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 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 Machine Learning 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
|
Tutorial Get started - a hands-on introduction to Charmed Kubeflow for new users |
How-to guides Step-by-step guides covering key operations and common tasks |
|
Explanation Concepts - discussion and clarification of key topics |
Reference Technical information - specifications, APIs, architecture |
Project and community
Charmed Kubeflow is an official distribution of the Kubeflow project. It’s an open-source project that welcomes community contributions, suggestions, fixes and constructive feedback.
- Read our Code of Conduct
- Try out some Charmed Kubeflow projects
- Join the Discourse forum
- Contribute and report bugs
- Contribute to the documentation
Thinking about using Charmed Kubeflow for your next project? Get in touch!
Navigation
Navigation
| Level | Path | Navlink |
|---|---|---|
| 1 | / | Charmed Kubeflow |
| 1 | tutorial | Tutorial |
| 2 | get-started-with-charmed-kubeflow | Get started with Charmed Kubeflow |
| 1 | how-to-guides | How-to guides |
| 2 | manage-charmed-kubeflow | Manage Charmed Kubeflow |
| 3 | install | Install |
| 3 | deploy-kubeflow-with-aws-cloud-formation | Install on AWS with CloudFormation |
| 3 | install-on-microk8s-behind-a-web-proxy | Install on Microk8s behind a web proxy |
| 3 | install-on-microk8s-on-aws | Install on MicroK8s on AWS |
| 3 | install-on-nvidia-dgx | Install on NVIDIA DGX |
| 3 | manage-profiles | Manage profiles |
| 3 | customise | Customize |
| 3 | use-nvidia-gpus | Use NVIDIA GPUs |
| 3 | upgrade | Upgrade |
| 4 | upgrade-14-16 | Upgrade from 1.4 to 1.6 |
| 4 | upgrade-16-17 | Upgrade from 1.6 to 1.7 |
| 3 | troubleshooting | Troubleshoot |
| 3 | uninstall | Uninstall |
| 2 | integrate-with-other-charms | Integrate Charmed Kubeflow with other charms |
| 3 | integrate-with-mlflow | Integrate with MLFlow |
| 3 | integrate-observability-stack | Integrate with Canonical Observability Stack (COS) |
| 3 | allow-access-minio | Allow access to MinIO |
| 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 | 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, Seldon Core and Kubeflow |
| 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 |
| 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 |
| 1 | explanation | Explanation |
| 2 | authorisation | Authorisation |
| contributing-docs | Contributing to docs |
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/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 |