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130 lines
4.8 KiB
ReStructuredText
130 lines
4.8 KiB
ReStructuredText
============================
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OpenVINO 2023.0
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============================
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.. meta::
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:google-site-verification: _YqumYQ98cmXUTwtzM_0WIIadtDc6r_TMYGbmGgNvrk
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.. raw:: html
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<link rel="stylesheet" type="text/css" href="_static/css/homepage_style.css">
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.. container::
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:name: ov-homepage-banner
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OpenVINO 2023.0
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.. raw:: html
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<div class="line-block">
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<section class="splide" aria-label="Splide Banner Carousel">
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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">Even more integrations in 2023.0!<br>Load TensorFlow, TensorFlow Lite, and PyTorch models directly, without manual conversion.<br><a href="https://docs.openvino.ai/2023.0/Supported_Model_Formats.html">See the supported model formats...</a></li>
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<li class="splide__slide">CPU inference has become even better. ARM processors are supported and thread scheduling is available on 12th gen Intel® Core and up.<br><a href="https://docs.openvino.ai/2023.0/openvino_docs_OV_UG_OV_Runtime_User_Guide.html">See how to run OpenVINO on various devices...</a></li>
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<li class="splide__slide">Post-training optimization and quantization-aware training now in one tool!<br><a href="https://docs.openvino.ai/2023.0/openvino_docs_model_optimization_guide.html">See the new NNCF capabilities...</a></li>
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</ul>
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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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:color: primary
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:outline:
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Get started
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.. rst-class:: openvino-diagram
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.. image:: _static/images/ov_homepage_diagram.png
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:align: center
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.. grid:: 2 2 3 3
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:class-container: ov-homepage-higlight-grid
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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-type: doc
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See latest benchmark numbers for OpenVINO and OpenVINO Model Server
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.. grid-item-card:: Flexible Workflow
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:link: Supported_Model_Formats
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:link-alt: Supported Model Formats
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:link-type: doc
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Load models directly (for TensorFlow, ONNX, PaddlePaddle) or convert to the OpenVINO format.
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.. grid-item-card:: Run Inference
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:link: openvino_docs_OV_UG_Integrate_OV_with_your_application
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:link-alt: integrating OpenVINO with your app
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:link-type: doc
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Get results in just a few lines of code
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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-type: doc
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Cloud-ready deployments for microservice applications
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.. grid-item-card:: Model Optimization
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:link: openvino_docs_model_optimization_guide
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:link-alt: model optimization
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:link-type: doc
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Reach for performance with post-training and training-time compression with NNCF
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Feature Overview
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##############################
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.. grid:: 1 2 2 2
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:class-container: ov-homepage-feature-grid
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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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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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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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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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Compiled models are cached improving start-up time even more.
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.. toctree::
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:maxdepth: 2
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:hidden:
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GET STARTED <get_started>
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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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