Introduction
In the rapidly evolving world of web development, the advent of WebGPU and ONNX Runtime Web (ORT Web) marks a significant milestone. Leveraging these innovative technologies, developers are now able to execute machine learning models directly in the browser, introducing a new era of faster and more efficient applications. In this blog post, we will explore how WebGPU, ONNX (Open Neural Network Exchange) Runtime Web, and Transformer.js can be utilized to build Reading Comprehension with Aggregated Gradients (RAG).
Understanding WebGPU
WebGPU is a vital step towards utilizing GPU in web browsers for efficient computations. It provides developers with low-level access to GPU hardware for enhanced performance in modern web applications. Its unveiling symbolizes major strides in machine learning, computer graphics, and data processing.
Exploring ONNX Runtime Web
ONNX Runtime Web, on the other hand, is a JavaScript library designed to run pre-trained ONNX models in browsers. It makes full use of WebAssembly and WebGL for compute acceleration, aiding in the development of remarkably powerful client-side applications.
Teaming up WebGPU with ONNX Runtime Web
Combining the capabilities of WebGPU and ONNX Runtime Web lets developers harness the power of efficient GPU computation to run machine learning models in a browser. This results in improved performance and faster prediction times, paving the way for real-time interactive applications.
The Role of Transformer.js
Transformer.js is a JavaScript library designed to convert, compress and accelerate the execution of Transformer models on the web. With the help of Transformer.js, developers can import models encoded in multiple formats, convert them into a compressed format optimized for fast loading and execution, and then serve them directly to web browsers for ultra-efficient computation.
Building RAG with WebGPU, ONNX Runtime Web, and Transformer.js
Reading Comprehension with Aggregated Gradients (RAG) is a technique designed to assist in question answering tasks. By utilizing WebGPU, ONNX Runtime Web, along with Transformer.js, developers can build effective RAG applications, making machine learning models perform without requiring access to a server.
Conclusion
WebGPU, ONNX Runtime Web, and Transformer.js represent a significant leap for browser-based applications. By enabling efficient machine learning computations directly in the browser, these technologies hold the promise of creating a new realm of powerful, real-time, interactive applications.
Reference Documentation
For an in-depth understanding of such technologies, it’s recommended to explore the original blog post on Use WebGPU, ONNX Runtime Web & Transformer.js to build RAG and review the latest articles on Microsoft’s cloud products and related documentation on the Microsoft Tech Community and Microsoft’s official site.
“Building RAG applications with WebGPU + ONNX Runtime Web + Transformer.js by Phi-3-mini”, “Using WebGPU + ONNX Runtime Web + Transformer.js for Phi-3-mini RAG applications development”, “Phi-3-mini’s guide on creating RAG applications using WebGPU + ONNX Runtime Web + Transformer.js”, “Detailed steps to build RAG applications using WebGPU + ONNX Runtime Web + Transformer.js by Phi-3-mini”
