IntenseWebs/openvino
3
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
Files
4122ef50d65ff313b6e0248b33566b0eb2bcb5dd
openvino/docs/IE_PLUGIN_DG/Intro.md
Ilya Churaev 6f0aaf2bb5 ngraph docs to master (#2568) 
* First draft of nGraph documentation

* updated according to review comments

* Updated

* Reviewed the nGraph Transformation section, added missing images

* Update nGraph_dg.md

* Delete python_api.md

Removed since there is already the nGraph_Python_API.md document with a comprehensive overview.

* Fixed links to images

Co-authored-by: Andrey Zaytsev <andrey.zaytsev@intel.com>
Co-authored-by: CCR\avladimi <anastasiya.ageeva@intel.com>
2020-10-07 14:49:47 +03:00

2.4 KiB
Raw Blame History

@mainpage Overview of Inference Engine Plugin Library

The plugin architecture of the Inference Engine 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.

Inference Engine Plugin Library

Inference Engine plugin dynamic library consists of several main components:

  1. [Plugin class](@ref plugin):
    • Provides information about devices of a specific type.
    • Can create an [executable network](@ref executable_network) instance which represents a Neural Network backend specific graph structure for a particular device in opposite to the InferenceEngine::ICNNNetwork interface which is backend-independent.
    • Can import an already compiled graph structure from an input stream to an [executable network](@ref executable_network) object.
  2. [Executable Network class](@ref executable_network):
    • 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 infer_request).
    • Can export an internal backend specific graph structure to an output stream.
  3. [Inference Request class](@ref infer_request):
    • Runs an inference pipeline serially.
    • Can extract performance counters for an inference pipeline execution profiling.
  4. [Asynchronous Inference Request class](@ref async_infer_request):
    • Wraps the [Inference Request](@ref infer_request) class and runs pipeline stages in parallel on several task executors based on a device-specific pipeline structure.

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 <dldt source dir>/docs/template_plugin.

Detailed guides

  • [Build](@ref plugin_build) a plugin library using CMake*
  • Plugin and its components [testing](@ref plugin_testing)
  • [Quantized networks](@ref quantized_networks)
  • [Writing ngraph transformations](@ref ngraph_transformation) guide

API References