Category: AWS Batch

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.

Today we are excited to announce that all 9000+ applications provided by the BioContainers community are available within ECR Public Gallery! You don’t need an AWS account to access these images, but having one allows many more pulls to the internet, and unmetered usage within AWS. If you perform any sort of bioinformatics analysis on AWS, you should check it out!

In this post, we describe how to orchestrate protein folding jobs on AWS Batch. We also compare the performance of OpenFold and AlphaFold on a set of public targets. Finally, we will discuss how to optimize your protein folding costs.

AWS service teams continuously improve the underlying infrastructure and operations of managed services, and AWS Batch is no exception. The AWS Batch team recently moved most of their job scheduler fleet to a serverless infrastructure model leveraging AWS Fargate. I had a chance to sit with Devendra Chavan, Senior Software Development Engineer on the AWS Batch team, to discuss the move to AWS Fargate and its impact on the Batch managed scheduler service component.

In this blog post, we demonstrate how to leverage the AWS Genomics Command line and Amazon SageMaker to analyze large-scale exome sequences and derive meaningful insights. We use the bioinformatics workflow manager Nextflow, it’s open source library of pipelines, NF-Core, and AWS Batch.

This blog post explains how to run parallel rendering workloads and produce an animation in a cost and time effective way using AWS Batch and AWS Step Functions. AWS Batch manages the rendering jobs on Amazon Elastic Compute Cloud (Amazon EC2), and AWS Step Functions coordinates the dependencies across the individual steps of the rendering workflow. Additionally, Amazon EC2 Spot instances can be used to reduce compute costs by up to 90% compared to On-Demand prices.