cbfb8a1678
* Brushed the general optimization page * Opt GUIDE, WIP * perf hints doc placeholder * WIP * WIP2 * WIP 3 * added streams and few other details * fixed titles, misprints etc * Perf hints * movin the runtime optimizations intro * fixed link * Apply suggestions from code review Co-authored-by: Tatiana Savina <tatiana.savina@intel.com> * some details on the FIL and other means when pure inference time is not the only factor * shuffled according to general->use-case->device-specifics flow, minor brushing * next iter * section on optimizing for tput and latency * couple of links to the features support matrix * Links, brushing, dedicated subsections for Latency/FIL/Tput * had to make the link less specific (otherwise docs compilations fails) * removing the Temp/Should be moved to the Opt Guide * shuffled the tput/latency/etc info into separated documents. also the following docs moved from the temp into specific feature, general product desc or corresponding plugins - openvino_docs_IE_DG_Model_caching_overview - openvino_docs_IE_DG_Int8Inference - openvino_docs_IE_DG_Bfloat16Inference - openvino_docs_OV_UG_NoDynamicShapes * fixed toc for ov_dynamic_shapes.md * referring the openvino_docs_IE_DG_Bfloat16Inference to avoid docs compilation errors * fixed main product TOC, removed ref from the second-level items * reviewers remarks * reverted the openvino_docs_OV_UG_NoDynamicShapes * reverting openvino_docs_IE_DG_Bfloat16Inference and openvino_docs_IE_DG_Int8Inference * "No dynamic shapes" to the "Dynamic shapes" as TOC * removed duplication * minor brushing * Caching to the next level in TOC * brushing * more on the perf counters ( for latency and dynamic cases) Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>
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
OpenVINO™ 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.