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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In this blog post, we show how you can track and manage hybrid quantum-classical experiments with the Hybrid Jobs feature in Amazon Braket and Amazon SageMaker Experiments, used by ML developers on AWS to organize their experiments and …
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Deep learning (DL) is a fast-evolving field, and practitioners are constantly innovating DL models and inventing ways to speed them up. Custom operators are one of the mechanisms developers use to push the boundaries of DL innovation by …
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Introduction Quantum computers hold the promise of driving novel approaches to solving complex problems across multiple fields, including optimization, machine learning, and the simulation of physical systems. Researchers are already using …
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Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). It provides a single, web-based visual interface where you can perform all ML development steps, including preparing data and …
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To build machine learning models, machine learning engineers need to develop a data transformation pipeline to prepare the data. The process of designing this pipeline is time-consuming and requires a cross-team collaboration between …
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