Features of Kubeflow on GCP

Reasons to use Kubeflow on Google Cloud Platform (GCP)

Running Kubeflow on GCP brings you the following features:

• You useDeployment Manager todeclaratively manage all non-Kubernetes resources (including the GKEcluster). Deployment Manager is easy to customize for your particular usecase.
• You can take advantage ofGKE autoscaling to scaleyour cluster horizontallyand vertically to meet the demands of machine learning (ML) workloads withlarge resource requirements.
• Cloud Identity-Aware Proxy (Cloud IAP)makes it easy to securely connect to Jupyter and otherweb apps running as part of Kubeflow.
• Stackdriver providespersistent logs to aid in debugging and troubleshooting.
• You can use GPUs and Cloud TPU toaccelerate your workload.

Next steps

• Deploy Kubeflow if you haven’t already done so.
• Run a full ML workflow on Kubeflow, using theend-to-end MNIST tutorial or theGitHub issue summarizationexample.

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Last modified 04.03.2020: Clarified that GCP basic auth is not supported and removed most references (#1765) (8703f266)