Kubeflow Edge

  • By Kubeflow Charmers
juju deploy kubeflow-edge
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You will need Juju 2.9 to be able to run this command. Learn how to upgrade to Juju 2.9.
Channel Version Revision Published Runs on
latest/stable 46 46 28 Jul 2021
latest/candidate 46 46 28 Jul 2021
latest/beta 46 46 28 Jul 2021
latest/edge 46 46 28 Jul 2021


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Kubeflow Operators


Charmed Kubeflow is the full set Kubernetes operators to deliver the 30+ applications and services that make up the latest version of Kubeflow, for easy operations anywhere, from workstations to on-prem, to public cloud and edge.

A charm is a software package that includes an operator together with metadata that supports the integration of many operators in a coherent aggregated system. The individual charms that make up Charmed Kubeflow can be found under charms/.

This technology leverages the Juju Operator Lifecycle Manager to provide day-0 to day-2 operations of Kubeflow.

Visit charmed-kubeflow.io for more.


There are two possible paths, depending on your choice of Kubernetes:

  1. For any Kubernetes, follow the installation instructions.
  2. On MicroK8s, you simply have to enable the Kubeflow add-on.


Read the official documentation.

Usage details

Argo UI

You can view pipelines from the Pipeline Dashboard available on the central dashboard, or by going to /argo/.


Pipelines are available either by the main dashboard, or from within notebooks via the fairing library.

Note that until https://github.com/kubeflow/pipelines/issues/1654 is resolved, you will have to attach volumes to any locations that output artifacts are written to, see the attach_output_volume function in pipline-samples/sequential.py for an example.

TensorFlow Jobs

To submit a TensorFlow job to the dashboard, you can run this kubectl command:

kubectl create -n <NAMESPACE> -f path/to/job/definition.yaml

Where <NAMESPACE> matches the name of the Juju model that you're using, and path/to/job/definition.yaml should point to a TFJob definition similar to the mnist.yaml example found here.

TensorFlow Serving

See Charmed TF-serving


Follow the official uninstall documentation.


For information on how to run the tests in this repo, see the tests README.