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.
- 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 27.07.2020: Address comments. (27c4adf1)