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Features of Kubeflow on GCP

Reasons to use Kubeflow on Google Cloud Platform (GCP)

Running Kubeflow on GCP brings you the following features:

  • You use Deployment Manager to declaratively manage all non-Kubernetes resources (including the GKE cluster). Deployment Manager is easy to customize for your particular use case.
  • You can take advantage of GKE autoscaling to scale your cluster horizontally and vertically to meet the demands of machine learning (ML) workloads with large resource requirements.
  • Cloud Identity-Aware Proxy (Cloud IAP) makes it easy to securely connect to Jupyter and other web apps running as part of Kubeflow.
  • Kubeflow’s basic authentication service supports simple username/password access to your Kubeflow resources. Basic auth is an alternative to Cloud IAP:
    • We recommend Cloud IAP for production and enterprise workloads.
    • Consider basic auth only when you want to test Kubeflow and use it without sensitive data.
  • Stackdriver provides persistent logs to aid in debugging and troubleshooting.
  • You can use GPUs and Cloud TPU to accelerate your workload.

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