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
- 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.
- 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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Last modified 02.05.2019: Tech writer refactor of GCP deployment docs. (#676) (10135e0a)