mirror of https://github.com/kubeflow/website.git
redirect distribution-specific docs to external websites (#3572)
* remove distributions section (redirect to install kf page) Signed-off-by: Mathew Wicks <thesuperzapper@users.noreply.github.com> * remove circular aws redirect Signed-off-by: Mathew Wicks <thesuperzapper@users.noreply.github.com> * redirect distribution pages to external websites Signed-off-by: Mathew Wicks <thesuperzapper@users.noreply.github.com> * create sidebar link to "installing kubeflow" distribution list Signed-off-by: Mathew Wicks <5735406+thesuperzapper@users.noreply.github.com> --------- Signed-off-by: Mathew Wicks <thesuperzapper@users.noreply.github.com> Signed-off-by: Mathew Wicks <5735406+thesuperzapper@users.noreply.github.com> Co-authored-by: Mathew Wicks <thesuperzapper@users.noreply.github.com>
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Mathew Wicks
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@ -195,7 +195,6 @@ docs/started/requirements/ /docs/started/getting-started/
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/docs/guides/* /docs/:splat
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/docs/pipelines/concepts/* /docs/components/pipelines/overview/concepts/:splat
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/docs/pipelines/* /docs/components/pipelines/:splat
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/docs/distributions/aws/* /docs/aws/
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/docs/aws/* /docs/distributions/aws/
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/docs/azure/* /docs/distributions/azure/:splat
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/docs/gke/* /docs/distributions/gke/:splat
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@ -205,6 +204,12 @@ docs/started/requirements/ /docs/started/getting-started/
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/docs/components/istio/* /docs/external-add-ons/istio/:splat
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/docs/components/feature-store/* /docs/external-add-ons/feature-store/:splat
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/docs/components/serving/* /docs/external-add-ons/serving/:splat
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# redirect distribution pages to external websites
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/docs/distributions/aws/* https://awslabs.github.io/kubeflow-manifests/
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/docs/distributions/azure/* https://azure.github.io/kubeflow-aks/main/
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/docs/distributions/charmed/* https://charmed-kubeflow.io/
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/docs/distributions/ekf/* https://www.arrikto.com/enterprise-kubeflow/
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/docs/distributions/gke/* https://googlecloudplatform.github.io/kubeflow-gke-docs/docs/:splat
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/docs/distributions/ibm/* https://ibm.github.io/manifests/
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/docs/distributions/ibm/* https://ibm.github.io/manifests/
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/docs/distributions/nutanix/* https://nutanix.github.io/kubeflow-manifests/
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@ -1,5 +0,0 @@
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approvers:
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- RFMVasconcelos
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reviewers:
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- 8bitmp3
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@ -1,5 +1,5 @@
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title = "Distributions"
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description = "A list of available Kubeflow distributions"
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description = "Distributions of Kubeflow"
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weight = 40
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@ -1,5 +0,0 @@
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approvers:
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- surajkota
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- mbaijal
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- akartsky
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@ -1,9 +0,0 @@
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title = "Kubeflow on AWS"
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description = "Running Kubeflow on Amazon EKS and Amazon Web Services"
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weight = 20
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[Kubeflow on AWS](https://awslabs.github.io/kubeflow-manifests/) is an open source distribution of Kubeflow that allows customers to build machine learning systems with ready-made AWS service integrations. Use Kubeflow on AWS to streamline data science tasks and build highly reliable, secure, and scalable machine learning systems with reduced operational overheads.
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For more information, see the [Kubeflow on AWS documentation](https://awslabs.github.io/kubeflow-manifests/docs/).
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title = "Kubeflow on Azure"
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description = "Running Kubeflow on Azure Kubernetes Service (AKS)"
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weight = 20
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[Kubeflow on AKS](https://azure.github.io/kubeflow-aks/main/) is an open source distribution of the Kubeflow manifests.
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For more information, see the [Kubeflow on AKS documentation](https://azure.github.io/kubeflow-aks/main/docs/).
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@ -1,6 +0,0 @@
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approvers:
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- grobbie
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- DomFleischmann
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reviewers:
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- ca-scribner
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- DnPlas
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title = "Charmed Kubeflow from Canonical"
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description = "A production-ready, free-to-use, Kubeflow distribution from Canonical for easy consumption anywhere, from workstations to on-prem, public cloud and edge."
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weight = 50
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[Charmed Kubeflow](https://charmed-kubeflow.io/) from [Canonical](https://www.canonical.com/) delivers a powerful, sophisticated end-to-end MLOps platform which you can deploy using any conformant Kubernetes distribution. The solution enables optimised AI training and modelling, in a robust and automated way, allowing data scientists to focus on AI/ML projects, instead of underlying infrastructure. The enterprise-ready platform is available backed with 24/7 support, expert set-up services, and managed services with a service level agreement (SLA). With a growing ecosystem of powerful extensions and integrations including MLFlow and Seldon, the Charmed Kubeflow solution amplifies productivity levels for data scientists and machine learning engineers working with advanced analytics and AI.
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Charmed Kubeflow is Canonical’s version of the upstream Kubeflow project. It uses charms, allowing components to easily connect and work effectively for different use cases. Main features include:
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* _Free to use_: Charmed Kubeflow is offered as free, open-source software. You don’t need a support agreement or license to deploy Charmed Kubeflow.
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* _Integrated_: A growing ecosystem of extensions and integrations, such as [MLFlow](https://mlflow.org/) to help scale up your data science initiatives.
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* _Available_: Charmed Kubeflow is a fully portable solution for any cloud, including on-premise Kubernetes. Train your teams once to work anywhere.
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* _Supported_: [24/7 support](https://ubuntu.com/support), [professional services](https://ubuntu.com/ai/services) and [managed services](https://ubuntu.com/managed/apps) with guaranteed SLA are available.
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* _Community-driven:_ Charmed Kubeflow is an [open-source product](https://github.com/canonical/bundle-kubeflow) driven by the community’s [feedback](https://discourse.charmhub.io/tag/kubeflow). You can always contribute or get our team’s support.
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---
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[Charmed Kubeflow deployment guide](https://charmed-kubeflow.io/docs/quickstart)
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Instructions for Kubeflow deployment with Kubeflow Charmed Operators
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[Charmed Kubeflow upgrade guide](https://charmed-kubeflow.io/docs/upgrade)
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Instructions to update to the latest version of Charmed Kubeflow.
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[Charmed Kubeflow tutorials](https://charmed-kubeflow.io/tutorials)
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A collection of fun tutorials once you get started with Kubeflow
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[Charmed Kubeflow documentation](https://charmed-kubeflow.io/docs)
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Official documentation of Charmed Kubeflow
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title = "Arrikto Enterprise Kubeflow"
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description = "Kubeflow distribution with additional automation, reproducibility, portability, and security features"
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weight = 50
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The <a href="https://www.arrikto.com/enterprise-kubeflow/" target="_blank">Arrikto Enterprise Kubeflow (EKF)</a> distribution extends the capabilities of the Kubeflow platform with additional automation, reproducibility, portability, and security features.
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- *Automation*: Orchestrate your end-to-end ML workflow with a click of a button. Start by tagging cells in Jupyter Notebooks to define pipeline steps, hyperparameter tuning, GPU usage, and metrics tracking. Click a button to create pipeline components and KFP DSL, resolve dependencies, inject data objects into each step, deploy the data science pipeline, and serve the best model. Or use the Kale SDK to do all the above with your preferred IDE.
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- *Reproducibility*: Snapshot pipeline code, libraries, and data for every step with Arrikto’s Rok data management platform. Roll back to any machine learning pipeline step at it’s exact execution state for easy debugging. Collaborate with other data scientists through a Git-style publish/subscribe versioning workflow.
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- *Portability*: Deploy and upgrade your Kubeflow environment with a proven GitOps process across all major public clouds, and on-prem infrastructure. Move ML workflows seamlessly across with Rok Registry.
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- *Security*: Manage teams and user access via GitLab or any ID provider via Istio/OIDC. Isolate user ML data access within their own namespace while enabling notebook and pipeline collaboration in shared namespaces. Manage secrets and credentials securely, and efficiently.
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approvers:
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- joeliedtke
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- zijianjoy
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reviewers:
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- joeliedtke
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title = "Kubeflow on Google Cloud"
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description = "Running Kubeflow on Kubernetes Engine and Google Cloud Platform"
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weight = 20
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[Kubeflow on Google Cloud](https://googlecloudplatform.github.io/kubeflow-gke-docs) is an open-source toolkit for building machine learning (ML) systems. Seamlessly integrated with GCP services Kubeflow allows you to build secure, scalable, and reliable ML workflows of any complexity, while reducing operational costs and development time.
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Start using [Kubeflow on Google Cloud](https://googlecloudplatform.github.io/kubeflow-gke-docs) today.
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approvers:
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- Tomcli
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- yhwang
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title = "Kubeflow on IKS"
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description = "Running Kubeflow on IBM Cloud Kubernetes Service (IKS)"
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weight = 20
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[Kubeflow on IKS](https://ibm.github.io/manifests/) is an open source distribution of the Kubeflow manifests.
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For more information, see the [Kubeflow on IKS documentation](https://ibm.github.io/manifests/docs/).
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title = "List of Kubeflow Distributions"
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description = "A list showing packaged distributions of Kubeflow"
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weight = 10
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manualLinkRelref = "../started/installing-kubeflow.md#packaged-distributions-of-kubeflow"
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We maintain a list showing __packaged distributions of Kubeflow__ on the [Installing Kubeflow](/docs/started/installing-kubeflow/#packaged-distributions-of-kubeflow) page.
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approvers:
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- johnugeorge
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- deepak-muley
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reviewers:
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- johnugeorge
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- deepak-muley
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title = "Kubeflow on Nutanix"
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description = "Running Kubeflow on Nutanix Kubernetes Engine(NKE)"
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weight = 20
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[Kubeflow on NKE](https://nutanix.github.io/kubeflow-manifests/) project is based on Kubeflow, dedicated to making deployments of machine learning (ML) workflows on Nutanix Kubernetes Engine simple, portable and scalable. Kubeflow on NKE project provides seamless integration with Nutanix services.
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For more information, see the [Kubeflow on NKE documentation](https://nutanix.github.io/kubeflow-manifests/docs/).
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