AWS Machine Learning Blog

Category: Graviton

Accelerated PyTorch inference with torch.compile on AWS Graviton processors

Originally PyTorch used an eager mode where each PyTorch operation that forms the model is run independently as soon as it’s reached. PyTorch 2.0 introduced torch.compile to speed up PyTorch code over the default eager mode. In contrast to eager mode, the torch.compile pre-compiles the entire model into a single graph in a manner that’s optimal for […]

Sprinklr_Services

Sprinklr improves performance by 20% and reduces cost by 25% for machine learning inference on AWS Graviton3

This is a guest post co-written with Ratnesh Jamidar and Vinayak Trivedi from Sprinklr. Sprinklr’s mission is to unify silos, technology, and teams across large, complex companies. To achieve this, we provide four product suites, Sprinklr Service, Sprinklr Insights, Sprinklr Marketing, and Sprinklr Social, as well as several self-serve offerings. Each of these products are […]

Accelerate NLP inference with ONNX Runtime on AWS Graviton processors

ONNX is an open source machine learning (ML) framework that provides interoperability across a wide range of frameworks, operating systems, and hardware platforms. ONNX Runtime is the runtime engine used for model inference and training with ONNX. AWS Graviton3 processors are optimized for ML workloads, including support for bfloat16, Scalable Vector Extension (SVE), and Matrix […]

Accelerating large-scale neural network training on CPUs with ThirdAI and AWS Graviton

This guest post is written by Vihan Lakshman, Tharun Medini, and Anshumali Shrivastava from ThirdAI. Large-scale deep learning has recently produced revolutionary advances in a vast array of fields. Although this stunning progress in artificial intelligence remains remarkable, the financial costs and energy consumption required to train these models has emerged as a critical bottleneck […]

Reduce Amazon SageMaker inference cost with AWS Graviton

Amazon SageMaker provides a broad selection of machine learning (ML) infrastructure and model deployment options to help meet your ML inference needs. It’s a fully-managed service and integrates with MLOps tools so you can work to scale your model deployment, reduce inference costs, manage models more effectively in production, and reduce operational burden. SageMaker provides […]

Optimized PyTorch 2.0 inference with AWS Graviton processors

New generations of CPUs offer a significant performance improvement in machine learning (ML) inference due to specialized built-in instructions. Combined with their flexibility, high speed of development, and low operating cost, these general-purpose processors offer an alternative to other existing hardware solutions. AWS, Arm, Meta and others helped optimize the performance of PyTorch 2.0 inference […]

Modulate makes voice chat safer while reducing infrastructure costs by a factor of 5 with Amazon EC2 G5g instances

This is a guest post by Carter Huffman, CTO and Co-founder at Modulate. Modulate is a Boston-based startup on a mission to build richer, safer, more inclusive online gaming experiences for everyone. We’re a team of world-class audio experts, gamers, allies, and futurists who are eager to build a better online world and make voice […]