OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
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Evgeny Lazarev 5bafab9e72
Allow partially defined dimensions to read/serialize from/to IR (#6819)
* Allow to read and serialize IRs with -1 in dimensions (partially defined shape)

* Added unit test for reading/writing IR with partially defined shapes

* Added missing xml file with test IR

* Remove copy-paste issue

* Output message fix

* Restored statification of the output shapes during IR serialization

* Try to make dynamic shapes static with upper bound

* Code style changes
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.ci Paddlepaddle unit tests CI fixes (#6820) 2021-07-28 17:34:30 +03:00
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model-optimizer Implement transformation for TensorFlow 2 Map Function (aka tf.map_fn) (#6836) 2021-07-30 12:10:59 +03:00
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OpenVINO™ Toolkit

Stable release Apache License Version 2.0 GitHub branch checks state Azure DevOps builds (branch)

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:

Support

Please report questions, issues and suggestions using:


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