# Overview of OpenVINO Plugin Library {#openvino_docs_ie_plugin_dg_overview}

@sphinxdirective

.. toctree::
   :maxdepth: 1
   :caption: Converting and Preparing Models
   :hidden:

   Implement Plugin Functionality <openvino_docs_ov_plugin_dg_plugin>
   Implement Compiled Model Functionality <openvino_docs_ov_plugin_dg_compiled_model>
   Implement Synchronous Inference Request <openvino_docs_ov_plugin_dg_infer_request>
   Implement Asynchronous Inference Request <openvino_docs_ov_plugin_dg_async_infer_request>
   Provide Plugin Specific Properties <openvino_docs_ov_plugin_dg_properties>
   Implement Remote Context <openvino_docs_ov_plugin_dg_remote_context>
   Implement Remote Tensor <openvino_docs_ov_plugin_dg_remote_tensor>
   openvino_docs_ov_plugin_dg_plugin_build
   openvino_docs_ov_plugin_dg_plugin_testing
   openvino_docs_ie_plugin_detailed_guides
   openvino_docs_ie_plugin_api_references

@endsphinxdirective

The plugin architecture of the OpenVINO allows to develop and plug independent inference 
solutions dedicated to different devices. Physically, a plugin is represented as a dynamic library 
exporting the single `CreatePluginEngine` function that allows to create a new plugin instance.

OpenVINO Plugin Library
-----------------------

OpenVINO plugin dynamic library consists of several main components:

1. [Plugin class](@ref openvino_docs_ov_plugin_dg_plugin):
    - Provides information about devices of a specific type.
    - Can create an [compiled model](@ref openvino_docs_ov_plugin_dg_compiled_model) instance which represents a Neural Network backend specific graph structure for a particular device in opposite to the ov::Model 
    which is backend-independent.
    - Can import an already compiled graph structure from an input stream to an 
    [compiled model](@ref openvino_docs_ov_plugin_dg_compiled_model) object.
2. [Compiled Model class](@ref openvino_docs_ov_plugin_dg_compiled_model):
    - Is an execution configuration compiled for a particular device and takes into account its capabilities.
    - Holds a reference to a particular device and a task executor for this device.
    - Can create several instances of [Inference Request](@ref openvino_docs_ov_plugin_dg_infer_request).
    - Can export an internal backend specific graph structure to an output stream.
3. [Inference Request class](@ref openvino_docs_ov_plugin_dg_infer_request):
    - Runs an inference pipeline serially.
    - Can extract performance counters for an inference pipeline execution profiling.
4. [Asynchronous Inference Request class](@ref openvino_docs_ov_plugin_dg_async_infer_request):
    - Wraps the [Inference Request](@ref openvino_docs_ov_plugin_dg_infer_request) class and runs pipeline stages in parallel on several task executors based on a device-specific pipeline structure.
5. [Plugin specific properties](@ref openvino_docs_ov_plugin_dg_properties):
    - Provides the plugin specific properties.
6. [Remote Context](@ref openvino_docs_ov_plugin_dg_remote_context):
    - Provides the device specific remote context. Context allows to create remote tensors.
7. [Remote Tensor](@ref openvino_docs_ov_plugin_dg_remote_tensor)
    - Provides the device specific remote tensor API and implementation.

> **NOTE**: This documentation is written based on the `Template` plugin, which demonstrates plugin 

development details. Find the complete code of the `Template`, which is fully compilable and up-to-date,
at `<openvino source dir>/src/plugins/template`.


Detailed guides
-----------------------

* [Build](@ref openvino_docs_ov_plugin_dg_plugin_build) a plugin library using CMake
* Plugin and its components [testing](@ref openvino_docs_ov_plugin_dg_plugin_testing)
* [Quantized networks](@ref openvino_docs_ov_plugin_dg_quantized_models)
* [Low precision transformations](@ref openvino_docs_OV_UG_lpt) guide
* [Writing OpenVINO™ transformations](@ref openvino_docs_transformations) guide

API References
-----------------------

* [OpenVINO Plugin API](@ref ov_dev_api)
* [OpenVINO Transformation API](@ref ie_transformation_api)