Simplify and support your TorchServe workloads using Ray Serve Deep Learning Containers
TorchServe is no longer maintained, leaving teams to own the entire GPU inference stack. The AWS Ray Serve Deep Learning Container is a supported, pre-tested container with the framework, GPU drivers, and serving layer already assembled. This post walks through deploying a vision-language model on Amazon EKS using the Ray Serve DLC on a single GPU node.
The TorchServe platform, which facilitates model inference, has reached its end of life, with no further updates or security patches expected. This leaves teams responsible for managing dependencies and ensuring compatibility themselves. AWS offers an alternative solution in the form of Ray Serve Deep Learning Containers (DLCs), which provide pre-built, optimized Docker images for serving machine learning models.
These containers include the GPU stack, deep learning frameworks, and additional utilities for vision, audio, and multimodal workloads. The Ray Serve DLC specifically extends the inference capabilities introduced by TorchServe to the Ray Serve framework. In this article, we will deploy a vision-language model using the GPU variant of the Ray Serve DLC on Amazon Elastic Kubernetes Service (Amazon EKS).
The process involves setting up an Amazon EKS cluster with a single GPU node and deploying the Ray Serve DLC along with a ConfigMap containing the serving application code. The application is written as a Python class decorated with @serve.deployment, specifying the use of a single GPU. The model is loaded using the Transformers library and exposed as an HTTP endpoint.
With this setup, users can serve their models efficiently and focus on the core functionality without worrying about the underlying infrastructure or dependencies.
Written by urgent.news from AWS Machine Learning's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
