About OpenVINO
==============

.. toctree::
   :maxdepth: 1
   :hidden:

   about-openvino/key-features
   about-openvino/performance-benchmarks
   about-openvino/compatibility-and-support
   about-openvino/contributing
   Release Notes <about-openvino/release-notes-openvino>

OpenVINO is a toolkit for simple and efficient deployment of various deep learning models.
In this section you will find information on the product itself, as well as the software
and hardware solutions it supports.

OpenVINO (Open Visual Inference and Neural network Optimization) is an open-source software
toolkit designed to optimize, accelerate, and deploy deep learning models for user applications.
OpenVINO is actively developed by Intel® to work efficiently on a wide range of Intel® hardware platforms,
including CPUs (x86 and Arm), GPUs, and NPUs.


Features
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One of the main purposes of the OpenVINO toolkit is to streamline integration and deployment of
deep learning models. Yet its feature set is much wider, offering various optimization and implementation options.

To learn about the main properties of OpenVINO, see the :doc:`Key Features <about-openvino/key-features>`.

Architecture
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To learn more about how OpenVINO works, read the Developer documentation on its `architecture <https://github.com/openvinotoolkit/openvino/blob/master/src/docs/architecture.md>`__ and `core components <https://github.com/openvinotoolkit/openvino/blob/master/src/README.md>`__.

OpenVINO Ecosystem
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Along with the primary components of model optimization and runtime, the toolkit also includes:

* `Neural Network Compression Framework (NNCF) <https://github.com/openvinotoolkit/nncf>`__ - a tool for enhanced OpenVINO™ inference to get performance boost with minimal accuracy drop.
* :doc:`Openvino Notebooks <learn-openvino/interactive-tutorials-python>`- Jupyter Python notebook, which demonstrate key features of the toolkit.
* `OpenVINO Model Server <https://github.com/openvinotoolkit/model_server>`__ - a server that enables scalability via a serving microservice.
* :doc:`OpenVINO Training Extensions  <documentation/openvino-ecosystem/openvino-training-extensions>` – a convenient environment to train Deep Learning models and convert them using the OpenVINO™ toolkit for optimized inference.
* :doc:`Dataset Management Framework (Datumaro) <documentation/openvino-ecosystem/datumaro>` - a tool to build, transform, and analyze datasets.

Community
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OpenVINO community plays a vital role in the growth and development of the open-sourced toolkit. Users can contribute to OpenVINO and get support using the following channels:

* `OpenVINO GitHub issues, discussions and pull requests <https://github.com/openvinotoolkit/openvino>`__
* `OpenVINO Blog <https://blog.openvino.ai/>`__
* `Community Forum <https://community.intel.com/t5/Intel-Distribution-of-OpenVINO/bd-p/distribution-openvino-toolkit>`__
* `OpenVINO video <https://www.youtube.com/watch?v=_Jnjt21ZDS8&list=PLg-UKERBljNxdIQir1wrirZJ50yTp4eHv>`__
* `Support Information <https://www.intel.com/content/www/us/en/support/products/96066/software/development-software/openvino-toolkit.html>`__

Case Studies
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OpenVINO has been employed in various case studies across a wide range of industries and applications, including healthcare, retail, safety and security, transportation, and more. Read about how OpenVINO enhances efficiency, accuracy, and safety in different sectors on the `success stories page <https://www.intel.com/content/www/us/en/internet-of-things/ai-in-production/success-stories.html>`__.


