Features of Kubeflow on 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 of autoscaling to scaleyour cluster horizontallyand vertically to meet the demands of machine learning (ML) workloads withlarge resource requirements.
• Kubeflow’s basic authentication service supports simple username/passwordaccess to your Kubeflow resources. Basic auth is an alternative to CloudIAP:
  • We recommend Cloud IAP for production and enterprise workloads.
  • Consider basic auth only when you want to test Kubeflow and use itwithout sensitive data.
• You can use GPUs and Cloud TPU toaccelerate your workload.

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