KubeFlow
Warning
Deploying KubeFlow requires an existing Kubernetes cluster, with 16GB+ of RAM and 12+ CPUs, ideally with a GPU-enabled node group.
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