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.
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Last modified 08.04.2019: Fixes up the GKE deployment and deletion guides. (#606) (85b33663)