Enhancing Model Inference with Amazon SageMaker and Sticky Session Routing
Amazon SageMaker is revolutionizing the way data scientists and developers build, train, and deploy machine learning models. With its fully managed service, SageMaker offers a range of ML infrastructure and deployment options to meet the diverse needs of users. Multimodal models, which incorporate various data types like text, audio, and images, have become increasingly popular, posing challenges like large data transfer overhead and slow response times.
Introducing sticky session routing on Amazon SageMaker Inference – a feature designed to optimize the performance and user experience of generative AI applications. By using sticky session routing, you can reuse previously processed information within the same session, reducing latency and improving overall user experience.
How It Works
Sticky session routing in combination with load balancing and stateful sessions in TorchServe enables seamless deployment and utilization of multimodal models like the Large Language and Vision Assistant (LLaVA) model. This approach minimizes data transfer overhead and optimizes response times by caching multimedia data in GPU memory.
Deployment Steps:
- Build a TorchServe Docker container and push it to Amazon ECR.
- Build TorchServe model artifacts and upload them to Amazon S3.
- Create the SageMaker endpoint.
- Run inference with the model.
Notebook Instance Setup:
Follow the steps to create a SageMaker notebook instance, deploy the LLaVa model, and run the inference notebook to experience the benefits of sticky session routing and stateful model inference.
Conclusion
The new sticky routing feature on Amazon SageMaker offers unique advantages for serving multimodal models with ultra-low latency and enhanced end-user experiences. By leveraging this feature, you can create stateful endpoints for your models and optimize performance. Give it a try for your use case and share your feedback with us!
About the Authors
Harish Rao, Raghu Ramesha, Lingran Xia, Naman Nandan, Li Ning, Frank Liu, Deepika Damojipurapu, and Alan Tan are passionate professionals at AWS, specializing in AI, ML, and SageMaker. They bring a wealth of expertise and experience to help customers unlock the full potential of machine learning technologies.