Category: AWS Batch

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 […]

In this post, I show how to integrate the AWS Batch CLI by AWS ParallelCluster with API Gateway.

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 contributed by Steve Kendrex, Senior Technical Product Manager, AWS Batch   Introduction   AWS offers a broad range of instances that are advantageous for batch workloads. The scale and provisioning speed of AWS’ compute instances allow you to get up and running at peak capacity in minutes without paying for downtime. Today, I’m […]

Leveraging Elastic Fabric Adapter to run HPC and ML Workloads on AWS Batch  This post is contributed by  Sean Smith, Software Development Engineer II, AWS ParallelCluster & Arya Hezarkhani, Software Development Engineer II, AWS Batch and HPC   On August 2, 2019, AWS Batch announced support for Elastic Fabric Adapter (EFA). This enables you to run highly […]

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 […]

This post is courtesy of Ryan Ulaszek, AWS Genomics Partner Solutions Architect and Aaron Friedman, AWS Healthcare and Life Sciences Partner Solutions Architect In 2017, we published a four part blog series on how to build a genomics workflow on AWS. In part 1, we introduced a general architecture highlighting three common layers: job, batch and […]

Contributed by Amr Ragab, HPC Application Consultant, AWS Professional Services and Aswin Damodar, Senior Software Development Engineer, AWS Batch At Supercomputing 2018 in Dallas, TX, AWS announced AWS Batch support for running tightly coupled workloads in a multi-node parallel jobs environment. This AWS Batch feature enables applications that require strong scaling for efficient computational workloads. Some of […]