Yuan Xu c9bfd3bf8b Updating installation guide structure for 22/1 (#10343)
* Add Overview page

* update overview

* update install dev tools page

* Revert "Add Overview page"

* create overview page

* update movidius setup guide

* split the configurations for linux part to a separate topi

* split the general configurations for linux to a separate topic

* create a separate topic for configurations for vpu on windows

* create a separate topic on configurating gpu

* create a separate topic for configurations for ncs2

* update structure

* update structure

* update structure

* restructure

* update overview

* update

* update according to comments

* update structure

* update the structure

* correct naming

* correct naming

* update trademark symbol

* remove .bak file

* update

* test formatting

* update

* update

* fix errors

* add a leading sentence for GPU configurations

* update structure

* delete redundant files

* Update docs/install_guides/configurations-for-intel-gpu.md

Co-authored-by: Sergey Lyubimtsev <sergey.lyubimtsev@intel.com>

* Update docs/install_guides/configurations-for-ncs2.md

Co-authored-by: Sergey Lyubimtsev <sergey.lyubimtsev@intel.com>

* Update docs/install_guides/installing-openvino-config-ivad-vpu.md

Co-authored-by: Sergey Lyubimtsev <sergey.lyubimtsev@intel.com>

* fix formatting

* fix errors

* fix errors

* fix errors

Co-authored-by: Sergey Lyubimtsev <sergey.lyubimtsev@intel.com>
2022-02-18 19:47:00 +03:00
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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.

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.

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* Other names and brands may be claimed as the property of others.

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