Ilya LavrenovandMikhail Nosov 86b175534a Docs: complete migration guide (#10652)
* Updated glossary

* Removed references to OpenVX

* Moved migration_ov_2_0 to OpenVINO User guide

* Replaced IE with OV runtime

* Complete migration guide

* Migration 2.0

* Self-review

* Added property migration guide

* Fixed table

* Added preprocessing migration

* Update docs/OV_Runtime_UG/migration_ov_2_0/preprocessing.md

Co-authored-by: Mikhail Nosov <mikhail.nosov@intel.com>

* Update docs/OV_Runtime_UG/migration_ov_2_0/preprocessing.md

Co-authored-by: Mikhail Nosov <mikhail.nosov@intel.com>

* Update docs/snippets/ov_preprocessing_migration.cpp

Co-authored-by: Mikhail Nosov <mikhail.nosov@intel.com>

* reivew fixes

* Preprocessing intro updated

* Updated config migration guide

* Updates

* Fixes

Co-authored-by: Mikhail Nosov <mikhail.nosov@intel.com>
2022-03-02 12:16:58 +03:00
2022-02-03 16:51:26 +03:00
2022-03-01 16:56:15 +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.

Repository components

License

Deep Learning Deployment 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%