AWS HPC Blog
Tag: Machine Learning
Optimizing MPI application performance on hpc7a by effectively using both EFA devices
Get the inside scoop on optimizing your MPI apps and configuration for AWS’s powerful new Hpc7a instances. Dual rail gives these instances huge networking potential @ 300 Gb/s – if properly used. This post provides benchmarks, sample configs, and real speedup numbers to help you maximize network performance. Whether you run weather simulations, CFD, or other HPC workloads, you’ll find practical tips for your codes.
Run simulations using multiple containers in a single AWS Batch job
Run simulations using multiple containers in a single AWS Batch job Matthew Hansen, Principal Solutions Architect, AWS Advanced Computing & Simulation Recently, AWS Batch launched a new feature that makes it possible to run multiple containers within a single job. This enables new scenarios customers have asked about like simulations for autonomous vehicles, multi-robot collaboration, […]
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.