Deploy AI Inference with Azure Cognitive Services and NVIDIA Triton Server

Table of Contents

1. Introduction
2. Use Case 1: Live Captioning in Microsoft Teams
   1. Overview of Triton Inference Server
   2. Custom Backends for AI Inference Deployment
   3. Challenges and Solutions in Live Captioning Service
3. Use Case 2: Document Translation with Mixture of Experts Model
   1. Advantages of Larger Models and Multilingual Multitask Learning
   2. Building and Deploying Large-Scale Models
   3. Optimizations for Mixture of Experts Models
   4. Cost Efficiency and Performance Improvements
4. Conclusion
5. FAQ

Live Captioning in Microsoft Teams: Enabling Advanced AI Capabilities with Azure Cognitive Services

[Note: The following article provides an overview of how Microsoft and NVIDIA have collaborated to enable advanced AI capabilities in Microsoft Teams, specifically in live captioning and document translation. It discusses the use of NVIDIA Triton Inference Server and custom backends to deploy machine learning and deep learning models at scale, as well as the challenges and solutions involved in these use cases.]

Introduction

In this article, we will explore two interesting use cases where Microsoft and NVIDIA have partnered to enable advanced AI capabilities using Azure Cognitive Services. The first use case focuses on live captioning in Microsoft Teams, while the Second use case delves into document translation with a mixture of experts model.

Use Case 1: Live Captioning in Microsoft Teams

Overview of Triton Inference Server

Triton Inference Server is an open-source software that simplifies AI inference deployment by supporting deep learning and machine learning frameworks such as TensorFlow, PyTorch, ONNX Runtime, and TensorRT. It enables the deployment of machine learning and deep learning models at scale on various platforms, including CPUs, GPUs, Linux, Windows OS, public clouds, or Edge. With its features and utilities, Triton maximizes hardware utilization and provides efficient measurement of hardware utilization.

Custom Backends for AI Inference Deployment

Triton Inference Server supports custom Python and C++ backends, allowing users to have ultimate control over how to run and optimize inferencing. These custom backends are useful for implementing specific functionalities and addressing unique requirements of AI inference workloads. They enable the deployment of pre-trained models from frameworks like PyTorch or TensorFlow and provide optimal performance using backends like TensorRT, ONNX Runtime, or FasterTransform Backends.

Challenges and Solutions in Live Captioning Service

One of the use cases discussed is live captioning in Microsoft Teams. The live captioning service in Teams relies on a global distributed system that performs real-time speech recognition for millions of files across millions of Teams meetings. Traditionally, this posed a challenge in terms of cost and latency, as large-scale CPU usage was required to ensure real-time captioning. However, by leveraging NVIDIA Triton Inference Server and custom backends, Microsoft and NVIDIA were able to optimize the service in a cost-efficient manner. By decoupling the GPU and CPU workloads, the teams managed to reduce CPU requirements by about 25 percent and overall service costs by approximately 20 percent, all without compromising on latency or user experience.

Use Case 2: Document Translation with Mixture of Experts Model

Advantages of Larger Models and Multilingual Multitask Learning

The second use case focuses on document translation using a mixture of experts (MoE) model. Microsoft's AI at Scale initiative aims to build larger models that can cover multilingual and multitask learning. These larger models offer increased capacity and scalability, allowing for the support of multiple languages and tasks simultaneously. By utilizing sparsely activated models, the efficiency of scaling models is improved significantly.

Building and Deploying Large-Scale Models

To enable efficient training and deployment of large-scale models, Microsoft and NVIDIA collaborated to develop an efficient and scalable training framework. With the support of Azure ML auto scaling and Triton Server, a scalable pipeline was built for document translation. The pipeline leverages GPU resources effectively, thanks to dynamic batching implemented in Triton. This ensures the maximum utilization of GPUs and helps manage large-scale MoE models efficiently.

Optimizations for Mixture of Experts Models

Implementing MoE models presented a challenge in terms of efficiency, as they were slower compared to highly optimized dense models. To address this, NVIDIA optimized the Faster Transformer library by adding special Attention kernels for cheap attention and optimized kernels for running the MoE layers efficiently. These optimizations resulted in significant speed-ups and reduced runtime costs.

Cost Efficiency and Performance Improvements

With the optimizations in place, serving large-scale MoE models became more cost-efficient. The custom Python backend in Triton enables the integration of these optimized models, allowing for efficient AI inference deployment. By leveraging Triton's capabilities and optimizations, Microsoft and NVIDIA achieved notable improvements in translation quality and cost reduction. For instance, just one multilingual MoE model can replace around 10 separate models, resulting in significant cost savings.

Conclusion

The collaboration between Microsoft and NVIDIA has resulted in the development of advanced AI capabilities for live captioning and document translation. By leveraging NVIDIA Triton Inference Server and custom backends, these capabilities have been deployed at scale, improving efficiency, reducing costs, and enhancing user experience. The use of larger models and mixture of experts architectures has proven to be effective in achieving multilingual and multitask learning objectives. These advancements pave the way for further developments in AI-powered applications and services.

FAQ

Q: What is Triton Inference Server? A: Triton Inference Server is an open-source software that simplifies AI inference deployment by supporting deep learning and machine learning frameworks such as TensorFlow, PyTorch, ONNX Runtime, and TensorRT. It provides efficient deployment of machine learning and deep learning models on various platforms and maximizes hardware utilization.

Q: How did Microsoft and NVIDIA optimize the live captioning service in Microsoft Teams? A: By leveraging NVIDIA Triton Inference Server and custom backends, Microsoft and NVIDIA were able to decouple GPU and CPU workloads, reducing CPU requirements and overall service costs while ensuring real-time captioning and a seamless user experience.

Q: What are the advantages of using a mixture of experts (MoE) model for document translation? A: MoE models offer increased capacity and scalability, allowing for multilingual and multitask learning. By utilizing sparsely activated models and expert parallelism, Microsoft and NVIDIA were able to build larger models that support multiple languages simultaneously.

Q: How were the MoE models optimized for efficiency? A: NVIDIA optimized the Faster Transformer library by adding special attention kernels for cheap attention and optimized kernels for running the MoE layers efficiently. These optimizations resulted in significant speed-ups and reduced runtime costs.

Q: How did the collaboration between Microsoft and NVIDIA result in cost savings? A: By leveraging efficient model deployment using Triton Inference Server and custom backends, Microsoft and NVIDIA were able to achieve cost savings by replacing multiple separate models with a single multilingual MoE model. This resulted in reduced CPU requirements and overall service costs.

Q: What is the future of AI-powered applications and services Based on these advancements? A: The advancements in AI capabilities for live captioning and document translation open up opportunities for further developments in AI-powered applications and services. The use of larger models and multilingual multitask learning can enhance the efficiency and effectiveness of AI models in various domains.

Note: The FAQ section covers popular questions that users may have regarding the content of the article. However, additional FAQs specific to the use cases and technologies discussed can be provided upon request.