Charmed Kubeflow documentation

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

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

CKF meets the need of building ML applications in a structured and consistent manner while contributing to higher productivity and better collaboration within teams.

It is intended for data scientists and ML engineers, providing an advanced toolkit to organise and scale their work.


In this documentation

Tutorial
Get started - a hands-on introduction to CKF for newcomers
How-to guides
Step-by-step guides covering key operations and common tasks with CKF
Explanation
Discussion and clarification of key topics
Reference
Technical information, including specifications, APIs, settings and configuration

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.

Navigation

Navigation
Level Path Navlink
1 / Home
1 tutorial Tutorial
2 get-started-with-charmed-kubeflow Get started
2 explore-kubeflow Explore components
1 how-to How to
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 deploy-charmed-kubeflow-to-gke Deploy to GKE
3 manage-profiles Manage profiles
3 enable-istio-cni-plugin Enable Istio CNI plugin
3 enable-https-on-charmed-kubeflow Enable HTTPS on CKF
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
4 upgrade-18-19 Upgrade from 1.8 to 1.9
3 troubleshooting Troubleshoot
3 uninstall Uninstall
3 how-tosetup-ssh-vm-access-with-port-forwarding SSH VM Access
3 backup Backup Kubeflow Control Plane
3 restore Restore Kubeflow Control Plane
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 integrate-with-azure-blob-storage Integrate with Azure Blob Storage
3 integrate-with-azure-spot-virtual-machines Integrate with Azure spot virtual machines
3 integrate-identity-providers-with-charmed-kubeflow Integrate identity providers with Charmed Kubeflow
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
3 add-and-reorder-links-on-the-kubeflow-dashboard Add and reorder links on the Kubeflow Dashboard
3 configure-the-kubeflow-notebook-creation-page Configure the Kubeflow Notebook creation page
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 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
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 monitoring Monitoring
3 prometheus-metrics Prometheus metrics
3 prometheus-alerts Prometheus alerts
3 grafana-dashboards Grafana dashboards
3 loki-logs Loki logs
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
3 release-notes-1-9 Charmed Kubeflow 1.9
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

Redirects

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/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
/docs/enable-tls-ingress-gateway-for-a-single-host /docs/enable-tls-ingress-gateway-for-a-single-host