Sergey Lyalin 1c5e76c4db Dynamic Shapes Documentation (#10656)
* 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>
2022-03-03 09:00:28 +03:00
2022-03-02 18:00:32 +03:00
2022-02-03 16:51:26 +03:00
2021-05-31 15:24:56 +03:00
2018-10-16 13:45:03 +03:00
2020-11-17 16:44:44 +03:00

OpenVINO™ Toolkit

Stable release Apache License Version 2.0 GitHub branch checks state Azure DevOps builds (branch) PyPI Downloads

This toolkit allows developers to deploy pre-trained deep learning models through a high-level OpenVINO™ Runtime C++ and Python APIs integrated with application logic.

This open source version includes several components: namely Model Optimizer, OpenVINO™ Runtime, Post-Training Optimization Tool, as well as CPU, GPU, MYRIAD, multi device and heterogeneous plugins to accelerate deep learning inferencing on Intel® CPUs and Intel® Processor Graphics. It supports pre-trained models from the Open Model Zoo, along with 100+ open source and public models in popular formats such as TensorFlow, ONNX, PaddlePaddle, MXNet, Caffe, Kaldi.

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License

OpenVINO™ Toolkit is licensed under Apache License Version 2.0. By contributing to the project, you agree to the license and copyright terms therein and release your contribution under these terms.

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Languages
C++ 80.5%
Python 15.5%
C 2.8%
CMake 0.9%
Cython 0.1%