to solve real-world problems.
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* :doc:`OpenVINO Supported Models <openvino_supported_models>` - Check which models OpenVINO supports on your hardware.
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* :doc:`Performance Benchmarks <openvino_docs_performance_benchmarks>` - View results from benchmarking models with OpenVINO on Intel hardware.
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@ -23,9 +23,9 @@ OpenVINO 2023.2
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<div class="splide__track">
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<ul class="splide__list">
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<li class="splide__slide">An open-source toolkit for optimizing and deploying deep learning models.<br>Boost your AI deep-learning inference performance!</li>
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<li class="splide__slide"Better OpenVINO integration with PyTorch!<br>Use PyTorch models directly, without converting them first.<br>
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<a href="https://docs.openvino.ai/2023.3/openvino_docs_MO_DG_prepare_model_convert_model_Convert_Model_From_PyTorch.html">Learn more...</a>
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<a href="https://docs.openvino.ai/2023.3/openvino_docs_OV_Converter_UG_prepare_model_convert_model_Convert_Model_From_PyTorch.html">Learn more...</a>
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</li>
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<li class="splide__slide">OpenVINO via PyTorch 2.0 torch.compile()<br>Use OpenVINO directly in PyTorch-native applications!<br>
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<a href="https://docs.openvino.ai/2023.3/pytorch_2_0_torch_compile.html">Learn more...</a>
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@ -36,7 +36,7 @@ OpenVINO 2023.2
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</div>
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</section>
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</div>
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.. button-ref:: get_started
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:ref-type: doc
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:class: ov-homepage-banner-btn
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@ -56,35 +56,35 @@ OpenVINO 2023.2
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.. grid-item-card:: Performance Benchmarks
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:link: openvino_docs_performance_benchmarks
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:link-alt: performance benchmarks
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:link-alt: performance benchmarks
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:link-type: doc
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See latest benchmark numbers for OpenVINO and OpenVINO Model Server
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.. grid-item-card:: Work with Multiple Model Formats
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:link: openvino_docs_model_processing_introduction
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:link-alt: Supported Model Formats
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:link-alt: Supported Model Formats
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:link-type: doc
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OpenVINO supports different model formats: PyTorch, TensorFlow, TensorFlow Lite, ONNX, and PaddlePaddle.
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.. grid-item-card:: Deploy at Scale with OpenVINO Model Server
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:link: ovms_what_is_openvino_model_server
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:link-alt: model server
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:link-alt: model server
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:link-type: doc
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Cloud-ready deployments for microservice applications
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.. grid-item-card:: Optimize Models
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:link: openvino_docs_model_optimization_guide
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:link-alt: model optimization
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:link-alt: model optimization
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:link-type: doc
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Boost performance using quantization and compression with NNCF
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.. grid-item-card:: Use OpenVINO with PyTorch Apps with torch.compile()
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.. grid-item-card:: Use OpenVINO with PyTorch Apps with torch.compile()
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:link: pytorch_2_0_torch_compile
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:link-alt: torch.compile
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:link-alt: torch.compile
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:link-type: doc
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Optimize generation of the graph model with PyTorch 2.0 torch.compile() backend
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@ -94,7 +94,7 @@ OpenVINO 2023.2
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:link-alt: gen ai
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:link-type: doc
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Enhance the efficiency of Generative AI
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Enhance the efficiency of Generative AI
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Feature Overview
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@ -105,25 +105,25 @@ Feature Overview
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.. grid-item-card:: Local Inference & Model Serving
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You can either link directly with OpenVINO Runtime to run inference locally or use OpenVINO Model Server
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You can either link directly with OpenVINO Runtime to run inference locally or use OpenVINO Model Server
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to serve model inference from a separate server or within Kubernetes environment
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.. grid-item-card:: Improved Application Portability
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Write an application once, deploy it anywhere, achieving maximum performance from hardware. Automatic device
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discovery allows for superior deployment flexibility. OpenVINO Runtime supports Linux, Windows and MacOS and
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Write an application once, deploy it anywhere, achieving maximum performance from hardware. Automatic device
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discovery allows for superior deployment flexibility. OpenVINO Runtime supports Linux, Windows and MacOS and
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provides Python, C++ and C API. Use your preferred language and OS.
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.. grid-item-card:: Minimal External Dependencies
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Designed with minimal external dependencies reduces the application footprint, simplifying installation and
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dependency management. Popular package managers enable application dependencies to be easily installed and
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Designed with minimal external dependencies reduces the application footprint, simplifying installation and
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dependency management. Popular package managers enable application dependencies to be easily installed and
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upgraded. Custom compilation for your specific model(s) further reduces final binary size.
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.. grid-item-card:: Enhanced App Start-Up Time
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In applications where fast start-up is required, OpenVINO significantly reduces first-inference latency by using the
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CPU for initial inference and then switching to another device once the model has been compiled and loaded to memory.
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In applications where fast start-up is required, OpenVINO significantly reduces first-inference latency by using the
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CPU for initial inference and then switching to another device once the model has been compiled and loaded to memory.
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Compiled models are cached improving start-up time even more.
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