Simplifying LLM Deployment with Amazon SageMaker

Overview

In today’s ever-evolving digital landscape, Generative AI has emerged as a pivotal asset across diverse industries. Generative AI, an artificial intelligence class, exhibits the extraordinary capability to autonomously create an expansive range of content, encompassing music, visual art, text, images, and more. It revolutionizes industries and creative processes, offering exciting content generation and innovation possibilities.

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Introduction

Generative AI has emerged as a pivotal asset across diverse industries in today’s ever-evolving digital landscape.

Generative AI, an artificial intelligence class, exhibits the extraordinary capability to autonomously create an expansive range of content, encompassing music, visual art, text, images, and more. It revolutionizes industries and creative processes, offering exciting content generation and innovation possibilities.

Deployment Challenges

But when it comes to deploying the large language model for Generative AI applications, difficulties arise, as it demands substantial RAM support for processing these extensive models, auto scaling based on the traffic, etc. Additionally, maintaining one’s infrastructure for such deployments can pose significant challenges, including hardware costs, maintenance, and scalability issues.

This is where Amazon SageMaker steps in as the one-stop solution for Generative AI model deployment.

One Stop Deployment

Amazon SageMaker, a comprehensive machine learning service by Amazon Web Services (AWS), simplifies Generative AI model deployment and auto-scaling and adopts a pay-as-you-go pricing model. In this context, let’s delve deeper into the foundational model available in Amazon SageMaker and how it acts as a one stop solution for Generative AI deployments.

Foundational model in Amazon SageMaker

Foundation models are pre-trained on large amounts of data so that you can perform a wide range of tasks, such as article summarization and text, image, or video generation.

sage

Fig 1

In the above, Fig 1 represents the foundational model of Amazon SageMaker as part of JumpStart.

Deploying Generative AI model

We will be deploying the Llama2 7B model for Inferencing and showcasing the power of the Generative AI model on Text Generation.

Llama2:

The Llama 2 pre-trained Large Language models have been trained on a massive corpus of 2 trillion tokens, and they boast twice the context length compared to Llama 1. Furthermore, their fine-tuned models have undergone training using an extensive dataset comprising over 1 million human annotations.

Integrated IDE:
Amazon SageMaker Studio is an all-in-one integrated development environment (IDE) within a web-based interface. Here, you can seamlessly access a suite of specialized tools designed for every facet of machine learning (ML) development, encompassing tasks from data preparation to model construction, training, and deployment.’

Step-by-Step Guide

In the jumpstart model, look for the Text Generation model, as we can see many LLM models available for easy training and deployment.

We will go through the below steps for deploying Llama 7b model

Step 1 – Deployment Configuration

sage2

Fig 2

Above, Fig 2 represents the hosting instance type and other meta details. Once we configure it and click Deploy, it will go to the creating stage and be ready for real-time inference.

Amazon SageMaker also has an advanced option to train with our enterprise data, where the result of the LLM model will be more towards enterprise data.

sage3

Fig 3

The process begins by creating a training job in the background of training a model with Amazon SageMaker. This job is specifically designed for training and utilizes the data source chosen from Amazon S3. After the training process is finished, the model and its associated artifacts are produced. These trained models and artifacts are then employed to create an endpoint, which is used for performing inferences or predictions.

Step 2 – Inferencing the Llama2 7B

Once the Endpoint is created, use the endpoint from the studio option and open the notebook. It will take through the model inferencing part with examples.

Sample Code:

prompt ="Can you explain to me briefly what is Python programming language?"
payloads= {
    "inputs": prompt, 
    "parameters": {"max_new_tokens": 60, "top_p": 0.9, "temperature": 0.6, "return_full_text": False},
        }
query_response = query_endpoint(payloads)
print("Input Prompt")
print(prompt)
print("Generated Response")
print(f"> {query_response[0]['generation']}")

prompt ="Can you explain to me briefly what is Python programming language?"

payloads= {

"inputs": prompt,

"parameters": {"max_new_tokens": 60, "top_p": 0.9, "temperature": 0.6, "return_full_text": False},

}

query_response = query_endpoint(payloads)

print("Input Prompt")

print(prompt)

print("Generated Response")

print(f"> {query_response[0]['generation']}")

Sample Generated Response from LLM:

Input Prompt
Can you explain to me briefly what is Python programming language?
Generated Response
>Python is a programming language to create web applications, scripts, and other software. It is a high-level, interpreted programming language widely used for data analysis, machine learning, and scientific computing. Python is known for its readability and ease of use, making it a

Step 3 – Clean Up the Resource:

After conducting tests with various prompts, you can release both the models and the endpoint by utilizing the “Delete” button on the “Delete Endpoint” tab.

Conclusion

Deploying large language models presents its own set of challenges, from infrastructure complexity to managing fine-tuning and inference. However, Amazon SageMaker offers a one-stop solution that streamlines the entire process. With our example of deploying Llama7b and its efficient inference capabilities using a simple prompt, it’s evident that SageMaker is a powerful ally for overcoming the hurdles of large language model deployment, making it accessible and effective for a wide range of applications. Embracing this solution can unlock the true potential of AI-powered language understanding and generation in today’s fast-paced digital landscape.

Drop a query if you have any questions regarding LLM or Amazon SageMaker, and we will get back to you quickly.

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FAQs

1. What is LLM?

ANS: – Large Language Models, also known as LLMs, are deep learning architectures belonging to the category of transformer networks. These models can understand and generate various types of content, including text, images, audio, and more.

2. What are the benefits of using Amazon SageMaker for large language model deployment?

ANS: – Amazon SageMaker simplifies the deployment process for large language models, addressing infrastructure, scaling, and maintenance challenges.

3. What are the different model sizes available in Llama2?

ANS: – Llama 2 has different variants of model sizes, such as 7B, 13B, and 70B.

4. What are some of the potential applications for Llama 2?

ANS: – Llama 2 can be used for various applications, including machine translation, text summarization, question answering, and chatbots.

WRITTEN BY Ganesh Raj

Ganesh Raj V works as a Sr. Research Associate at CloudThat. He is a highly analytical, creative, and passionate individual experienced in Data Science, Machine Learning algorithms, and Cloud Computing. In a quest to learn and work with recent technologies, he strives hard to stay updated on advanced technologies along efficiently solving problems analytically.