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:
- For any Kubernetes, follow the installation instructions.
- On MicroK8s, you simply have to enable the Kubeflow add-on.
Read the official documentation.
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 kubeflow/pipelines#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.
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.
Follow the official uninstall documentation.
For information on how to run the tests in this repo, see the tests README.