AWS HPC Blog

Leveraging Seqera Platform on AWS Batch for machine learning workflows - Part 1 of 2

Leveraging Seqera Platform on AWS Batch for machine learning workflows – Part 1 of 2

Nextflow is popular workflow framework for genomics pipelines, but did you know you can also use it for machine-learning? ML is already being used for medical imaging, protein folding, drug discovery, and gene editing. In this post, we explain how to build an example Nextflow pipeline that performs ML model-training and inference for image analysis.

Enhancing ML workflows with AWS ParallelCluster and Amazon EC2 Capacity Blocks for ML

Enhancing ML workflows with AWS ParallelCluster and Amazon EC2 Capacity Blocks for ML

No more guessing if GPU capacity will be available when you launch ML jobs! EC2 Capacity Blocks for ML let you lock in GPU reservations so you can start tasks on time. Learn how to integrate Caacity Blocks into AWS ParallelCluster to optimize your workflow in our latest technical blog post.

Create a Slurm cluster for semiconductor design with AWS ParallelCluster

Create a Slurm cluster for semiconductor design with AWS ParallelCluster

If you work in the semiconductor industry with electronic design automation tools and workflows, this guide will help you build an HPC cluster on AWS specifically configured for your needs. It covers AWS ParallelCluster and customizations specifically to cater to EDA.

Using a Level 4 Digital Twin for scenario analysis and risk assessment of manufacturing production on AWS

Using a Level 4 Digital Twin for scenario analysis and risk assessment of manufacturing production on AWS

This post was contributed by Orang Vahid (Dir of Engineering Services) and Kayla Rossi (Application Engineer) at Maplesoft, and Ross Pivovar (Solution Architect) and Adam Rasheed (Snr Manager) from Autonomous Computing at AWS One of the most common objectives for our Digital Twin (DT) customers is to use DTs for scenario analysis to assess risk […]

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