Deploying Large Language Models (LLMs) on AWS SageMaker

This guide will walk you through the process of deploying Large Language Models (LLMs) on AWS SageMaker.

Prerequisites

Before you begin, ensure you have the following:

• An AWS account with the necessary permissions to create and manage SageMaker resources.
• A trained language model saved in a format compatible with SageMaker, such as TensorFlow SavedModel or PyTorch model.
• Basic knowledge of AWS SageMaker concepts and usage.

Steps

1. Prepare your model

• Ensure your language model is trained and saved in a format compatible with SageMaker.
• If necessary, package your model code and dependencies into a Docker container for SageMaker inference.

2. Upload your model to Amazon S3 or from HuggingFace

• Upload your trained model artifacts to an Amazon S3 bucket.
• Ensure that the IAM role used by SageMaker has permissions to access the S3 bucket.

3. Set up SageMaker

• Log in to the AWS Management Console and navigate to Amazon SageMaker.
• Create a new SageMaker notebook instance or use an existing one.
• Open a Jupyter notebook and create a SageMaker endpoint configuration.

4. Deploy your model

• Use the SageMaker SDK to deploy your model to a SageMaker endpoint.
• Specify the model artifacts location on S3 and configure the instance type and count.

5. Test your deployment

• Once the deployment is complete, test your model by sending sample input data to the SageMaker endpoint.
• Verify that the model inference results are as expected.

6. Monitor and manage your endpoint

• Monitor the performance and usage of your SageMaker endpoint using Amazon CloudWatch.
• If necessary, update or delete your SageMaker endpoint to manage costs and resources.

Additional Resources

• AWS SageMaker Documentation
• SageMaker Python SDK Documentation
• SageMaker Model Deployment Best Practices

Requirements for this repo

• Python 3.8 using venv
• Lamba Function with the code in lambda_function.py
• Sagemaker Endpoint present at Inferences tab in Sagemaker

Example Code Snippet (Python - SageMaker SDK)

import sagemaker
from sagemaker import Model

# Set up SageMaker session and role
sagemaker_session = sagemaker.Session()
role = "arn:aws:iam::123456789012:role/service-role/AmazonSageMaker-ExecutionRole-20220318T124567"

# Define model artifacts location on S3
model_data = "s3://your-bucket-name/path/to/model.tar.gz"

# Create a SageMaker model
model = Model(
    model_data=model_data,
    image_uri="your-model-image-uri",
    role=role,
    sagemaker_session=sagemaker_session
)

# Deploy the model to a SageMaker endpoint
predictor = model.deploy(
    instance_type="ml.m5.large",
    initial_instance_count=1
)

# Test the deployed model
result = predictor.predict("Sample input data")
print(result)