[DOCS] Fix reference to PyTorch conversion (#22391)
* Fix reference to PyTorch conversion * Fixing formatting in Release Notes * Update docs/articles_en/about_openvino/releasenotes_for_openvino.rst
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Sebastian Golebiewski
GitHub
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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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@ -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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