Reasons to Use Kubeflow on GCP

Reasons to deploy Kubeflow on GCP

Running Kubeflow on GKE brings the following advantages:

  • 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.
  • Basic auth service supports simple username/password access to your Kubeflow. It is an alternative to Cloud IAP service:
    • We recommend IAP for production and enterprise workloads.
    • Consider basic auth only when trying to test out Kubeflow and use it without sensitive data.
  • Stackdriver makes it easy to persist logs to aid in debugging and troubleshooting
  • You can use GPUs and TPUs to accelerate your workload.