Ivan Tikhonov 1c3208ffe0 Low Latency transformation (#2869)
* initial draft of adding sinks to ngraph::Function

* style fixes

* code style fixes

* code style fixes

* code style fix

* review fix+build fix

* code style fix

* fix build

* API changed according to latest discussion

* review fixes

* review fixes + tests

* initial draft of adding sinks to ngraph::Function

* style fixes

* code style fixes

* code style fixes

* code style fix

* review fix+build fix

* code style fix

* fix build

* API changed according to latest discussion

* review fixes

* review fixes + tests

* added 1 more ctor

* style fixes

* used new api in ir parser

* fixed build

* update low latency transformation, fix unroll transformation, add unit tests, modify subgraph tests

* fix low latency transformation

* Update low latency transformation, unit and sub-graph tests

* update LowLatency transformation and tests

* ngraph codestyle

* fix build, update description

* resolve review remarks

Co-authored-by: Svetlana Dolinina <svetlana.a.dolinina@intel.com>
2020-11-06 14:11:11 +03:00
2020-11-06 14:11:11 +03:00
2020-10-14 18:35:21 +03:00
2020-07-20 17:36:08 +03:00
2020-05-19 19:04:27 +03:00
2020-10-27 15:12:33 +03:00
2018-10-16 13:45:03 +03:00
2020-09-21 21:35:24 +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 two components: namely Model Optimizer and Inference Engine, as well as CPU, GPU 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.

Documentation

How to Contribute

See CONTRIBUTING for contribution to the code. See CONTRIBUTING_DOCS for contribution to the documentation. Thank you!

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