Tag: Torchvision
Open source observability for AWS Inferentia nodes within Amazon EKS clusters | Amazon Web Services
Recent developments in machine learning (ML) have led to increasingly large models, some of which require hundreds of billions of parameters. Although they are...
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Identifying Pauli spin blockade using deep learning
Jonas Schuff1, Dominic T. Lennon1, Simon Geyer2, David L. Craig1, Federico Fedele1, Florian Vigneau1, Leon C. Camenzind2, Andreas V. Kuhlmann2, G. Andrew D. Briggs1,...
Access private repos using the @remote decorator for Amazon SageMaker training workloads | Amazon Web Services
As more and more customers are looking to put machine learning (ML) workloads in production, there is a large push in organizations to shorten...
AWS Inferentia2 builds on AWS Inferentia1 by delivering 4x higher throughput and 10x lower latency | Amazon Web Services
The size of the machine learning (ML) models––large language models (LLMs) and foundation models (FMs)––is growing fast year-over-year, and these models need faster and...
Host ML models on Amazon SageMaker using Triton: ONNX Models | Amazon Web Services
ONNX (Open Neural Network Exchange) is an open-source standard for representing deep learning models widely supported by many providers. ONNX provides tools for optimizing...
Scale your machine learning workloads on Amazon ECS powered by AWS Trainium instances | Amazon Web Services
Running machine learning (ML) workloads with containers is becoming a common practice. Containers can fully encapsulate not just your training code, but the entire...
Host ML models on Amazon SageMaker using Triton: CV model with PyTorch backend | Amazon Web Services
PyTorch is a machine learning (ML) framework based on the Torch library, used for applications such as computer vision and natural language processing. One...
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...
How to extend the functionality of AWS Trainium with custom operators
Deep learning (DL) is a fast-evolving field, and practitioners are constantly innovating DL models and inventing ways to speed them up. Custom operators are...
Implement unified text and image search with a CLIP model using Amazon SageMaker and Amazon OpenSearch Service
The rise of text and semantic search engines has made ecommerce and retail businesses search easier for its consumers. Search engines powered by unified...
Hosting YOLOv8 PyTorch models on Amazon SageMaker Endpoints
Deploying models at scale can be a cumbersome task for many data scientists and machine learning engineers. However, Amazon SageMaker endpoints provide a simple...
Federated Learning on AWS with FedML: Health analytics without sharing sensitive data – Part 2
This blog post is co-written with Chaoyang He and Salman Avestimehr from FedML. Analyzing real-world healthcare and life sciences (HCLS) data poses several practical...
Create Amazon SageMaker models using the PyTorch Model Zoo
Deploying high-quality, trained machine learning (ML) models to perform either batch or real-time inference is a critical piece of bringing value to customers. However,...
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