Go to file
Alexander Zhogov 28f258e18d
Enable public CI (#789)
* Enable public CI

* Exclude failed nGraph UT by *GPU*:*CPU*

* Disable absent tests

* Exclude failed nGraph UT constant.shared_data
2020-06-05 15:55:45 +03:00
.github/workflows Enable public CI (#789) 2020-06-05 15:55:45 +03:00
cmake Publishing 2020.3 content 2020-06-02 21:59:45 +03:00
docs Publishing 2020.3 content 2020-06-02 21:59:45 +03:00
inference-engine fix permissions for shell scripts 2020-06-02 22:32:00 +03:00
model-optimizer fix permissions for shell scripts 2020-06-02 22:32:00 +03:00
ngraph@1797d7fb71 Publishing 2020.3 content 2020-06-02 21:59:45 +03:00
scripts fix permissions for shell scripts 2020-06-02 22:32:00 +03:00
tools Publishing 2020.2 content 2020-04-13 21:17:23 +03:00
.clang-format Publishing 2020.1 content 2020-02-11 22:48:49 +03:00
.coveragerc Publishing R3 2018-10-16 13:45:03 +03:00
.editorconfig Publishing R3 2018-10-16 13:45:03 +03:00
.gitattributes Publishing R3 2018-10-16 13:45:03 +03:00
.gitignore Publishing R4 (#41) 2018-11-23 16:19:43 +03:00
.gitmodules Publishing 2020.1 content 2020-02-11 22:48:49 +03:00
.pylintdict Publishing R3 2018-10-16 13:45:03 +03:00
.pylintrc Publishing R3 2018-10-16 13:45:03 +03:00
CMakeLists.txt Publishing 2020.3 content 2020-06-02 21:59:45 +03:00
LICENSE Publishing R3 2018-10-16 13:45:03 +03:00
README.md change repo name to openvino in readme files 2020-06-03 00:08:25 +03:00
azure-pipelines.yml Enable public CI (#789) 2020-06-05 15:55:45 +03:00
build-instruction.md change repo name to openvino in readme files 2020-06-03 00:08:25 +03:00
get-started-linux.md change repo name to openvino in readme files 2020-06-03 00:08:25 +03:00
install_dependencies.sh w (#394) 2020-05-26 00:28:09 +03:00

README.md

OpenVINO™ Toolkit - Deep Learning Deployment Toolkit repository

Stable release Apache License Version 2.0

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

We welcome community contributions to the Deep Learning Deployment Toolkit repository. If you have an idea how to improve the product, please share it with us doing the following steps:

We will review your contribution and, if any additional fixes or modifications are necessary, may give some feedback to guide you. Your pull request will be merged into GitHub* repositories if accepted.

Support

Please report questions, issues and suggestions using:


* Other names and brands may be claimed as the property of others.