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Kubernetes manifests for deploying end-to-end ML platforms
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This repository provides community-maintained Kubernetes manifests for deploying the Kubeflow platform. It targets users and engineers seeking to explore and utilize Kubeflow's end-to-end machine learning capabilities across various Kubernetes environments, simplifying complex deployments.
How It Works
The project leverages kustomize
to manage and deploy a comprehensive set of Kubeflow components and common services. Manifests are organized into applications
(official components), common
(dependencies like Istio, Cert-Manager), and experimental
modules. It synchronizes upstream component versions, allowing for either a full platform installation or granular component selection, facilitating customized Kubeflow distributions.
Quick Start & Requirements
while ! kustomize build example | kubectl apply --server-side --force-conflicts -f -; do echo "Retrying to apply resources"; sleep 20; done
kustomize
v5.4.3+, kubectl
, kind
v0.27+, Docker/Podman, and potential Linux kernel tuning for fs.inotify
.Highlighted Details
Maintenance & Community
The project is managed by the Platform/Manifests/security Working Group, with active community engagement on the CNCF Slack channel #kubeflow-platform
and biweekly meetings. Contribution guidelines are detailed in CONTRIBUTING.md
.
Licensing & Compatibility
The repository's license is not explicitly stated in the provided README content. It is designed for Kubernetes environments and specifies compatibility with major cloud provider managed Kubernetes services.
Limitations & Caveats
The master branch is intended for exploration and feedback; stable releases are recommended for production. Installation commands may require retries due to Kubernetes CRD eventual consistency. Default user credentials must be updated for security-sensitive deployments, and some features like Workspaces 2.0 are still under development.
1 day ago
Inactive