Category: AWS Batch
Today we’re announcing Research and Engineering Studio on AWS, a self-service portal to help scientists and engineers access and manage virtual desktops to see their data and run their interactive applications in the cloud.
AWS is developing new tools that enable easier and faster deployment of level 3/4 digital twins. This post discusses how a fleet calibrated level 3 digital twin can be cost effectively deployed on AWS Cloud.
Today, we’re diving deep into the open-source frameworks that move MPI messages around, and showing you how work we did in the Open MPI and libfabrics community lead to an improvement for EFA users – and everyone else, too.
AWS Batch recently added support for Graviton and Windows containers on Fargate. Read about how these and other features like large task sizes and configurable local storage make AWS Batch on Fargate a fantastic serverless solution for your batch workloads.
In this post we’ll show how generative AI, combined with conventional physics-based CFD can create a rapid design process to explore new design concepts in automotive and aerospace from just a single image.
How Amazon’s Search M5 team optimizes compute resources and cost with fair-share scheduling on AWS Batch
In this post, we share how Amazon Search optimizes their use of accelerated compute resources using AWS Batch fair-share scheduling to schedule distributed deep learning workloads.
If you want to build enterprise-grade HPC on AWS, what’s the best path to get started? Should you create a new AWS account and build from scratch? In this post we’ll walk you through the best practices for getting setup cleanly from the start.
In this post we’ll show how to use AWS Batch, AWS Fargate and Amazon Event Bridge to create a job scheduling solution for containers that’s fully managed, serverless, and event-driven.
In this post we’ll show you how the NFL used AWS to scale their ML workloads and produce the first comprehensive dataset of helmet impacts across multiple NFL seasons. They were able to reduce manual labor by 90% and the results beats human labelers in accuracy by 12%!
Today we dive into details of AWS Batch fair share policies and show how they affect job placement. You’ll see the result of different share policies, and hear about practical use cases where you can benefit from fair share job queues in Batch.