AWS HPC Blog
Tag: ML
Large scale training with NVIDIA NeMo Megatron on AWS ParallelCluster using P5 instances
Launching distributed GPT training? See how AWS ParallelCluster sets up a fast shared filesystem, SSH keys, host files, and more between nodes. Our guide has the details for creating a Slurm-managed cluster to train NeMo Megatron at scale.
Building an AI simulation assistant with agentic workflows
Simulations provide critical insights but running them takes specialized people, which can slow everyone down. We show how a Simulation Assistant can use LLMs and agents to start these workflows via chat so you can get results sooner.
Using machine learning to drive faster automotive design cycles
Aerospace and automotive companies are speeding up their product design using AI. In this post we’ll discuss how they’re using machine learning to shift design cycles from hours to seconds using surrogate models.
Accelerate drug discovery with NVIDIA BioNeMo Framework on Amazon EKS
This post was contributed by Doruk Ozturk and Ankur Srivastava at AWS, and Neel Patel at NVIDIA. Introduction Drug discovery is a long and expensive process. Pharmaceutical companies must sift through thousands of compound possibilities to find potential new drugs to treat diseases. This process takes multiple years and costs billions of dollars, with the […]
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.
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.
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.
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.