Tag: AWS Batch

This post is written by Sumeeth Siriyur, Specialist Solutions Architect. AWS Outposts is a family of fully managed solutions that deliver AWS infrastructure and services to virtually any on-premises or edge location for a truly consistent hybrid experience. Outposts is ideal for workloads that need low latency access to on-premises applications or systems, local data […]

This post is written by Nikhil Anand, Solutions Architect  AWS Batch enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch processing jobs on AWS. With AWS Batch you no longer have to install and manage batch computing software or server clusters used to run your jobs. This lets you […]

This post was contributed by Clayton Thomas, Solutions Architect, AWS WW Public Sector SLG Govtech. Many AWS Batch users choose to store and consume their AWS Batch job container images on AWS using Amazon Elastic Container Registries (ECR). AWS Batch and Amazon Elastic Container Service (ECS) natively support pulling from Amazon ECR without any extra […]

This post is authored by Deepthi Chelupati, Senior Product Manager for Amazon EC2 Spot Instances, and Chad Schmutzer, Principal Developer Advocate for Amazon EC2 Customers have been using EC2 Spot Instances to save money and scale workloads to new levels for over a decade. Launched in late 2009, Spot Instances are spare Amazon EC2 compute […]

This post is contributed by Christian Kniep, Sr. Developer Advocate, HPC and AWS Batch. Scientists, researchers, and engineers are using AWS Batch to run workloads reliably at scale, and to offload the undifferentiated heavy lifting in their day-to-day work. But even with a slight chance of failure in the stack, the act of mitigating these […]

This post is contributed by Alberto Falzone, Consultant, HPC and Roberto Meda, Senior Consultant, HPC. High Performance Computing (HPC) workflows across industry verticals such as Design and Engineering, Oil and Gas, and Life Sciences often require GPU-based 3D/OpenGL rendering. Setting up drivers and applications for these types of workflows can require significant effort. Similar GPU […]

This post was written by Christian Kniep, Senior Developer Advocate for HPC and AWS Batch.  For HPC workloads, visibility into the logs of jobs is important to debug a job which failed, but also to have insights into a running job and track its trajectory to influence the configuration of the next job or terminate […]

This post is written by Sivasubramanian Ramani In many real work applications, you can use custom Docker images with AWS Batch and AWS CloudFormation to execute complex jobs efficiently.   This post provides a file processing implementation using Docker images and Amazon S3, AWS Lambda, Amazon DynamoDB, and AWS Batch. In this scenario, the user […]

This post is courtesy of Lee Pang – AWS Technical Business Development  I recently worked with a customer who needed to process a bunch of raw sequence files (FastQs) into Hi-C format (*.hic), which is used for the structural analysis of DNA/chromatin loops and sequence accessibility. The tooling they were interested in using was the Juicer […]

Contributed by Manuel Manzano Hoss, Cloud Support Engineer I remember playing around with graphics processing units (GPUs) workload examples in 2017 when the Deep Learning on AWS Batch post was published by my colleague Kiuk Chung. He provided an example of how to train a convolutional neural network (CNN), the LeNet architecture, to recognize handwritten digits […]