Amazon SageMaker HyperPod helps build, train and deploy AI models at scale. You can spin-up preferred IDE on HyperPod to develop their models and run experiments, then scale to thousands of accelerators to run distributed training and finally deploy models for real-time or batch inference. With HyperPod task governance capabilities, you can optimize the utilization of the cluster resources. In this talk, we will show how interaction with HyperPod clusters is simplified via the SageMaker HyperPod CLI and SDK, which streamlined end-to-end model development, from training, fine-tuning to deployment. We'll show HyperPod CLI/SDK capabilities with live coding and troubleshooting.

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