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Ray jobs on Amazon SageMaker HyperPod: scalable and resilient distributed AI
- Foundation model (FM) training and inference has increased computational needs in the industry, requiring efficient systems for distributing workloads and optimizing performance.
- Ray is an open source framework simplifying the creation, deployment, and optimization of distributed Python jobs, offering a unified programming model for seamless scaling.
- Ray's high-level APIs abstract complexities of distributed computing, emphasizing efficient task scheduling, fault tolerance, and automatic resource management.
- Amazon SageMaker HyperPod is purpose-built for large-scale FM development and deployment, offering resilience and optimal performance via same spine placement of instances.
- Combining Ray's efficiency with SageMaker HyperPod's resiliency provides a robust framework for scaling generative AI workloads.
- Ray clusters on SageMaker HyperPod consist of a head node orchestrating task scheduling and worker nodes executing distributed workloads.
- KubeRay facilitates running Ray clusters on Kubernetes, leveraging Amazon EKS for efficient allocation and fault tolerance.
- RayCluster, RayJob, and RayService in KubeRay operator provide resources for managing, submitting, and deploying Ray applications on Kubernetes clusters.
- Creating a persistent Ray cluster on SageMaker HyperPod enables enhanced resiliency, auto-resume capabilities, and seamless recovery from node failures for distributed ML training jobs.
- SageMaker HyperPod's built-in resiliency features, such as agent-based health checks, offer infrastructure stability for large-scale AI workloads training and inference.
- Implementation steps for running Ray jobs on SageMaker HyperPod include setting up Ray clusters, creating shared file systems, installing operators, and deploying training jobs.
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