a3dc2649e2
* GitHub CI: Add nGraph ONNX check * Fix job name * Fix commands * Enable nGraph Docker * Fix * Fix * Fix * Remove Actions * Decrease a number of workers for executing models tests * Enable "Docker run tests" * Unset parallel execution * Add cloning models * Update model_zoo_preprocess.sh cmd * Fix model_zoo_preprocess.sh cmd * Add share * ls -alR /mnt/onnxtestdata * Change path * move clone models * Update script * Add wget * Update * Update to master * Update * Update * clone into tmp * Enable clone * Fix * Use model_zoo_preprocess * Add copy to share * Enable tests * Get MSFT * Run tests * Try 16 cores * rub tests * list models * run tests * Run tests, no --model_zoo_xfail * Run tests, -n 8 * Run tests, -n 1 * Run tests, -n 4 * Run tests, -n 2 * Run with -n 1 * Update info * First try to run onnx ci: * disable MSFT models for first try, * try to align onnx_models * Enable steps * Update cmake * Add destination for cmake build * Try to fix cmake build * set ninja and instal dependencies * Revert changes from Blaczkowski, Rafal * Add swapfile 15 GB, run on AMD CPU 16 cores, 64 GB RAM * Enable model_zoo_preprocess.sh * Add reference-if-able * Update * test_zoo_models.py -n 8 * Fix clone * Set LIN_VMSS_VENV_EPHEMERAL_WU2, F8s_v2 * git clone --single-branch * test_zoo_models.py -n 6, D16as_v4 * -n 4 * clean * -n 2 * -n 4, swap 48 GB * E16ds_v4 (16-128), -n 8 * -n 8 * Set LIN_VMSS_VENV_ONNX_WU2 * -n 4 * del -n 16 for ut Co-authored-by: rblaczko <rafal.blaczkowski@intel.com>
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.