cad355a03e
* Adjusted documentation labels * Renamed images * fix doc tests Co-authored-by: CCR\ntyukaev <nikolay.tyukaev@intel.com> # Conflicts: # docs/IE_PLUGIN_DG/ExecutableNetwork.md
113 lines
5.2 KiB
Markdown
113 lines
5.2 KiB
Markdown
# Documentation {#documentation}
|
|
|
|
@sphinxdirective
|
|
|
|
.. toctree::
|
|
:maxdepth: 1
|
|
:caption: Converting and Preparing Models
|
|
:hidden:
|
|
|
|
openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide
|
|
omz_tools_downloader
|
|
|
|
|
|
.. toctree::
|
|
:maxdepth: 1
|
|
:caption: Deploying Inference
|
|
:hidden:
|
|
|
|
openvino_docs_OV_UG_OV_Runtime_User_Guide
|
|
openvino_2_0_transition_guide
|
|
openvino_deployment_guide
|
|
openvino_inference_engine_tools_compile_tool_README
|
|
|
|
|
|
.. toctree::
|
|
:maxdepth: 1
|
|
:caption: Tuning for Performance
|
|
:hidden:
|
|
|
|
openvino_docs_optimization_guide_dldt_optimization_guide
|
|
openvino_docs_MO_DG_Getting_Performance_Numbers
|
|
openvino_docs_model_optimization_guide
|
|
openvino_docs_deployment_optimization_guide_dldt_optimization_guide
|
|
openvino_docs_tuning_utilities
|
|
openvino_docs_performance_benchmarks
|
|
|
|
|
|
.. toctree::
|
|
:maxdepth: 1
|
|
:caption: Graphical Web Interface for OpenVINO™ toolkit
|
|
:hidden:
|
|
|
|
workbench_docs_Workbench_DG_Introduction
|
|
workbench_docs_Workbench_DG_Install
|
|
workbench_docs_Workbench_DG_Work_with_Models_and_Sample_Datasets
|
|
Tutorials <workbench_docs_Workbench_DG_Tutorials>
|
|
User Guide <workbench_docs_Workbench_DG_User_Guide>
|
|
workbench_docs_Workbench_DG_Troubleshooting
|
|
|
|
.. toctree::
|
|
:maxdepth: 1
|
|
:hidden:
|
|
:caption: Media Processing and Computer Vision Libraries
|
|
|
|
Intel® Deep Learning Streamer <openvino_docs_dlstreamer>
|
|
openvino_docs_gapi_gapi_intro
|
|
OpenCV* Developer Guide <https://docs.opencv.org/master/>
|
|
OpenCL™ Developer Guide <https://software.intel.com/en-us/openclsdk-devguide>
|
|
|
|
.. toctree::
|
|
:maxdepth: 1
|
|
:caption: Add-Ons
|
|
:hidden:
|
|
|
|
ovms_what_is_openvino_model_server
|
|
ote_documentation
|
|
ovsa_get_started
|
|
|
|
.. toctree::
|
|
:maxdepth: 1
|
|
:caption: OpenVINO Extensibility
|
|
:hidden:
|
|
|
|
openvino_docs_Extensibility_UG_Intro
|
|
openvino_docs_transformations
|
|
OpenVINO Plugin Developer Guide <openvino_docs_ie_plugin_dg_overview>
|
|
|
|
.. toctree::
|
|
:maxdepth: 1
|
|
:hidden:
|
|
:caption: Use OpenVINO™ Toolkit Securely
|
|
|
|
openvino_docs_security_guide_introduction
|
|
openvino_docs_security_guide_workbench
|
|
openvino_docs_OV_UG_protecting_model_guide
|
|
ovsa_get_started
|
|
|
|
@endsphinxdirective
|
|
|
|
This section provides reference documents that guide you through developing your own deep learning applications with the OpenVINO™ toolkit. These documents will most helpful if you have first gone through the [Get Started](get_started.md) guide.
|
|
|
|
## Converting and Preparing Models
|
|
With the [Model Downloader](@ref omz_tools_downloader) and [Model Optimizer](MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md) guides, you will learn to download pre-trained models and convert them for use with the OpenVINO™ toolkit. You can provide your own model or choose a public or Intel model from a broad selection provided in the [Open Model Zoo](model_zoo.md).
|
|
|
|
## Deploying Inference
|
|
The [OpenVINO™ Runtime User Guide](./OV_Runtime_UG/openvino_intro.md) explains the process of creating your own application that runs inference with the OpenVINO™ toolkit. The [API Reference](./api_references.html) defines the OpenVINO Runtime API for Python, C++, and C. The OpenVINO Runtime API is what you'll use to create an OpenVINO™ inference application, use enhanced operations sets and other features. After writing your application, you can use the [Deployment with OpenVINO](./OV_Runtime_UG/deployment/deployment_intro.md) for deploying to target devices.
|
|
|
|
## Tuning for Performance
|
|
The toolkit provides a [Performance Optimization Guide](optimization_guide/dldt_optimization_guide.md) and utilities for squeezing the best performance out of your application, including [Accuracy Checker](@ref omz_tools_accuracy_checker), [Post-Training Optimization Tool](@ref pot_introduction), and other tools for measuring accuracy, benchmarking performance, and tuning your application.
|
|
|
|
## Graphical Web Interface for OpenVINO™ Toolkit
|
|
You can choose to use the [OpenVINO™ Deep Learning Workbench](@ref workbench_docs_Workbench_DG_Introduction), a web-based tool that guides you through the process of converting, measuring, optimizing, and deploying models. This tool also serves as a low-effort introduction to the toolkit and provides a variety of useful interactive charts for understanding performance.
|
|
|
|
## Media Processing and Computer Vision Libraries
|
|
|
|
The OpenVINO™ toolkit also works with the following media processing frameworks and libraries:
|
|
|
|
* [Intel® Deep Learning Streamer (Intel® DL Streamer)](@ref openvino_docs_dlstreamer) — A streaming media analytics framework based on GStreamer, for creating complex media analytics pipelines optimized for Intel hardware platforms. Go to the Intel® DL Streamer [documentation](https://dlstreamer.github.io/) website to learn more.
|
|
* [Intel® oneAPI Video Processing Library (oneVPL)](https://www.intel.com/content/www/us/en/develop/documentation/oneapi-programming-guide/top/api-based-programming/intel-oneapi-video-processing-library-onevpl.html) — A programming interface for video decoding, encoding, and processing to build portable media pipelines on CPUs, GPUs, and other accelerators.
|
|
|
|
You can also add computer vision capabilities to your application using optimized versions of [OpenCV](https://opencv.org/).
|
|
|