Unlocking the Potential of AI on Windows with ONIX Runtime and Olive

Table of Contents

1. Introduction
2. Microsoft Build 2023 Overview
3. The Importance of ONIX Runtime
4. The Hybrid Loop Development Pattern
5. Azure EP and Hybrid AI Inferencing
6. ONIX Runtime as a Gateway to Windows AI
7. ONIX Runtime Features and Benefits
   • Open Source and Extensible Framework
   • Model Packaging and Integration into Apps
   • Easy Portability across Different Platforms
   • Optimized for Specific Hardware and Platforms
8. Running ONIX Models on Different Execution Providers
   • CPU, GPU, and NPU Coverage
   • Support for Intel, AMD, and NVIDIA
   • Integration with JavaScript and Web GPU
9. Integrating Whisper Models with ONIX Runtime
   • Whisper Model Overview
   • Whisper Model Configurations
   • Running Whisper Models Locally and in the Cloud
10. Using Olive for Model Optimization
    • Olive Workflow and Optimization Process
    • Creating Configurations for Model Optimization
    • Optimizing Models for Specific Devices
11. ONIX Runtime and ML.NET Integration
    • Future Support for ML.NET Models
    • Converting ML.NET Models to ONIX Runtime
12. Conclusion

Introduction

Welcome to the ultimate guide to Microsoft Build 2023 and the exciting developments in AI inferencing and model optimization. In this article, we will explore the groundbreaking technology of ONIX Runtime and its role as the gateway to Windows AI. We will Delve into the hybrid loop development pattern, the Azure EP, and the benefits of using ONIX Runtime for AI inferencing across different platforms and hardware. You will also discover how to integrate Whisper models with ONIX Runtime using the powerful optimization tool called Olive. So let's dive in and unlock the full potential of AI on Windows!

Microsoft Build 2023 Overview

Microsoft Build is an annual developer conference that brings together software engineers, architects, and tech enthusiasts from around the world. The conference showcases the latest innovations, updates, and technologies from Microsoft across a wide range of fields, including AI, cloud computing, machine learning, and more. Build 2023 promises to be an exceptional event with deep dive Sessions and Q&A sessions to provide valuable insights and knowledge for developers.

The Importance of ONIX Runtime

ONIX Runtime is a groundbreaking technology announced at Build 2022 as the gateway to Windows AI. It allows third-party developers to access the same internal tooling that Microsoft uses for their apps and services, enabling them to leverage the power of AI in their own applications. ONIX Runtime provides a seamless integration of AI models into Windows devices, making it easier than ever for developers to deploy AI in Windows applications. With its open-source nature and support from various silicon partners, ONIX Runtime ensures compatibility, up-to-date optimization, and extensibility for AI inferencing across different hardware platforms.

The Hybrid Loop Development Pattern

The hybrid loop development pattern was introduced at Build 2022 as a flexible approach to AI inferencing. It enables developers to choose between local inferencing on the device or cloud inferencing Based on specific conditions and requirements. The hybrid loop development pattern allows for customization of the AI inferencing experience, taking into account factors such as power consumption, connectivity, performance, and privacy. By leveraging the capabilities of ONIX Runtime and Azure EP, developers can seamlessly switch between local and cloud inferencing, providing a tailored AI experience for their users.

Azure EP and Hybrid AI Inferencing

Azure EP (Execution Provider) is a crucial component of the hybrid loop development pattern and is integrated with ONIX Runtime. It enables developers to take AdVantage of the Azure cloud infrastructure for AI inferencing, providing a scalable, secure, and cost-effective solution. With Azure EP, developers have a single entry point for AI inferencing, whether they choose to call the cloud or perform local inferencing. This flexibility empowers developers to optimize their AI applications based on factors such as battery life, model accuracy, connectivity, and privacy concerns, offering a customizable and efficient AI experience for end-users.

ONIX Runtime as a Gateway to Windows AI

ONIX Runtime plays a pivotal role as the gateway to Windows AI by providing developers with a unified and optimized platform for AI inferencing on Windows devices. It bridges the gap between AI models and hardware accelerators, ensuring seamless integration and efficient utilization of the underlying hardware. With ONIX Runtime, developers can effortlessly deploy AI models on Windows, Windows Unarm, iOS, Android, and Linux platforms with minimal code modifications. The extensible nature of ONIX Runtime allows for easy porting of models across different platforms, making it a versatile and valuable tool for AI development.

ONIX Runtime Features and Benefits

ONIX Runtime offers a wide range of features and benefits that empower developers to optimize and deploy AI models effectively. Let's explore some of these key features and how they enhance the AI inferencing experience:

• Open Source and Extensible Framework: ONIX Runtime is an open-source framework that allows developers to contribute and enhance its capabilities. It ensures regular updates, compatibility with the latest hardware, and optimization techniques from various silicon partners. Developers can leverage the extensibility of ONIX Runtime to incorporate their own custom optimizations and performance enhancements.

• Model Packaging and Integration into Apps: ONIX Runtime simplifies the process of packaging AI models and integrating them into applications. Developers can use ONIX Runtime to Create a Package that includes the optimized model, the necessary execution providers, and boilerplate code in multiple programming languages. This streamlined process reduces development time and allows for seamless integration of AI inferencing into existing applications.

• Easy Portability across Different Platforms: ONIX Runtime enables developers to build AI models once and deploy them across various platforms, including Windows, iOS, Android, and Linux. The unified APIs and execution providers ensure that AI models work seamlessly across different hardware architectures, providing consistent performance and results.

• Optimized for Specific Hardware and Platforms: ONIX Runtime offers specific execution providers for different hardware accelerators, such as CPUs, GPUs, and NPUs. Developers can optimize the performance and accuracy of their AI models by targeting specific hardware platforms. This optimization ensures the best possible inferencing experience on each device, maximizing efficiency and resource utilization.

With these features, ONIX Runtime empowers developers to create high-performance AI applications that deliver exceptional accuracy, efficiency, and user experience across multiple platforms and devices.

Running ONIX Models on Different Execution Providers

ONIX Runtime supports the execution of ONIX models on various execution providers, including CPUs, GPUs, and NPUs. Let's explore the options for running ONIX models on different execution providers:

• CPU, GPU, and NPU Coverage: ONIX Runtime supports traditional CPUs and GPUs, making it compatible with a wide range of hardware configurations. It also offers coverage for NPUs (Neural Processing Units), specialized hardware accelerators designed specifically for AI workloads. The support for CPUs, GPUs, and NPUs enables developers to leverage the available hardware resources efficiently and achieve optimal performance.

• Support for Intel, AMD, and NVIDIA: ONIX Runtime works seamlessly with hardware from leading manufacturers such as Intel, AMD, and NVIDIA. It provides specific execution providers and optimizations for these hardware platforms, ensuring compatibility, performance, and accuracy. Developers can utilize the full potential of their hardware resources to unlock the AI capabilities of Windows.

• Integration with JavaScript and Web GPU: ONIX Runtime extends its support to JavaScript and Web GPU, enabling AI inferencing directly in web browsers. This integration opens up new possibilities for AI-powered web applications and allows developers to leverage the powerful capabilities of ONIX Runtime across different programming languages and platforms.

By offering a wide range of execution providers and platform support, ONIX Runtime caters to diverse hardware configurations and enables developers to deliver optimal AI inferencing experiences to end-users.

Integrating Whisper Models with ONIX Runtime

Whisper models are a Type of AI model used for speech and audio processing. Integrating Whisper models with ONIX Runtime allows developers to perform speech recognition, audio transcription, and other audio-related tasks with exceptional accuracy and efficiency. Let's dive into the process of integrating Whisper models with ONIX Runtime using the powerful optimization tool called Olive:

• Whisper Model Overview: Whisper models are designed for speech recognition and audio processing tasks. They come in multiple sizes, ranging from tiny to large, with varying levels of word error rate and accuracy. Developers can choose the appropriate Whisper model based on their specific requirements and available hardware resources.

• Whisper Model Configurations: Olive provides convenient scripts and tooling to generate configuration files for Whisper models. These configuration files contain essential parameters for model optimization, such as the model name, target hardware information, evaluators for performance metrics, and optimization passes. Developers can modify these configurations to cater to their specific needs and hardware environments.

• Running Whisper Models Locally and in the Cloud: Once the configuration files are generated, developers can use Olive to optimize the Whisper models for their target hardware. Olive leverages ONIX Runtime's optimization capabilities to create optimized models, integrated with execution providers and post-processing logic. These optimized models can then be used for local inferencing on devices or cloud inferencing, depending on the specific requirements and conditions.

Integrating Whisper models with ONIX Runtime unlocks the full potential of speech and audio processing, allowing developers to create AI-powered applications with industry-leading accuracy and performance.

Using Olive for Model Optimization

Olive is a powerful optimization tool that simplifies the process of optimizing AI models using ONIX Runtime. It provides a workflow and a set of tools to optimize models for specific hardware and platforms. Let's explore the key aspects of using Olive for model optimization:

• Olive Workflow and Optimization Process: Olive follows a systematic workflow for model optimization using ONIX Runtime. It begins by selecting the target hardware and creating configuration files for the optimization process. The configuration files contain information about the model, execution providers, evaluators, and optimization passes. These configuration files act as a blueprint for optimizing the model.

• Creating Configurations for Model Optimization: Olive provides scripts and tools to generate configuration files for different models and hardware configurations. Developers can use these scripts to customize the optimization process and fine-tune it for their specific requirements. Olive's extensibility allows developers to incorporate their own custom configuration files and evaluators to further enhance the optimization process.

• Optimizing Models for Specific Devices: With Olive, developers can optimize AI models for specific devices and hardware configurations. Olive leverages ONIX Runtime's capabilities to create optimized models that are tailored to the target hardware. This optimization process ensures maximum performance and efficiency while maintaining the desired accuracy and quality of the inferencing results.

Olive serves as a valuable tool in the AI development workflow, empowering developers to optimize and deploy AI models efficiently using ONIX Runtime. It streamlines the optimization process and provides a unified platform for AI inferencing on Windows devices.

ONIX Runtime and ML.NET Integration

ML.NET is a popular machine learning framework for developing AI models using C# and .NET. Integration between ONIX Runtime and ML.NET opens up new possibilities for AI development and deployment. Let's explore the integration between ONIX Runtime and ML.NET:

• Future Support for ML.NET Models: The integration between ONIX Runtime and ML.NET is a future plan. The ML.NET team is aware of the importance of ONIX Runtime and its capabilities. In the future, developers can expect ML.NET models to be seamlessly integrated with ONIX Runtime, providing a unified experience for AI development and deployment.

• Converting ML.NET Models to ONIX Runtime: While the direct integration between ML.NET and ONIX Runtime is currently in the works, developers can convert their ML.NET models to ONIX models for optimization and deployment using Olive. The ONIX model can then be executed using ONIX Runtime, ensuring compatibility and performance across different platforms and devices.

This integration will empower developers to leverage the strengths of both ML.NET and ONIX Runtime, combining the simplicity and familiarity of ML.NET with the efficiency and optimization capabilities of ONIX Runtime.

Conclusion

In this ultimate guide to Microsoft Build 2023, we have explored the revolutionary technology of ONIX Runtime and its role as the gateway to Windows AI. We have discovered the hybrid loop development pattern, the Azure EP, and the benefits of using ONIX Runtime for AI inferencing across different platforms. We have also delved into Olive, the optimization tool for ONIX Runtime, and its integration with Whisper models for speech and audio processing. The future integration between ONIX Runtime and ML.NET holds great promise for AI development and deployment. With these advancements, developers can unlock the full potential of AI on Windows and deliver exceptional experiences to end-users. So, as you embark on your AI Journey, remember to harness the power of ONIX Runtime and its ecosystem to create intelligent and innovative solutions.