Category: AWS Batch

In this post, we discuss how Novo Nordisk approached the deployment of a scale-out HPC platform for running AlphaFold, while meeting their enterprise IT requirements and keeping the user experience simple.

Complicated multi-step workflows can be challenging to deploy, especially when using a variety of high-compute resources. Covalent is an open-source orchestration tool that streamlines the deployment of distributed workloads on AWS resources. In this post, we outline key concepts in Covalent and develop a machine learning workflow for AWS Batch in just a handful of steps.

Oxford Nanopore sequencers enables direct, real-time analysis of long DNA or RNA fragments. They work by monitoring changes to an electrical current as nucleic acids are passed through a protein nanopore. The resulting signal is decoded to provide the specific DNA or RNA sequence by virtue of  compute-intensive algorithms called basecallers. This blog post presents the benchmarking results for two of those Oxford Nanopore basecallers — Guppy and Dorado — on AWS. This benchmarking project was conducted in collaboration between G42 Healthcare, Oxford Nanopore Technologies and AWS.

Many applications leverage distributed task systems like Celery to handle asynchronous work. In this post, we describe how to handle compute-intensive Celery tasks using AWS Batch to scale the compute resources and run worker agents.

Today we show you how to get insights into the costs of running AWS Batch workloads on Amazon EKS using Kubernetes pod labels with Kubecost.

In this post, AWS Professional Services highlights how they helped data scientists from Amazon Logistics rearchitect their algorithm for improving the efficiency of their supply-chain by making better planning decisions. Leveraging best practices for deploying scalable HPC applications on AWS, the teams saw a 4X improvement in run time.

AWS Batch customers often ask for guidance to optimize their architectures and make their workload to scale rapidly. Here we describe an observability solution that provides insights into your AWS Batch architectures and allows you to optimize them for scale and quickly identify potential throughput bottlenecks for jobs and instances.

Since launch, EFA has seen continuous improvements in performance. In this post, we talk about our 2nd generation of EFA, which takes another step in improving Machine Learning and High Performance Computing in the Cloud.

Pay-as-you-go resources are a compelling but budget-limited researchers performing HPC workloads need help working within the bounds of their grants. In this post, we show how to build a real-time cost guardian for AWS Batch to help enforce those limits.

Today, we discuss AWS batch on Amazon EKS, and the initial motivation and design choices the team made when we developed the service, and some of the challenges to overcome.