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.
- 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.
Last modified 04.03.2020: Clarified that GCP basic auth is not supported and removed most references (#1765) (8703f266)