OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
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Maxim Shevtsov e3cc4833f4
Auto batch smart reshape strict testing (once we moved to dim tracking) (#10253)
* fixed perf-counters

* explicit auto-batching params that should guarantee the auto-batching is triggered ( to avoid fallback to no-batching when the selected batch1 size is just 1)

* makeConvPoolReluNoReshapes and using that whenever applicable to gaurantee the auto-batching is required (not important for things like plugin/executable-network config tests, but important for the inference-requests)

* getDefaultNGraphFunctionForTheDevice moved to the ov_behavior_test_utils.hpp
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cmake Renamed ov_runtime => openvino, ov_ => openvino_ prefix (#10069) 2022-02-03 20:03:41 +03:00
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scripts [VPU] Rename INTEL_VPU to INTEL_MYRIAD, move thirdparty and vpu_dependencies (#9827) 2022-01-31 16:58:33 +03:00
src Auto batch smart reshape strict testing (once we moved to dim tracking) (#10253) 2022-02-12 02:00:34 +03:00
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CMakeLists.txt [VPU] Rename INTEL_VPU to INTEL_MYRIAD, move thirdparty and vpu_dependencies (#9827) 2022-01-31 16:58:33 +03:00
CODEOWNERS [VPU] Rename INTEL_VPU to INTEL_MYRIAD, move thirdparty and vpu_dependencies (#9827) 2022-01-31 16:58:33 +03:00
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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

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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