Taylor Yeonbok Lee 746b77c74a [GPU] Revised unique ID setting scheme. (#10548)
* Revised unique ID setting scheme. Previously it was using program id to distinguish the loop body networks' id.
However, it results in cl cache miss for same network loaded multiple time, because program ids are differnt.
Now revised it to use parent primitive id instead of program_id for unique id of nodes in body networks.

* Revised adding unique_id to entry points to have a temporal number as unique id

* Revert the canceld change

* Added test to check whether two networks loaded from same function creates same cl cache
2022-02-22 09:34:46 +03:00
2022-02-03 16:51:26 +03:00
2021-05-31 15:24:56 +03:00
2018-10-16 13:45:03 +03:00
2020-11-17 16:44:44 +03:00

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.

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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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C++ 80.5%
Python 15.5%
C 2.8%
CMake 0.9%
Cython 0.1%