81685c8d21
* moving the HETERO logic to the Auto-Batch (WIP), reverting to the ALLOW_AUTO_BATCHING and using that in the GPU remote tests * shortned the vars names in the ie_core and prevented recursive auto-batching calls by checking for exclusive requests and disabling further auto-batching in the plugin, when HETERO is involved * checking for the batch-dim presence (this is still WA until the https://github.com/openvinotoolkit/openvino/pull/9559 is merged) - pls see CVS-75317 +clang for the ie_core.cpp * moving the HETERO logic back to the ie_core.cpp, storing the _so internally for no-batch code-path
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
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