Category: AWS Batch

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 […]

Contributed by W. Lee Pang and Emil Lerch, WWPS Professional Services DNA is often referred to as the “source code of life.” All living cells contain long chains of deoxyribonucleic acid that encode instructions on how they are constructed and behave in their surroundings. Genomics is the study of the structure and function of DNA […]

Contributed by Amr Ragab, HPC Application Consultant, AWS Professional Services AWS provides several managed services for file- and streaming-based media encoding options. Amazon Elastic Transcoder and AWS Elemental MediaConvert each provide several options for video/audio transcoding, optimized for various client devices. AWS Elemental Live provides online streaming-based encoding from edge camera devices. Through AWS Greengrass, edge […]

This post courtesy of Arya Hezarkhani. AWS CloudFormation allows developers and systems administrators to easily create and manage a collection of related AWS resources (called a CloudFormation stack) by provisioning and updating them in an orderly and predictable way. CloudFormation users can now deploy and manage AWS Batch resources in exactly the same way that […]

This post is courtesy of Aaron Friedman – Healthcare and Life Sciences Partner Solutions Architect at AWS and Angel Pizarro – Scientific Computing Technical Business Development Manager at AWS This post is the fourth in a series on how to build a genomics workflow on AWS. In Part 1, we introduced a general architecture, shown below, and […]

This post is courtesy of Aaron Friedman – Healthcare and Life Sciences Partner Solutions Architect at AWS and Angel Pizarro – Scientific Computing Technical Business Development Manager at AWS This post is the third in a series on how to build a genomics workflow on AWS. In Part 1, we introduced a general architecture, shown below, and […]

This post is courtesy of Aaron Friedman – Healthcare and Life Sciences Partner Solutions Architect at AWS and Angel Pizarro – Scientific Computing Technical Business Development Manager at AWS This post is the second in a series on how to build a genomics workflow on AWS. In Part 1, we introduced a general architecture, shown below, and […]

Thanks to my colleague Kiuk Chung for this great post on Deep Learning using AWS Batch. —- GPU instances naturally pair with deep learning as neural network algorithms can take advantage of their massive parallel processing power. AWS provides GPU instance families, such as g2 and p2, which allow customers to run scalable GPU workloads. You […]