Charmed Kubeflow Documentation

Charmed Kubeflow is an open-source, end-to-end, production-ready MLOps platform on top of cloud native technologies.

Charmed Kubeflow translates Machine Learning steps into complete workflows, enabling training, tuning, and shipping of Machine Learning (ML) models. It enables automation of workflows, increases quality of models, and simplifies deployment of ML workloads into production in a reliable way.

Charmed Kubeflow meets the need of building ML applications in a structured and consistent manner while contributing to higher productivity and better collaboration in Data Science teams.

For Data Scientists and Machine Learning Engineers Charmed Kubeflow provides an advanced toolkit to organise and scale their work.

In this documentation

Learn how to deploy, debug and explore Kubeflow in this exciting sequence of tutorials
How-to guides
Navigate essential Kubeflow procedures like EKS installation
Deep-dive into the details of Kubeflow on topics like authorisation
Find technical information on things like authentication with Dex and supported versions

Project and community

Charmed Kubeflow is a member of the Ubuntu family. It’s an open-source project that welcomes community contributions, suggestions, fixes and constructive feedback.

Thinking about using Charmed Kubeflow for your next project? Get in touch!


Level Path Navlink
1 / Charmed Kubeflow
1 tutorial Tutorials
2 get-started-with-charmed-kubeflow Get started
2 explore-kubeflow Explore components
2 debugging-charmed-kubeflow Explore basic operations
1 how-to-guides How-to guides
2 manage-charmed-kubeflow Manage Charmed Kubeflow
3 install Install
3 install-in-airgapped-environment Install in airgapped environment
3 deploy-kubeflow-with-aws-cloud-formation Deploy to AWS with CloudFormation
3 install-on-microk8s-behind-a-web-proxy Deploy to Microk8s behind a web proxy
3 install-on-microk8s-on-aws Deploy to MicroK8s on AWS
3 install-on-nvidia-dgx Deploy to NVIDIA DGX
3 deploy-charmed-kubeflow-to-eks Deploy to EKS
3 deploy-charmed-kubeflow-to-aks Deploy to AKS
3 manage-profiles Manage profiles
3 customise Customize
3 enable-istio-cni-plugin Enable Istio CNI plugin
3 use-nvidia-gpus Use NVIDIA GPUs
3 upgrade Upgrade
4 databases-migration-guide MariaDB - MySQL Databases Migration
4 upgrade-14-16 Upgrade from 1.4 to 1.6
4 upgrade-16-17 Upgrade from 1.6 to 1.7
4 upgrade-17-171 Upgrade from 1.7 to 1.7 Patch 1
4 upgrade-17-18 Upgrade from 1.7 to 1.8
3 troubleshooting Troubleshoot
3 uninstall Uninstall
3 how-tosetup-ssh-vm-access-with-port-forwarding SSH VM Access
2 integrate-with-other-charms Integrate Charmed Kubeflow with other charms
3 integrate-with-mlflow Integrate with MLFlow
3 allow-access-minio Allow access to MinIO
3 integrate-with-cos Integrate with COS
2 integrate-with-external-tools Integrate Charmed Kubeflow with external tools
3 mindspore Integrate with Mindspore
3 ldap Set up LDAP Authentication
2 use-kubeflow Use Kubeflow
3 launch-ngc-notebooks Launch NGC Notebooks
3 serve-a-model-using-triton-inference-server Serve a model using Triton Inference Server
3 accelerated-ml-experiments-on-microk8s-with-inaccel-fpga-operator-and-kubeflow-katib Accelerated ML experiments on MicroK8s with InAccel FPGA Operator and Kubeflow Katib
3 aks-spot Use AKS spot instances for pipelines
3 build-an-mlops-pipeline-with-mlflow-seldon-core-and-kubeflow Build an MLOps pipeline with MLFlow
3 ml-workflow-kubeflow-with-katib-and-mlflow ML Workflow: Kubeflow with Katib and MLFlow
3 dynamically-configure-the-notebook-images-list Dynamically configure the Notebook images list
3 add-and-reorder-links-on-the-kubeflow-dashboard Add and reorder links on the Kubeflow Dashboard
1 reference Reference
2 authentication Authentication
2 aws-kubeflow-cloudformation-form AWS Kubeflow CloudFormation Form
2 kubeflow-bundle Kubeflow bundle Components
2 kubeflow-dashboard Kubeflow Dashboard
2 mlops-pipeline MLOps pipeline
3 list-of-mlops-tools List of MLOps tools
2 profile Profile
2 supported-versions Supported versions
2 release-notes Release notes
3 release-notes-1-7 Charmed Kubeflow 1.7
3 release-notes-1-8 Charmed Kubeflow 1.8
1 explanation Explanation
2 authorisation Authorisation
2 charmed-vs–upstream-kubeflow Charmed Vs. Upstream Kubeflow
contributing-docs Contributing to docs
create-eks-cluster-for-mlops Setup EKS before installing CKF
create-aks-cluster-for-mlops Setup AKS before installing CKF


Mapping table
Location Path
/docs/mlops-pipelines-with-mlflow-seldon-core-and-kubeflow /docs/build-an-mlops-pipeline-with-mlflow-seldon-core-and-kubeflow
/docs/quickstart /docs/get-started-with-charmed-kubeflow
/docs/kubeflow-basics /docs/get-started-with-charmed-kubeflow
/docs/aws /docs/install-on-microk8s-on-aws
/docs/authentication-with-oidc-and-keycloak /docs/how-to-guides
/docs/visual-workflow-design-with-charmed-kubeflow-and-elyra /docs/how-to-guides
/docs/run-spark-on-kubernetes /docs/how-to-guides
/docs/using-vscode-on-charmed-kubeflow /docs/how-to-guides
/docs/setting-up-remote-access /docs/how-to-guides
/docs/operators-and-bundles /docs/components
/docs/deploy-kubeflow-on-microk8s-behind-a-web-proxy /docs/prepare-to-install-on-microk8s-behind-a-web-proxy
/docs/nvidia-gpu-integration-outside-microk8s /docs/use-nvidia-gpus
/docs/components /docs/kubeflow-bundle
/docs/dashboard /docs/install
/docs/prepare-to-install-on-microk8s-behind-a-web-proxy /docs/install-on-microk8s-behind-a-web-proxy
/docs/enable-istio-cni-plugin /docs/enable-istio-cni-plugin