Accelerate Inference of ONNX Models with OpenVINO Execution Provider
Table of Contents
- Introduction
- What is ONNX Runtime?
- Accelerating Inference with OpenVINO™ Toolkit
- How Does the OpenVINO Execution Provider Work?
- Getting Started with OpenVINO Execution Provider for ONNX Runtime
- Demo - Object Detection with OpenVINO Execution Provider
- Insights into the Architecture Diagram
- Reference Implementations from Intel® Edge Software Hub
- Explore OpenVINO Toolkit on Medium
- Conclusion
🚀 Introduction
In this article, we will explore the OpenVINO™ Execution Provider for ONNX Runtime, a powerful tool from Intel that helps accelerate inference for AI developers. We'll begin by understanding ONNX Runtime and its cross-platform capabilities. Then, we'll dive into how the OpenVINO Toolkit enhances the performance of deep learning models. We'll learn how developers can leverage the OpenVINO Execution Provider to accelerate inferencing of ONNX models, and even take a look at a demo showcasing object detection. Along the way, we'll gain insights into the architecture diagram and explore some useful resources. So let's get started!
🌟 What is ONNX Runtime?
ONNX Runtime is a cross-platform machine learning model accelerator. It allows developers to deploy deep learning models on a range of Intel compute devices, including those powered by frameworks like PyTorch, TensorFlow, and Keras. With ONNX Runtime, developers can import the onnxruntime library and start an inference session, providing input data for the model to generate the output. It offers seamless integration with a variety of popular frameworks and delivers fast and efficient inferencing capabilities.
⚡ Accelerating Inference with OpenVINO™ Toolkit
The OpenVINO™ Toolkit, developed by Intel, is designed to enhance the performance of deep learning models using advanced optimization techniques specifically optimized for Intel® hardware. By leveraging the power of the OpenVINO Toolkit through the OpenVINO Execution Provider for ONNX Runtime, developers can significantly accelerate the inferencing of their ONNX models. The OpenVINO Execution Provider allows developers to import the onnxruntime library along with the OpenVINO Package and make a simple modification to the inference session line of code, setting the provider as OpenVINO Execution Provider. This unlocks the potential of OpenVINO and enables developers to take full advantage of its optimizations while using the ONNX Runtime API.
🛠 How Does the OpenVINO Execution Provider Work?
The OpenVINO Execution Provider seamlessly integrates with the ONNX Runtime, delivering better inferencing performance on Intel® compute devices compared to generic acceleration. Through a collaboration between Intel and Microsoft, the OpenVINO Execution Provider utilizes the capabilities of OpenVINO to optimize the execution of subgraphs within the ONNX model. When an ONNX model is loaded into the ONNX Runtime application, it undergoes conversion into an in-memory graph representation. The graph partitioner or capability module queries the available backends and determines which parts of the graph are supported by the OpenVINO Execution Provider. When the subgraphs are executed, ONNX Runtime sends them to the Execution Provider, which performs the inference using OpenVINO and returns the output.
🚀 Getting Started with OpenVINO Execution Provider for ONNX Runtime
To get started with the OpenVINO Execution Provider for ONNX Runtime, developers need to install the onnxruntime-openvino package and its dependencies. Intel provides a set of samples that demonstrate the performance boost achieved with the OpenVINO Execution Provider. For example, the Object Detection sample utilizes a YOLO Deep Learning ONNX Model from the ONNX Model Zoo. By presenting a frame-by-frame video to the ONNX Runtime, the sample utilizes the OpenVINO Execution Provider to run inference on various Intel® hardware devices and perform object detection tasks. Developers can follow the installation process and sample code to experience the enhanced inferencing capabilities.
🎥 Demo - Object Detection with OpenVINO Execution Provider
Let's take a closer look at how developers can leverage the OpenVINO Execution Provider for object detection tasks. The Object Detection sample provided by Intel demonstrates the usage of the OpenVINO Execution Provider and its integration with the ONNX Runtime API. In this demo, a YOLO Deep Learning ONNX Model from the ONNX Model Zoo is utilized to detect up to 80 different objects in a frame-by-frame video. By setting the provider as OpenVINO Execution Provider, developers can observe the improved performance of the inferencing process. The output of the sample showcases the detected objects, such as persons, cars, motorbikes, and more, using the COCO dataset. This demo provides valuable insights into the usage of the OpenVINO Execution Provider for accelerating object detection tasks.
🏛 Insights into the Architecture Diagram
The architecture of the OpenVINO Execution Provider for ONNX Runtime involves the transformation of an ONNX model into an in-memory graph representation within the ONNX Runtime Application. This representation is then passed to the graph partitioner or capability module, which queries the available backends and determines the supported subgraphs. The Execution Provider receives subgraphs from ONNX Runtime and executes the inferencing process using the power of OpenVINO. It then returns the output to ONNX Runtime. This architecture enables the seamless integration of the OpenVINO Execution Provider for accelerated inferencing.
📚 Reference Implementations from Intel® Edge Software Hub
Intel® Edge Software Hub provides reference implementations that showcase the capabilities of the OpenVINO Execution Provider for ONNX Runtime. Two notable reference implementations are available for developers to explore. The first one focuses on detecting and tracking driver behavior and actions to ensure safe driving. It utilizes the power of OpenVINO to enhance the performance of the detection and tracking algorithms. The Second reference implementation is designed for vehicle event Recording to monitor the exterior of a vehicle and send events to a cloud dashboard. These implementations highlight the flexibility and potential of the OpenVINO Execution Provider in various edge computing scenarios.
📖 Explore OpenVINO Toolkit on Medium
For developers looking for more in-depth information and practical guides, Intel maintains a Medium Channel dedicated to exploring the OpenVINO Toolkit. The Medium channel regularly publishes new articles covering various topics related to OpenVINO, such as developer guides, how-to articles, and insights into the latest developments. It is a valuable resource for staying up-to-date with the advancements and best practices in utilizing the OpenVINO Toolkit for accelerated inferencing.
🔚 Conclusion
In this article, we have explored the OpenVINO Execution Provider for ONNX Runtime, a powerful tool that allows developers to accelerate the inferencing of their ONNX models. We started by understanding the capabilities of ONNX Runtime, a cross-platform machine learning model accelerator. We then delved into the benefits of using the OpenVINO Toolkit to enhance the performance of deep learning models. We learned how developers can leverage the OpenVINO Execution Provider to accelerate inferencing and even witnessed a demo of object detection. The insights into the architecture and the reference implementations provided further guidance in utilizing the OpenVINO Execution Provider effectively. Lastly, we highlighted the Intel® Edge Software Hub's contributions and the Medium channel for further exploration of the OpenVINO Toolkit. With the OpenVINO Execution Provider, developers can unlock the full potential of their ONNX models and achieve faster and more efficient inferencing on Intel® hardware. So go ahead, dive into the world of accelerated inferencing with the OpenVINO Execution Provider for ONNX Runtime!
Highlights
- Learn how to accelerate inferencing of ONNX models using the OpenVINO Execution Provider for ONNX Runtime
- Understand the benefits of the OpenVINO Toolkit for enhancing the performance of deep learning models
- Explore a demo of object detection with the OpenVINO Execution Provider
- Gain insights into the architecture of the OpenVINO Execution Provider for ONNX Runtime
- Discover reference implementations from Intel® Edge Software Hub for real-world use cases
- Stay up-to-date with the OpenVINO Toolkit through Intel's Medium channel
FAQ
Q: What is ONNX Runtime?
A: ONNX Runtime is a cross-platform machine learning model accelerator that allows developers to deploy deep learning models on a range of Intel compute devices.
Q: How does the OpenVINO Execution Provider work?
A: The OpenVINO Execution Provider seamlessly integrates with the ONNX Runtime, delivering better inferencing performance on Intel® compute devices. It optimizes the execution of subgraphs within the ONNX model using the capabilities of OpenVINO.
Q: How can developers get started with the OpenVINO Execution Provider for ONNX Runtime?
A: Developers can start by installing the onnxruntime-openvino package and its dependencies. Intel provides samples that demonstrate the performance boost achieved with the OpenVINO Execution Provider.
Q: Are there any reference implementations available for exploring the OpenVINO Execution Provider?
A: Yes, Intel® Edge Software Hub provides reference implementations for detecting and tracking driver behavior as well as vehicle event recording. These implementations showcase the capabilities of the OpenVINO Execution Provider in edge computing scenarios.
Q: Where can I find more information and guides on the OpenVINO Toolkit?
A: Intel maintains a Medium channel dedicated to exploring the OpenVINO Toolkit. It regularly publishes articles covering various topics related to OpenVINO, including developer guides and how-to articles.
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