Version v0.7 of the documentation is no longer actively maintained. The site that you are currently viewing is an archived snapshot. For up-to-date documentation, see the latest version.
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
- 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.
Was this page helpful?
Glad to hear it! Please tell us how we can improve.
Sorry to hear that. Please tell us how we can improve.