From fd6f18665e5dc3e87914879ba697126d9cb5c6f2 Mon Sep 17 00:00:00 2001 From: Sebastian Golebiewski Date: Thu, 25 Jan 2024 08:48:39 +0100 Subject: [PATCH] [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 --- .../releasenotes_for_openvino.rst | 9 ++--- docs/home.rst | 34 +++++++++---------- 2 files changed, 20 insertions(+), 23 deletions(-) diff --git a/docs/articles_en/about_openvino/releasenotes_for_openvino.rst b/docs/articles_en/about_openvino/releasenotes_for_openvino.rst index 28057553d46..c03785f604a 100644 --- a/docs/articles_en/about_openvino/releasenotes_for_openvino.rst +++ b/docs/articles_en/about_openvino/releasenotes_for_openvino.rst @@ -69,8 +69,7 @@ Support Change and Deprecation Notices * Starting in release 2023.3 OpenVINO will no longer support Python 3.7 due to the Python community discontinuing support. Update to a newer version (currently 3.8-3.11) to avoid interruptions. -* All ONNX Frontend legacy API (known as ONNX_IMPORTER_API) will no longer be available in - 2024.0 release. +* All ONNX Frontend legacy API (known as ONNX_IMPORTER_API) will no longer be available in 2024.0 release. * ``PerfomanceMode.UNDEFINED`` property as part of the OpenVINO Python API will be discontinued in the 2024.0 release. @@ -246,11 +245,9 @@ OpenVINO™ Runtime * Create ov.Tensor from empty numpy arrays. * Constants from empty numpy arrays. * Autogenerated get/set methods for Node attributes. - * Inference functions (InferRequest.infer/start_async, CompiledModel.__call__ etc.) - support OVDict as the input. + * Inference functions (``InferRequest.infer/start_async``, ``CompiledModel.__call__`` etc.) support OVDict as the input. * PILLOW interpolation modes bindings. - (`PR #21188 `__ - external contribution: @meetpatel0963) + (`PR #21188 `__ external contribution: @meetpatel0963) * Torchvision to :doc:`OpenVINO preprocessing ` converter documentation has been added to OpenVINO docs. diff --git a/docs/home.rst b/docs/home.rst index 4f9f7034cb4..8c6f814c609 100644 --- a/docs/home.rst +++ b/docs/home.rst @@ -23,9 +23,9 @@ OpenVINO 2023.2
  • An open-source toolkit for optimizing and deploying deep learning models.
    Boost your AI deep-learning inference performance!
    • - +
  • Use PyTorch models directly, without converting them first.
    - Learn more... + Learn more...
  • OpenVINO via PyTorch 2.0 torch.compile()
    Use OpenVINO directly in PyTorch-native applications!
    Learn more... @@ -36,7 +36,7 @@ OpenVINO 2023.2
- + .. button-ref:: get_started :ref-type: doc :class: ov-homepage-banner-btn @@ -56,35 +56,35 @@ OpenVINO 2023.2 .. grid-item-card:: Performance Benchmarks :link: openvino_docs_performance_benchmarks - :link-alt: performance benchmarks + :link-alt: performance benchmarks :link-type: doc See latest benchmark numbers for OpenVINO and OpenVINO Model Server .. grid-item-card:: Work with Multiple Model Formats :link: openvino_docs_model_processing_introduction - :link-alt: Supported Model Formats + :link-alt: Supported Model Formats :link-type: doc OpenVINO supports different model formats: PyTorch, TensorFlow, TensorFlow Lite, ONNX, and PaddlePaddle. .. grid-item-card:: Deploy at Scale with OpenVINO Model Server :link: ovms_what_is_openvino_model_server - :link-alt: model server + :link-alt: model server :link-type: doc Cloud-ready deployments for microservice applications .. grid-item-card:: Optimize Models :link: openvino_docs_model_optimization_guide - :link-alt: model optimization + :link-alt: model optimization :link-type: doc Boost performance using quantization and compression with NNCF - .. grid-item-card:: Use OpenVINO with PyTorch Apps with torch.compile() + .. grid-item-card:: Use OpenVINO with PyTorch Apps with torch.compile() :link: pytorch_2_0_torch_compile - :link-alt: torch.compile + :link-alt: torch.compile :link-type: doc Optimize generation of the graph model with PyTorch 2.0 torch.compile() backend @@ -94,7 +94,7 @@ OpenVINO 2023.2 :link-alt: gen ai :link-type: doc - Enhance the efficiency of Generative AI + Enhance the efficiency of Generative AI Feature Overview @@ -105,25 +105,25 @@ Feature Overview .. grid-item-card:: Local Inference & Model Serving - You can either link directly with OpenVINO Runtime to run inference locally or use OpenVINO Model Server + You can either link directly with OpenVINO Runtime to run inference locally or use OpenVINO Model Server to serve model inference from a separate server or within Kubernetes environment .. grid-item-card:: Improved Application Portability - Write an application once, deploy it anywhere, achieving maximum performance from hardware. Automatic device - discovery allows for superior deployment flexibility. OpenVINO Runtime supports Linux, Windows and MacOS and + Write an application once, deploy it anywhere, achieving maximum performance from hardware. Automatic device + discovery allows for superior deployment flexibility. OpenVINO Runtime supports Linux, Windows and MacOS and provides Python, C++ and C API. Use your preferred language and OS. .. grid-item-card:: Minimal External Dependencies - Designed with minimal external dependencies reduces the application footprint, simplifying installation and - dependency management. Popular package managers enable application dependencies to be easily installed and + Designed with minimal external dependencies reduces the application footprint, simplifying installation and + dependency management. Popular package managers enable application dependencies to be easily installed and upgraded. Custom compilation for your specific model(s) further reduces final binary size. .. grid-item-card:: Enhanced App Start-Up Time - In applications where fast start-up is required, OpenVINO significantly reduces first-inference latency by using the - CPU for initial inference and then switching to another device once the model has been compiled and loaded to memory. + In applications where fast start-up is required, OpenVINO significantly reduces first-inference latency by using the + CPU for initial inference and then switching to another device once the model has been compiled and loaded to memory. Compiled models are cached improving start-up time even more.