Monitoring ML Workflows using Kubeflow on Amazon EKS #202
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vara-bonthu
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May 17, 2023
Thankyou @vara-bonthu |
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What does this PR do?
馃洃 Please open an issue first to discuss any significant work and flesh out details/direction - we would hate for your time to be wasted.
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As part of day 2 operations, customers want to monitor their Infrastructure, Amazon EKS clusters and application components. AWS customers use Amazon EKS to run machine learning workloads. Containerization allows machine learning engineers to package and distribute models easily, while Kubernetes helps in deploying, scaling, and improving. In addition to monitoring the behavior of the Amazon EKS clusters, it鈥檚 essential to monitor the behavior of machine learning workflows as well to ensure the operational resilience of workloads and platforms run by an organization.
Kubeflow is the open-source machine learning (ML) platform dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Kubeflow provides many components, including a central dashboard, multi-user Jupyter notebooks, Kubeflow Pipelines, KFServing, and Katib, as well as distributed training operators for TensorFlow, PyTorch, MXNet, and XGBoost. Kubeflow components export metrics which provides insights into the health and function of Kubeflow on Amazon Elastic Kubernetes Service (EKS).
Motivation
Idea on Monitoring ML Workflows using Kubeflow on ELS
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website/docsorwebsite/blogsection for this featurepre-commit run -awith this PR. Link for installing pre-commit locallyFor Moderators
Additional Notes