AWS HPC Blog
Tag: Machine Learning
Choosing the right compute orchestration tool for your research workload
Running big research jobs on AWS but not sure where to start? We break down options like Batch, ECS, EKS, and others to pick the right tool for your needs. Lots of examples for genomics, ML, engineering, and more!
Protein language model training with NVIDIA BioNeMo framework on AWS ParallelCluster
In this new post, we discuss pre-training ESM-1nv for protein language modeling with NVIDIA BioNeMo on AWS. Learn how you can efficiently deploy and customize generative models like ESM-1nv on GPU clusters with ParallelCluster. Whether you’re studying protein sequences, predicting properties, or discovering new therapeutics, this post has tips to accelerate your protein AI workloads on the cloud.
Introducing new alerts to help users detect and react to blocked job queues in AWS Batch
Heads up AWS Batch users! Learn how to get notifications when your job queue gets blocked so you can quickly troubleshoot and keep your workflows moving. Details in our blog.
Using large-language models for ESG sentiment analysis using Databricks on AWS
ESG is now a boardroom issue. See how Databricks’ AI solution helps understand emissions data and meet new regulations.
Amazon’s renewable energy forecasting: continuous delivery with Jupyter Notebooks
Interested in eliminating friction between data science and engineering teams? Read this post to learn how Amazon successfully transitioned Jupyter Notebooks from the lab to production.
Leveraging Seqera Platform on AWS Batch for machine learning workflows – Part 2 of 2
In this second part of using Nextflow for machine learning for life science workloads, we provide a step-by-step guide, explaining how you can easily deploy a Seqera environment on AWS to run ML and other pipelines.
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.
EFA: how fixing one thing, led to an improvement for … everyone
Today, we’re diving deep into the open-source frameworks that move MPI messages around, and showing you how work we did in the Open MPI and libfabrics community lead to an improvement for EFA users – and everyone else, too.
Conceptual design using generative AI and CFD simulations on AWS
In this post we’ll show how generative AI, combined with conventional physics-based CFD can create a rapid design process to explore new design concepts in automotive and aerospace from just a single image.
How Amazon’s Search M5 team optimizes compute resources and cost with fair-share scheduling on AWS Batch
In this post, we share how Amazon Search optimizes their use of accelerated compute resources using AWS Batch fair-share scheduling to schedule distributed deep learning workloads.