66f4c69b90
* Draft implementation of the telemetry sender utility * Examples of sending telemetry from the MO * More statistic about the model. * Intentional broken file to fail Mask-RCNN ONNX model conversion * Added joined list of ops used * Added requests to the requrements file and update BOM to include necessary files related to telemetry * Send telemetry alwasys * Refactored usage of GUID usage in the telemetry * Enabled sending telemetry always * Simplified function "TelemetryBackend.send" * Use other approach to send information about session to GA * Added automatic registration of the telemetry backends and allow to choose it during the telemetry class instantiation * Added "requests" as a requirement. Wrapped usage of requests module to not crash the app * Added timeout for sending data to GA. Increased the queue size to 1000 * Finalize Telemetry class implementation * Do not fail MO if non-critical component is not installed and updated Telemetry GA with the default property * Added sending version to a separate event * Use default TID to send the data * Set lower bound for the requests module which does not contain vulnerabilities Co-authored-by: Evgeny Lazarev <elazarev.nnov@gmail.com> |
||
|---|---|---|
| .ci | ||
| .github | ||
| cmake | ||
| docs | ||
| inference-engine | ||
| licensing | ||
| model-optimizer | ||
| ngraph | ||
| openvino | ||
| scripts | ||
| tests | ||
| thirdparty | ||
| tools | ||
| .gitattributes | ||
| .gitignore | ||
| .gitmodules | ||
| CMakeLists.txt | ||
| CODEOWNERS | ||
| install_build_dependencies.sh | ||
| Jenkinsfile | ||
| LICENSE | ||
| README.md | ||
| SECURITY.md | ||
OpenVINO™ Toolkit - Deep Learning Deployment Toolkit repository
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.
Resources:
- Docs: https://docs.openvinotoolkit.org/
- Wiki: https://github.com/openvinotoolkit/openvino/wiki
- Issue tracking: https://github.com/openvinotoolkit/openvino/issues
- Additional OpenVINO modules: https://github.com/openvinotoolkit/openvino_contrib
- HomePage
- OpenVINO™ 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.