Sergey Lyubimtsev e12b24a0bc CMake installation rules for 3rd party components (#2944)
* [MO] Add CMake install for Model Optimizer

* [MO] Update test for version.py

* [MO] Add CMake install for Model Optimizer

* [MO] Update test for version.py

* [MO] Add CMake install for Model Optimizer

* [MO] Update test for version.py

* Add CMake install rules for OpenVINO 3rd party dependencies

Remove unwanted targets from final distribution (test, static libs, etc)

* remove duplicated targets from install

align GNA Windows install path with Linux

* Add COMPONENT install name for OMP

remove empty lines

* Add OMP to CPack components

* remove gtest from deliverables

add explicitly clDNN_unit_tests target to build

* set clDNN_unit_tests properties only by condition

* remove install commands for clDNN

Co-authored-by: Alexander Zhogov <alexander.zhogov@intel.com>
2020-11-30 12:29:30 +03:00
2020-11-19 13:59:20 +03:00
2020-07-20 17:36:08 +03:00
2018-10-16 13:45:03 +03:00
2020-11-17 16:44:44 +03:00

OpenVINO™ Toolkit - Deep Learning Deployment Toolkit repository

Stable release Apache License Version 2.0 Azure DevOps builds (branch)

This toolkit allows developers to deploy pre-trained deep learning models through a high-level C++ Inference Engine API integrated with application logic.

This open source version includes several components: namely Model Optimizer, ngraph and Inference Engine, 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 Caffe*, TensorFlow*, MXNet* and ONNX*.

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%