Tag: ML

AWS Batch multi-node parallel jobs can now run on Amazon EKS to provide gang scheduling of pods across nodes for large scale distributed computing like ML model training. More details here.

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.

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.

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.

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 […]

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.

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!

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.

ESG is now a boardroom issue. See how Databricks’ AI solution helps understand emissions data and meet new regulations.

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.