Features of Kubeflow on GCP

Reasons to use Kubeflow on Google Cloud Platform (GCP)

Running Kubeflow on GCP has the following benefits:

• The Cloud Native Resource Manager to declaratively manage all non-Kubernetes resources (including the GKE cluster).
• You can take advantage of GKE’s Cluster Autoscaler to automatically resize the number of nodes in a node pool in your cluster depending on the workload demands.
• 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.

Next steps

• 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.

Last modified 27.07.2020: Address comments. (27c4adf1)