AWS HPC Blog
Author: Pierre-Yves Aquilanti
Pierre-Yves Aquilanti is Head of Frameworks ML Solutions at Amazon Web Services where he helps develop the industry’s best cloud based ML Frameworks solutions. His background is in High Performance Computing and prior to joining AWS, Pierre-Yves was working in the Oil & Gas industry. Pierre-Yves is originally from France and holds a Ph.D. in Computer Science from the University of Lille.
Optimizing your AWS Batch architecture for scale with observability dashboards
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.
Bayesian ML Models at Scale with AWS Batch
Ampersand is a data-driven TV advertising technology company that provides aggregated TV audience impression insights and planning on 42 million households, in every media market, across more than 165 networks and apps and in all dayparts (broadcast day segments). The Ampersand Data Science team estimated that building their statistical models would require up to 600,000 physical CPU hours to run, which would not be feasible without using a massively parallel and large-scale architecture in the cloud. AWS Batch enabled Ampersand to compress their time of computation over 500x through massive scaling while optimizing their costs using Amazon EC2 Spot. In this blog post, we will provide an overview of how Ampersand built their TV audience impressions (“impressions”) models at scale on AWS, review the architecture they have been using, and discuss optimizations they conducted to run their workload efficiently on AWS Batch.
AWS Batch Dos and Don’ts: Best Practices in a Nutshell
AWS Batch is a service that enables scientists and engineers to run computational workloads at virtually any scale without requiring them to manage a complex architecture. In this blog post, we share a set of best practices and practical guidance devised from our experience working with customers in running and optimizing their computational workloads. The readers will learn how to optimize their costs with Amazon EC2 Spot on AWS Batch, how to troubleshoot their architecture should an issue arise and how to tune their architecture and containers layout to run at scale.