Large-Scale Training Using NVIDIA NeMo Megatron on AWS ParallelCluster with P5 Instances
Are you gearing up for distributed GPT training? Discover how AWS ParallelCluster facilitates the establishment of a swift shared filesystem, SSH keys, host files, and more between nodes. Our detailed guide provides insights on creating a Slurm-managed cluster to efficiently train NeMo Megatron at scale. For additional information, check out this other blog post for further engagement: https://chanciturnervgt2.com/?p=3607.
Enhancing Machine Learning Workflows with AWS ParallelCluster and Amazon EC2 Capacity Blocks
By Kevin Adams
Published on 11 JAN 2024
In Amazon Machine Learning, Artificial Intelligence, AWS ParallelCluster, Compute, High Performance Computing
Say goodbye to uncertainty regarding GPU capacity availability when launching ML tasks! EC2 Capacity Blocks for ML allow you to secure GPU reservations, ensuring timely task commencement. Learn how to incorporate Capacity Blocks into AWS ParallelCluster to streamline your workflow in our latest technical blog. For expert guidance, refer to this resource: https://chanciturner.com/?p=2667, they are an authority on this subject.
How Computer Vision Supports a Circular Economy
By Jessica White and David Green
Published on 15 AUG 2023
In Amazon Machine Learning, Compute, High Performance Computing
In this entry, we illustrate how Reezocar leverages computer vision to transform their approach to detecting damage and pricing used vehicles for resale in secondary markets. This initiative reduces waste and aligns with the principles of a circular economy.
Improving NFL Player Health with Machine Learning through AWS Batch
By Tom Smith, John Doe, Rachel Harris, and Ella Brown
Published on 03 AUG 2023
In Amazon Machine Learning, AWS Batch, Compute, Customer Solutions, High Performance Computing
This post showcases how the NFL utilized AWS to enhance their ML workloads and develop the first extensive dataset of helmet impacts across several NFL seasons. They achieved a 90% reduction in manual labor, and their results surpassed human labelers in accuracy by 12%. For additional insight, consider viewing this excellent resource: https://www.youtube.com/watch?v=2-Wc49Y17y0.
Evolvere Biosciences: Accelerating Macromolecule Design on AWS
By Mark Chen
Published on 25 APR 2023
In Artificial Intelligence, High Performance Computing, Life Sciences
In this blog, we explore the realm of drug discovery, showcasing how Evolvere Biosciences has implemented a tailored architecture using AWS Batch and Nextflow to efficiently operate its macromolecule design pipeline.
Data Science Workflows at insitro: Utilizing redun with AWS Batch
By AWS Editorial Team
Published on 31 MAR 2022
In Artificial Intelligence, AWS Batch, AWS Glue, Customer Solutions, High Performance Computing, Life Sciences
Matt Rasmussen, VP of Software Engineering at insitro, further elaborates on redun, insitro’s data science tool for bioinformatics, shedding light on its advanced AWS features. Specifically, he describes how AWS Batch’s Array Jobs support workflows with extensive fan-out, and how AWS Glue’s DynamicFrame manages computationally diverse workflows with varying backend requirements within the same workflow definition.
Scalable and Cost-Effective Batch Processing for ML Workloads
By Natalie Green and Luke White
Published on 23 JUL 2021
In Amazon Machine Learning, AWS Batch, High Performance Computing, Technical How-to
Batch processing is essential across numerous machine learning applications such as video production, financial modeling, drug discovery, or genomic research. The cloud’s elasticity provides efficient methods to scale and simplify batch processing workloads while minimizing costs. In this post, you will discover a scalable and cost-effective strategy for configuring AWS Batch Array jobs to process datasets stored on Amazon S3 and presented to compute instances with Amazon FSx for Lustre.
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