* Remove broadcusting * Refactoring some code * Add unit tests * Update description * Refactoring transformation * Add is_broadcastable_shapes checks * Update is_eliminate_broadcast func * Add unit tests * Update unit tests * Add unit tests * Add unit tests * Remove unused include * Add dynemic tests * Update unit tests * Fix code style * Fix unit tests code style * Fix code style * Add one more case for elumenate broadcast * Fix according to review * Refactoring transformation code |
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inference-engine | ||
licensing | ||
model-optimizer | ||
ngraph | ||
openvino | ||
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tests | ||
tools | ||
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CODEOWNERS | ||
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SECURITY.md |
OpenVINO™ Toolkit - Deep Learning Deployment Toolkit repository
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.
Resources:
- Docs: https://docs.openvinotoolkit.org/
- Wiki: https://github.com/openvinotoolkit/openvino/wiki
- Issue tracking: https://github.com/openvinotoolkit/openvino/issues
- Additional OpenVINO modules: https://github.com/openvinotoolkit/openvino_contrib
- HomePage
- OpenVINO™ Release Notes
Support
Please report questions, issues and suggestions using:
- The
openvino
tag on StackOverflow* - GitHub* Issues
- Forum
* Other names and brands may be claimed as the property of others.