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* Added draft of Dynamic Shapes Doc * Better wording Co-authored-by: Ilya Churaev <ilyachur@gmail.com> * Apply suggestions from code review Better wording, grammar, technical fixes. No significant content rework. Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com> Co-authored-by: Evgenya Stepyreva <evgenya.stepyreva@intel.com> * Removed indentation in dynamic shapes snippets * Split dynamic shapes doc to two separate files, added more examples, fixed code review comments, connected to TOC * Fix links * Added aux doc to toc to avoid crash in docs build in CI * Added dynamicbatching in temp section * Apply suggestions from code review * Removed old DynamicBatching document * Applied @myshevts changes * Update docs/OV_Runtime_UG/ov_without_dynamic_shapes.md * Update ov_dynamic_shapes.md * Fix links to dynamic shapes doc Co-authored-by: Ilya Churaev <ilyachur@gmail.com> Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com> Co-authored-by: Evgenya Stepyreva <evgenya.stepyreva@intel.com>
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OpenVINO™ Runtime User Guide
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.. _deep learning inference engine:
.. toctree:: :maxdepth: 1 :hidden:
openvino_docs_Integrate_OV_with_your_application
openvino_docs_IE_DG_ShapeInference openvino_docs_OV_UG_Working_with_devices openvino_docs_OV_Runtime_UG_Preprocessing_Overview openvino_docs_OV_UG_DynamicShapes openvino_docs_IE_DG_supported_plugins_AUTO openvino_docs_OV_UG_Running_on_multiple_devices openvino_docs_OV_UG_Hetero_execution openvino_docs_OV_UG_Automatic_Batching openvino_docs_IE_DG_network_state_intro openvino_2_0_transition_guide openvino_docs_OV_Should_be_in_performance
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Introduction
OpenVINO Runtime is a set of C++ libraries with C and Python bindings providing a common API to deliver inference solutions on the platform of your choice. Use the OpenVINO Runtime API to read an Intermediate Representation (IR), ONNX, or PaddlePaddle model and execute it on preferred devices.
OpenVINO Runtime uses a plugin architecture. Its plugins are software components that contain complete implementation for inference on a particular Intel® hardware device: CPU, GPU, VPU, etc. Each plugin implements the unified API and provides additional hardware-specific APIs, for configuring devices, or API interoperability between OpenVINO Runtime and underlying plugin backend.
The scheme below illustrates the typical workflow for deploying a trained deep learning model:
Video
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<iframe allowfullscreen mozallowfullscreen msallowfullscreen oallowfullscreen webkitallowfullscreen height="315" width="100%" src="https://www.youtube.com/embed/e6R13V8nbak"> </iframe>
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- Inference Engine Concept. Duration: 3:43
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