Ilya Lavrenov
8fb699a42c
* 1. Removed explicit SHARED from libraries 2. Fixed double definition for ie_layer_validators * Fixed SEG in unit-test: order of initialization for global vars * Added an ability to find plugins.xml from static IE * Fixes in unit-test * Migrated to new macro for import / export * Minimized number of custom dllexport * Don't use IR v7 for static libraries * Revert for merge * Don't enable tests with dlopen for static libraries * Code style * Added condition for export * Revert format_reader * Removed forward decalaration with external linkage * Fixed IE linkage on Windows * Reverted back 2 flags * Minimal RRTI for cpuFuncTests * Minimal RRTI for cpuFuncTests * Still need IR v7 reader * Fixed build * Fixed compilation * clang-format fix * Removed BUILD_AS_IS and used USE_STATIC_IE * Enable IR v7 reader as static library * Fixed compilation for GPU plugin * Trying to build plugins as static library * Plugins are able provide their own name for CreatePluginEngine function * Fixed CPU * Fixed comments * Fixed ENABLE_IR_V7_READER usage * Fixed VPU * clang-format * Fixes * Fix * Load multiple plugins at once * Fixed interpreter undefined symbols * Trying to dynamically register static plugins * Reverted some ngraph changes * Fixed cpuUnitTests compilation * Fixed compilation * Fixed myriad * Fixed custom_opset tests * Reverted linker flags * Support both static and dynamic plugins * Fixed compilation of myriadFuncTests * Removed duplication * Fixes after self-review * Fixed linkage for preprocessing * Fixes for Windows * Fixes * Fixed cmake options * Fix * Fix * Fix 2
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.