Features of Kubeflow on 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.

Next steps

• Deploy Kubeflow if you haven’t already done so.
• Run a full ML workflow on Kubeflow, using the end-to-end MNIST tutorial or the GitHub issue summarization example.

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