Tag: AWS Batch

In this post we explore SeatGeek’s load testing system that simulates 50k simultaneous users. Originally built to prep SeatGeek for large-event traffic spikes, it now runs weekly to help them harden their code.

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.

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.

In this blog post, we catch a glimpse into drug discovery to see how Evolvere Biosciences has deployed a customized architecture w/ AWS Batch and Nextflow to quickly and easily run its macromolecule design pipeline.

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.

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 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.