In this post we’ll show you how the NFL used AWS to scale their ML workloads and produce the first comprehensive dataset of helmet impacts across multiple NFL seasons. They were able to reduce manual labor by 90% and the results beats human …
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In this post we show how physics-inspired graph neural networks can be used to solve the notoriously hard graph-coloring problem, at scale. This can help in an huge number of familiar resource-allocation problems from sports to rental cars. …
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Today’s demanding workloads such as database, rendering farm, analytics and ML workloads, have increasingly demanding IO requirements. These workloads need a reliable storage infrastructure that provides sufficient storage capacity, IOPS, …
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Lustre is a high-performance parallel file system commonly used in workloads requiring throughput up to hundreds of GB/s and sub-millisecond per-operation latencies, such as machine learning (ML), high performance computing (HPC), video …
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Complicated multi-step workflows can be challenging to deploy, especially when using a variety of high-compute resources. Covalent is an open-source orchestration tool that streamlines the deployment of distributed workloads on AWS …
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AWS ParallelCluster 3.6.0 can now detect GPU failures in HPC and AI/ML tasks. Health checks run at the start of Slurm jobs and if they fail, the job is requeued on another instance. This can increase reliability and prevent wasted spend. …
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Amazon SageMaker provides a broad selection of machine learning (ML) infrastructure and model deployment options to help meet your ML inference needs. It’s a fully-managed service and integrates with MLOps tools so you can work to scale …
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New generations of CPUs offer a significant performance improvement in machine learning (ML) inference due to specialized built-in instructions. Combined with their flexibility, high speed of development, and low operating cost, these …
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