b291ca8cfa
* Use Serialization as a default engine in MO * Added cmd option to use old serialization * Added mapping file generation * Test mapping file generation * Fix setBatchsize parameters order; fix mapping file generation * Added FrameworkNode; added method to read models with custom ops but without extensions * Added python API for read_network_without_extensions function; updated mo not to use IECore * Added read_model_without_extensions to IReader and IParser * Fix V7 IR reader * Fix pword value * Fix dllexport macro usage * Add metainfo to IR * Fix nGraph code style * Fix license header * Restore prepare_emit_ir behaviour * Fix compare_function to resolve situation when Result input port has multiple names * Update Compare Functions * Fix FrameworkNode validation * Self-review * CodeStyle check * --use_fallback -> --use_legacy_ir_generation * Sort imports in main.py * --path_to_model -> --input_model * Use logging instead of print * Code simplifucation&cleanup * Fix offline_Transformations key * Fix GeneraeMappingFile comments * Use Extension approach to work with custom ops * Fix versions check * Code clean-up * Moved FrameworkNode to inference_engine_transformations library * Fix FrameworkNode includes * Code clean-up |
||
|---|---|---|
| .ci | ||
| .github | ||
| cmake | ||
| docs | ||
| inference-engine | ||
| licensing | ||
| model-optimizer | ||
| ngraph | ||
| openvino | ||
| scripts | ||
| tests | ||
| thirdparty | ||
| tools | ||
| .gitattributes | ||
| .gitignore | ||
| .gitmodules | ||
| CMakeLists.txt | ||
| CODEOWNERS | ||
| install_build_dependencies.sh | ||
| Jenkinsfile | ||
| LICENSE | ||
| README.md | ||
| SECURITY.md | ||
OpenVINO™ Toolkit
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
- Storage: https://storage.openvinotoolkit.org/
- Additional OpenVINO™ modules: https://github.com/openvinotoolkit/openvino_contrib
- Intel® Distribution of OpenVINO™ toolkit Product Page
- Intel® Distribution of OpenVINO™ toolkit Release Notes
Support
Please report questions, issues and suggestions using:
- The
openvinotag on StackOverflow* - GitHub* Issues
- Forum
* Other names and brands may be claimed as the property of others.