OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
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Vladimir Gavrilov 3aed19a46a
XLNET models bugs (#1199)
* Started to write equality comparator for StridedSlice layers.

* Now considered only unique StridedSlice consumers of non-constant data nodes.

* Fixed test for the transformation ConvertGroupedStridedSlice.

* Deleted commented code.

* Small fixes.

* Moved functions unique_by and group_by_with_binary_predicate to mo/utils/utils.py.

* Deleted function collect_sequences.

* Added asserts into the constructor of StridedSlice.

* Written test for the case when 1) there are 4 StridedSlice operations; 2) 2 of StridedSlice have the same data; 3) 2 others StridedSlice have the same data; 4) all StridedSlice operations outputs are consumed by different operations.

* Added some comments.
2020-11-19 10:07:30 +03:00
.ci [OpenVino ONNX CI watchdog] Small improvements (#3096) 2020-11-13 12:17:11 +03:00
.github Update dependabot.yml 2020-11-18 13:07:34 +03:00
cmake Ngraph static lib (#3193) 2020-11-18 18:09:41 +03:00
docs nGraph: Fix TopK output sorting by index (#3092) 2020-11-18 14:14:33 +01:00
inference-engine Reduced usage of batch in python samples (#3178) 2020-11-18 20:43:59 +03:00
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model-optimizer XLNET models bugs (#1199) 2020-11-19 10:07:30 +03:00
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openvino ITT performance counters for first inference (#1741) 2020-11-12 14:00:14 +03:00
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tests Extend information to submit to a DB in time_tests (#3018) 2020-11-17 12:07:40 +03:00
tools Supported threading command line options for other devices (#2725) 2020-10-21 06:40:18 +03:00
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OpenVINO™ Toolkit - Deep Learning Deployment Toolkit repository

Stable release Apache License Version 2.0 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.

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