In this post, we demonstrate how Kubeflow on AWS (an AWS-specific distribution of Kubeflow) used with AWS Deep Learning Containers and Amazon Elastic File System (Amazon EFS) simplifies collaboration and provides flexibility in training deep learning models at scale on both Amazon Elastic Kubernetes Service (Amazon EKS) and Amazon SageMaker utilizing a hybrid architecture approach.
Machine learning (ML) development relies on complex and continuously evolving open-source frameworks and toolkits, as well as complex and continuously evolving hardware ecosystems. This poses a challenge when scaling out ML development to a cluster. Containers offer a solution, because they can fully encapsulate not just the training code, but the entire dependency stack down to the hardware libraries. This ensures an ML environment that is consistent and portable, and facilitates reproducibility of the training environment on each individual node of the training cluster.
Kubernetes is a widely adopted system for automating infrastructure deployment, resource

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