OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
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Yuan Hu 4f6dcc1d32
[AUTOPLUGIN] add fps log (#10662)
* add fps log

add format '%lf' for log
add INFO_RUN and DEBUG_RUN, code only run when greater than special log level
add fps log for device
print device config info with DEBUG_RUN
add mock test for DEBUG_RUN and INFO_RUN

Signed-off-by: Hu, Yuan2 <yuan2.hu@intel.com>

* use n / end -start instead of (n-1) / ((nst start) -(1st start))

Signed-off-by: Hu, Yuan2 <yuan2.hu@intel.com>
2022-03-16 09:55:41 +08:00
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cmake ncc build fixes (#10367) 2022-03-09 13:42:06 +03:00
docs Cherry-picked Changes to the OpenVINO 2.0 Transition Guide (#10936) (#10978) 2022-03-15 21:09:33 +00:00
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src [AUTOPLUGIN] add fps log (#10662) 2022-03-16 09:55:41 +08:00
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OpenVINO™ Toolkit

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

This toolkit allows developers to deploy pre-trained deep learning models through a high-level OpenVINO™ Runtime C++ and Python APIs integrated with application logic.

This open source version includes several components: namely Model Optimizer, OpenVINO™ Runtime, Post-Training Optimization Tool, 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 TensorFlow, ONNX, PaddlePaddle, MXNet, Caffe, Kaldi.

Repository components

License

OpenVINO™ 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

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