14c1e98e8c
* remove formatTimeMilli from time_utils.cpp * add traceCallStacks test case * add traceCallStacks test case in format_test.cpp * add param:"test" to function TraceCallStacks() * catch the exception of checkFormat * add space for try catch * rollback time_utils.cpp time_utils.hpp and log_utils_format_test.cpp * modify testcase for log.hpp * modify testcase from format_s to format_s_d_ld_u_lu2
OpenVINO™ Toolkit
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.
Resources
- Docs: https://docs.openvino.ai/
- Wiki: https://github.com/openvinotoolkit/openvino/wiki
- Issue tracking: https://github.com/openvinotoolkit/openvino/issues
- Storage: https://storage.openvinotoolkit.org/
- Additional OpenVINO™ toolkit modules: https://github.com/openvinotoolkit/openvino_contrib
- Intel® Distribution of OpenVINO™ toolkit Product Page
- Intel® Distribution of OpenVINO™ toolkit Release Notes
Support
Please report questions, issues and suggestions using:
- The
openvinotag on StackOverflow* - GitHub* Issues
- Forum
* Other names and brands may be claimed as the property of others.