openvino/docs/OV_Runtime_UG/Deep_Learning_Inference_Engine_DevGuide.md

OpenVINO™ Runtime User Guide

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openvino_2_0_transition_guide openvino_docs_IE_DG_Integrate_with_customer_application_new_API openvino_docs_OV_Runtime_UG_Model_Representation ngraph_transformation openvino_docs_deployment_optimization_guide_dldt_optimization_guide openvino_docs_IE_DG_Device_Plugins Direct ONNX Format Support <openvino_docs_IE_DG_ONNX_Support> openvino_docs_IE_DG_Paddle_Support openvino_docs_IE_DG_Int8Inference openvino_docs_IE_DG_Bfloat16Inference openvino_docs_IE_DG_DynamicBatching openvino_docs_IE_DG_ShapeInference openvino_docs_IE_DG_Model_caching_overview openvino_docs_IE_DG_Extensibility_DG_Intro openvino_docs_IE_DG_Memory_primitives openvino_docs_IE_DG_network_state_intro
openvino_docs_IE_DG_API_Changes openvino_docs_IE_DG_Known_Issues_Limitations openvino_docs_IE_DG_Glossary

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Introduction

Inference Engine is a set of C++ libraries with C and Python bindings providing a common API to deliver inference solutions on the platform of your choice. Use the Inference Engine API to read the Intermediate Representation (IR), ONNX and execute the model on devices.

Inference Engine uses a plugin architecture. Inference Engine plugin is a software component that contains complete implementation for inference on a certain Intel® hardware device: CPU, GPU, VPU, etc. Each plugin implements the unified API and provides additional hardware-specific APIs.

The scheme below illustrates the typical workflow for deploying a trained deep learning model:

Video

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• • Inference Engine Concept. Duration: 3:43

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