Master Generative AI with Amazon SageMaker

Table of Contents

1. Introduction
2. What is Hugging Face?
3. AWS's Partnership with Hugging Face
4. Customer Pain Points
5. Overcoming Challenges with AWS's Help
6. The Hugging Face LLM Inference Container
7. The Text Generation Inference Library (TGI)
8. Supported Model Architectures
9. Ease of Use of the LLM Container
10. Demo: Document Q&A Chatbot Application

Introduction

In this article, we will discuss the partnership between Hugging Face and AWS, focusing on how to get started with Generative AI using Amazon SageMaker. We will begin by understanding what Hugging Face is and its significance in the machine learning field. Then, we will explore the details of the partnership between Hugging Face and AWS, including the solutions provided by Hugging Face to make generative AI and machine learning more accessible.

Next, we will dive into the pain points faced by customers when running large language models and how Hugging Face and AWS have worked together to address these challenges. We will explore the Hugging Face LLM Inference Container, a purpose-built container designed for deploying and managing large language models securely and efficiently.

We will also discuss the Text Generation Inference Library (TGI), a solution created by Hugging Face to serve large language models. This library includes features such as model parallelism, optimized modeling code, custom CUDA kernels, and streaming for improved latency and throughput.

Throughout the article, we will highlight the various supported model architectures, making it easier for readers to understand which models are currently compatible with Hugging Face and AWS services. We will emphasize the ease of use of the LLM Container and provide a detailed demo of a Document Q&A Chatbot application built using the Hugging Face LLM Inference Container and Amazon SageMaker.

By the end of this article, readers will have a comprehensive understanding of Hugging Face, the AWS partnership, the challenges faced when deploying large language models, and the solutions provided by Hugging Face and AWS to overcome these challenges. They will also gain insights into the ease of use and capabilities of the Hugging Face LLM Inference Container through a practical demo.

Let's get started!

What is Hugging Face?

Hugging Face is a startup Based in Paris and New York, known for building the "GitHub for machine learning." They provide a platform called the Hugging Face Hub, hosting over 200,000 machine learning models, 30,000 data sets, and 85,000 machine learning demos called Spaces. Hugging Face is also the creator of popular machine learning libraries such as transformers, diffuses, and datasets, simplifying the development of generative AI applications.

AWS's Partnership with Hugging Face

AWS and Hugging Face have been partners since 2021, working together to make generative AI and machine learning more accessible to everyone. This partnership involves integrating Hugging Face's solutions into AWS, including SaaS solutions like inference endpoints and spaces. Additionally, Hugging Face provides a premium support offering called the Expert Acceleration Program on the AWS Marketplace. The partnership also includes collaborations with the SageMaker team to develop first-party integrations and solutions for the Amazon SageMaker service, such as deep learning containers for Hugging Face libraries.

Customer Pain Points

When it comes to deploying and running large language models (LLMs), customers often face several pain points. LLMs require significant computational resources, memory, and computational power to run efficiently. These models are often too big to fit on a single GPU, resulting in the need for model parallelism. Another critical concern is latency and throughput, especially for applications that require fast responses.

Furthermore, building generative AI applications differs from traditional software applications. While less coding is required, more Attention is needed for prompt engineering to ensure desired behaviors even when updating the model. Security and governance are also crucial for customers who have concerns about closed-source API black box solutions. They want to ensure compliance with security standards while having visibility into how the models are trained.

Overcoming Challenges with AWS's Help

To address these challenges, Hugging Face and AWS have collaborated on a range of solutions. They have created the Hugging Face LLM Inference Container for Amazon SageMaker, offering an efficient and secure way to deploy and manage LLMs. The container handles heavy lifting tasks such as model parallelism and optimization of latency and throughput.

One key component of the solution is the Text Generation Inference (TGI) library. Originally designed to serve BLOOM, the TGI library now supports various open available LLMs like LLaMA, Falcon, T5, and more. It implements model parallelism, tensor parallelism, and utilizes optimized modeling code and custom CUDA kernels to enhance performance. Notably, the library introduces features like flash attention for memory reduction and streaming to provide a seamless user experience.

To make it easier for users, Hugging Face has ensured compatibility with existing user experiences by following a similar API schema. Deploying an LLM model using the new container involves only a few lines of code. With the help of the Hugging Face model class from the SageMaker SDK, the image and model details are defined, and SageMaker takes care of the infrastructure management behind the scenes.

The Hugging Face LLM Inference Container

The Hugging Face LLM Inference Container is a purpose-built container designed specifically for deploying and managing large language models efficiently and securely. It simplifies the deployment process and offers optimizations for model parallelism, latency, and throughput. This container integrates seamlessly with the existing AWS ecosystem and provides a straightforward way to leverage the capabilities of Hugging Face and AWS services.

The Text Generation Inference Library (TGI)

The Text Generation Inference (TGI) library, created by Hugging Face, serves as a powerful solution for serving large language models. Initially developed for the BLOOM model, TGI addresses the challenges faced by deploying LLMs. It implements techniques like model parallelism and tensor parallelism to optimize the performance of LLMs. Additional optimizations include the use of flash attention to reduce memory requirements and streaming capabilities for enhanced user experiences. The TGI library supports popular open available LLMs like BLOOM, LLaMA, Falcon, StarCoder, and T5.

Supported Model Architectures

The Hugging Face LLM Inference Container and the Text Generation Inference library support various model architectures, giving users flexibility in choosing the right model for their needs. Supported models include LLaMA, T5, Falcon, and more. Compatibility with newer models like Flan-UL2 or LLaMA 2 is also ensured. The support for a wide range of model architectures enables users to leverage the latest advancements in generative AI.

Ease of Use of the LLM Container

Despite the complexities associated with deploying and running large language models, the Hugging Face LLM Inference Container offers a user-friendly experience. It maintains compatibility with existing user experiences and requires only a few lines of code to deploy a model. By leveraging the Hugging Face model class from the SageMaker SDK and specifying the required environment configurations, users can quickly deploy and serve LLMs via SageMaker. The container abstracts most of the infrastructure management complexities, allowing users to focus on building generative AI applications efficiently.

Demo: Document Q&A Chatbot Application

To showcase the capabilities of the Hugging Face LLM Inference Container, a demo of a Document Q&A Chatbot application is provided. The demo addresses the typical customer need of interrogating documents and extracting knowledge from them in a secure and private manner. The chatbot application, built with a web UI and a memory component, interacts with the deployed LLM model. The demo highlights how the LLM Inference Container and Amazon SageMaker can be used to build conversational applications that provide fast and accurate responses to user queries. The demo is available on GitHub, where users can explore the code and build upon it for their specific use cases.

Highlights

• Hugging Face is a startup known for building the "GitHub for machine learning."
• Hugging Face and AWS have partnered to make generative AI and machine learning more accessible.
• Customers face challenges when deploying and running large language models, such as computational resource requirements, latency, and throughput.
• The Hugging Face LLM Inference Container for Amazon SageMaker addresses these challenges and simplifies the deployment process.
• The Text Generation Inference (TGI) library, integrated with the LLM Container, offers optimized performance and supports various LLMs.
• The LLM Container is easy to use, compatible with existing user experiences, and handles infrastructure management seamlessly.
• A demo showcases the capabilities of the LLM Container through a Document Q&A Chatbot application, providing a secure and private way to extract knowledge from documents.

FAQ

Q: What is Hugging Face? A: Hugging Face is a startup that has built a platform called the Hugging Face Hub, hosting machine learning models, datasets, and demos. They also developed popular machine learning libraries like transformers, diffuses, and datasets.

Q: What is the partnership between AWS and Hugging Face? A: AWS and Hugging Face have partnered to make generative AI and machine learning more accessible. They have integrated solutions, built first-party integrations for Amazon SageMaker, and collaborated to optimize the deployment of large language models.

Q: What are the pain points faced by customers when deploying large language models? A: Customers often struggle with computational resource requirements, latency, throughput, prompt engineering, and concerns related to security and governance.

Q: How do Hugging Face and AWS overcome these challenges? A: They have developed the Hugging Face LLM Inference Container, designed specifically for deploying and managing large language models efficiently and securely. The Text Generation Inference (TGI) library addresses the challenges by implementing model parallelism, tensor parallelism, optimized modeling code, and custom CUDA kernels.

Q: Which model architectures are supported by the LLM Container and TGI library? A: The LLM Container and TGI library support popular model architectures like LLaMA, T5, Falcon, and more. Compatibility with newer models like Flan-UL2 or LLaMA 2 is also ensured.

Q: Is the LLM Container easy to use? A: Yes, the LLM Container is user-friendly and compatible with existing user experiences. It requires only a few lines of code to deploy a model, abstracting most of the infrastructure management complexities.

Q: Can You provide an example of a demo showcasing the LLM Container's capabilities? A: The Document Q&A Chatbot application demo demonstrates how the LLM Container can be used to build an application that extracts knowledge from documents while preserving security and privacy. The demo is available on GitHub for further exploration.