Supercharge Deep Learning Deployment: ONNX Models on GPU-Powered Multi-Model Endpoints

Supercharge Deep Learning Deployment: ONNX Models on GPU-Powered Multi-Model Endpoints

Supercharge Deep Learning Deployment: ONNX Models on GPU-Powered Multi-Model Endpoints

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Supercharge Deep Learning Deployment: ONNX Models on GPU-Powered Multi-Model Endpoints

As artificial intelligence and machine learning continue to make waves in today’s technology landscape, the Open Neural Network Exchange (ONNX) has emerged as an essential open-source standard for representing deep learning models. ONNX connects different frameworks and tools, providing a means of optimizing, standardizing, and exchanging machine learning models. This article is a continuation of the post “Run multiple deep learning models on GPU with Amazon SageMaker multi-model endpoints,” and today, we will showcase how to deploy ONNX-based models for multi-model endpoints (MMEs) using GPUs. Additionally, we will benchmark the performance benefits of ONNX with the ResNet50 model in comparison to PyTorch and TensorRT.

ONNX Runtime

ONNX Runtime (ORT) is an ML inference engine that optimizes model performance across multiple hardware platforms. It supports popular ML frameworks like PyTorch and TensorFlow, as well as features such as quantization and hardware acceleration. One notable feature of ONNX Runtime is TensorRT Optimization (ORT-TRT), which significantly boosts model performance by optimizing ONNX models for NVIDIA GPUs. We will also provide practical examples illustrating how ONNX Runtime works in conjunction with TensorRT optimizations.

Amazon SageMaker Triton Container Flow

Real-time inference with a SageMaker multi-model endpoint involves sending HTTPS requests containing the TargetModel header. The backbone behind this process is the Triton container, which features an HTTP server and offers dynamic batching capabilities. Moreover, Triton containers support multiple backends, including ONNX Runtime, and can process models on either CPUs or GPUs, depending on the model configuration.

Solution Overview

To ensure a seamless deployment of ONNX-based models in MMEs on GPUs, follow these steps:

  • Compile the model to ONNX format
  • Configure the model appropriately
  • Create a SageMaker multi-model endpoint

Prerequisites

Before diving into the ONNX deployment process, ensure you have access to an AWS account with the necessary permissions. You should also have appropriate SageMaker roles available.

In conclusion, adopting ONNX-based models for multi-model endpoints on GPUs offers numerous benefits. Not only does it streamline the deployment process for deep learning models, but it also enhances performance and flexibility, as evidenced by the ResNet50 example. As businesses continue to rely more heavily on ML models and AI solutions, ONNX serves as a powerful tool for simplifying and improving model deployment and optimization. By using ONNX in conjunction with GPU-powered MMEs, developers can further enhance the efficiency and effectiveness of their deep learning applications.

 
 
 
 
 
 
 
Casey Jones Avatar
Casey Jones
12 months ago

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