From the course: AWS Certified Machine Learning Engineer - Associate (MLA-C02) Cert Prep

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Amazon SageMaker HyperPod

Amazon SageMaker HyperPod

From the course: AWS Certified Machine Learning Engineer - Associate (MLA-C02) Cert Prep

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Amazon SageMaker HyperPod

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You're training a large language model with 70 billion parameters. You've got a cluster of 256 GPUs. Training will take 3 weeks. On day 12, one GPU fails. Your entire training job crashes. You restart from the last checkpoint. Six hours of work lost. On day 14, another failure. More lost time. More money burned. This is the reality of large-scale AI training. What if your cluster could automatically detect failures, replace faulty nodes, and resume training without you lifting a finger? That's Amazon SageMaker HyperPod. Today we're exploring the infrastructure that makes large-scale AI training actually manageable. Let's understand the problem first. Training large AI models requires hundreds or thousands of GPUs running together for weeks or months. But hardware fails. At scale, failures are guaranteed. Studies show that large GPU clusters experience failures every few days. Manual recovery wastes hours and thousands of dollars in idle compute time. Enter SageMaker HyperPod…

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