KubeFlow
Warning
Deploying KubeFlow requires an existing Kubernetes cluster containing a node group with at least 16GB+ of RAM and 12+ CPUs, and ideally with a GPU.
Deployment takes a long time as many large images need to be pulled. Progress can be followed via the Kubernetes Dashboard in the Workloads tab of the Kubeflow namespace, or via kubectl.
Introduction
KubeFlow is a machine learning toolkit for Kubernetes clusters, using Jupyter Notebooks and TensorFlow.
For an introduction to using KubeFlow, see the official documentation.
Warning
The KubeFlow app deployment is currently at a proof-of-concept stage and does not yet provide full integration with Azimuth's standard authentication and access management features. Full integration with the Azimuth identity provider is planned for a future release.
Launch configuration
To get started, in the Platforms tab, press the 
New Platform button, and select KubeFlow.
KubeFlow requires a worker node cluster with 16GB+ of RAM and 12+ CPUs. Ideally, it should be a GPU flavor.
You will then be presented with launch configuration options to fill in:
| Option | Explanation |
|---|---|
| Platform name | A name to identify the KubeFlow platform |
| Kubernetes cluster | The Kubernetes platform on which to deploy KubeFlow. If one hasn't already been created, check out the Kubernetes Overview. |
| App version | The version of the KubeFlow Azimuth Application to use. |
Accessing KubeFlow
The default login credentials for KubeFlow are:
- username: user@example.com
- password: 12341234