AWS HPC Blog
Tag: Machine Learning
Second generation EFA: improving HPC and ML application performance in the cloud
Since launch, EFA has seen continuous improvements in performance. In this post, we talk about our 2nd generation of EFA, which takes another step in improving Machine Learning and High Performance Computing in the Cloud.
Launch self-supervised training jobs in the cloud with AWS ParallelCluster
In this post we describe the process to launch large, self-supervised training jobs using AWS ParallelCluster and Facebook’s Vision Self-Supervised Learning (VISSL) library.
Building a Scalable Predictive Modeling Framework in AWS – Part 3
In this final part of this three-part blog series on building predictive models at scale in AWS, we will use the synthetic dataset and the models generated in the previous post to showcase the model updating and sensitivity analysis capabilities of the aws-do-pm framework.
Building a Scalable Predictive Modeling Framework in AWS – Part 2
In the first part of this three-part blog series, we introduced the aws-do-pm framework for building predictive models at scale in AWS. In this blog, we showcase a sample application for predicting the life of batteries in a fleet of electric vehicles, using the aws-do-pm framework.
Building a Scalable Predictive Modeling Framework in AWS – Part 1
Predictive models have powered the design and analysis of real-world systems such as jet engines, automobiles, and powerplants for decades. These models are used to provide insights on system performance and to run simulations, at a fraction of the cost compared to experiments with physical hardware. In this first post of three, we described the motivation and general architecture of the open-source aws-do-pm framework project for building predictive models at scale in AWS.