diff --git a/content/en/docs/started/installing-kubeflow.md b/content/en/docs/started/installing-kubeflow.md index ff35f26b73..502b2bd3b1 100644 --- a/content/en/docs/started/installing-kubeflow.md +++ b/content/en/docs/started/installing-kubeflow.md @@ -5,30 +5,161 @@ weight = 20 +++ -## What is Kubeflow? +This guide describes how to install standalone Kubeflow components or Kubeflow Platform using package +distributions or Kubeflow manifests. -Kubeflow is an end-to-end Machine Learning (ML) platform for Kubernetes, it provides components for each stage in the ML lifecycle, from exploration through to training and deployment. -Operators can choose what is best for their users, there is no requirement to deploy every component. +Read [the introduction guide](/docs/started/introduction) to learn more about Kubeflow, standalone +Kubeflow components and Kubeflow Platform. -Learn more about Kubeflow in the [Introduction](/docs/started/introduction/) and -[Architecture](/docs/started/architecture/) pages. +## Installation Methods -## How to install Kubeflow? +You can install Kubeflow using one of these methods: -Anywhere you are running Kubernetes, you should be able to run Kubeflow. -There are two primary ways to install Kubeflow: +- [**Standalone Kubeflow Components**](#standalone-kubeflow-components) +- [**Kubeflow Platform**](#kubeflow-platform) -1. [**Packaged Distributions**](#packaged-distributions-of-kubeflow) -1. [**Raw Manifests**](#raw-kubeflow-manifests) (advanced users) +## Standalone Kubeflow Components - - +Some components in the [Kubeflow ecosystem](/docs/started/architecture/#conceptual-overview) may be +deployed as standalone services, without the need to install the full Kubeflow platform. You might +integrate these services as part of your existing AI/ML platform or use them independently. -## Packaged Distributions of Kubeflow +These components are a quick and easy method to get started with the Kubeflow ecosystem. They +provide flexibility to users who may not require the capabilities of a full Kubeflow Platform. + +The following table lists Kubeflow components that may be deployed in a standalone mode. It also +lists their associated GitHub repository and +corresponding [ML lifecycle stage](/docs/started/architecture/#kubeflow-components-in-the-ml-lifecycle). + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ComponentML Lifecycle StageSource Code
+ + KServe + + + Model Serving + + + kserve/kserve + +
+ + Kubeflow Katib + + + Model Optimization and AutoML + + + kubeflow/katib + +
+ + Kubeflow Model Registry + + + Model Registry + + + kubeflow/model-registry + +
+ + Kubeflow MPI Operator + + + All-Reduce Model Training + + + kubeflow/mpi-operator + +
+ + Kubeflow Pipelines + + + ML Workflows and Schedules + + + kubeflow/pipelines + +
+ + Kubeflow Spark Operator + + + Data Preparation + + + kubeflow/spark-operator + +
+ + Kubeflow Training Operator + + + Model Training and Fine-Tuning + + + kubeflow/training-operator + +
+
+ +## Kubeflow Platform + +You can use one of the following methods to install the [Kubeflow Platform](/docs/started/introduction/#what-is-kubeflow-platform) +and get the full suite of Kubeflow components bundled together with additional tools. + +### Packaged Distributions Packaged distributions are maintained by various organizations and typically aim to provide -a simplified installation and management experience for Kubeflow. Some distributions can be -deployed on [all certified Kubernetes distributions](https://kubernetes.io/partners/#conformance), +a simplified installation and management experience for your **Kubeflow Platform**. Some distributions +can be deployed on [all certified Kubernetes distributions](https://kubernetes.io/partners/#conformance), while others target a specific platform (e.g. EKS or GKE). {{% alert title="Note" color="warning" %}} @@ -200,12 +331,16 @@ The following table lists distributions which are maintained by their r -## Raw Kubeflow Manifests +### Kubeflow Manifests -The raw Kubeflow Manifests are aggregated by the [Manifests Working Group](https://github.com/kubeflow/community/tree/master/wg-manifests) -and are intended to be used as the **base of packaged distributions**. +The Kubeflow Manifests are aggregated by the Manifests Working Group and are intended to be +used as the **base of packaged distributions**. -Advanced users may choose to install the manifests for a specific Kubeflow version by following the +Kubeflow Manifests contain all Kubeflow Components, Kubeflow Central Dashboard, and other Kubeflow +applications that comprise the **Kubeflow Platform**. This installation is helpful when you want to +try out the end-to-end Kubeflow Platform capabilities. + +Users may choose to install the manifests for a specific Kubeflow version by following the instructions in the `README` of the [`kubeflow/manifests`](https://github.com/kubeflow/manifests) repository. - [**Kubeflow 1.8:**](/docs/releases/kubeflow-1.8/) @@ -217,16 +352,15 @@ instructions in the `README` of the [`kubeflow/manifests`](https://github.com/ku {{% alert title="Warning" color="warning" %}} Kubeflow is a complex system with many components and dependencies. -Using the raw manifests requires a deep understanding of Kubernetes, Istio, and Kubeflow itself. +Using the Kubeflow manifests requires a deep understanding of Kubernetes, Istio, and Kubeflow itself. -When using the raw manifests, the Kubeflow community is not able to provide support for environment-specific issues or custom configurations. -If you need support, please consider using a [packaged distribution](#packaged-distributions-of-kubeflow). +When using the Kubeflow manifests, the community is not able to provide support for environment-specific issues or custom configurations. +If you need support, please consider using a [packaged distribution](#packaged-distributions). Nevertheless, we welcome contributions and bug reports very much. {{% /alert %}} - - ## Next steps -- Review the Kubeflow component documentation -- Explore the Kubeflow Pipelines SDK +- Review our [introduction to Kubeflow](/docs/started/introduction/). +- Explore the [architecture of Kubeflow](/docs/started/architecture). +- Learn more about the [components of Kubeflow](/docs/components/). diff --git a/content/en/docs/started/introduction.md b/content/en/docs/started/introduction.md index f8d0576fae..b404c161cc 100644 --- a/content/en/docs/started/introduction.md +++ b/content/en/docs/started/introduction.md @@ -4,11 +4,44 @@ description = "An introduction to Kubeflow" weight = 1 +++ -The Kubeflow project is dedicated to making deployments of machine learning (ML) -workflows on Kubernetes simple, portable and scalable. Our goal is not to -recreate other services, but to provide a straightforward way to deploy -best-of-breed open-source systems for ML to diverse infrastructures. Anywhere -you are running Kubernetes, you should be able to run Kubeflow. +## What is Kubeflow + +Kubeflow is a community and ecosystem of open-source projects to address each stage in the +machine learning (ML) lifecycle. It makes ML on Kubernetes simple, portable, and scalable. +The goal of Kubeflow is to facilitate the orchestration of Kubernetes ML workloads and to empower +users to deploy best-in-class open-source tools on any Cloud infrastructure. + +Whether you’re a researcher, data scientist, ML engineer, or a team of developers, Kubeflow offers +modular and scalable tools that cater to all aspects of the ML lifecycle: from building ML models to +deploying them to production for AI applications. + +## What are Standalone Kubeflow Components + +The Kubeflow ecosystem is composed of multiple open-source projects that address different aspects +of the ML lifecycle. Many of these projects are designed to be usable both within the +Kubeflow Platform and independently. These Kubeflow components can be installed standalone on a +Kubernetes cluster. It provides flexibility to users who may not require the full Kubeflow Platform +capabilities but wish to leverage specific ML functionalities such as model training or model serving. + +## What is Kubeflow Platform + +The Kubeflow Platform refers to the full suite of Kubeflow components bundled together with +additional integration and management tools. Using Kubeflow as a platform means deploying a +comprehensive ML toolkit for the entire ML lifecycle. + +In addition to the standalone Kubeflow components, the Kubeflow Platform includes + +- [Kubeflow Notebooks](/docs/components/notebooks/overview) for interactive data exploration and + model development. +- [Central Dashboard](/docs/components/central-dash/overview/) for easy navigation and management + with [Kubeflow Profiles](/docs/components/central-dash/profiles/) for access control. +- Additional tooling for data management (PVC Viewer), visualization (TensorBoards), and more. + +The Kubeflow Platform can be installed via +[Packaged Distributions](/docs/started/installing-kubeflow/#packaged-distributions) or +[Kubeflow Manifests](/docs/started/installing-kubeflow/#kubeflow-manifests). + +## Getting started with Kubeflow The following diagram shows the main Kubeflow components to cover each step of ML lifecycle on top of Kubernetes. @@ -17,8 +50,6 @@ on top of Kubernetes. alt="Kubeflow overview" class="mt-3 mb-3"> -## Getting started with Kubeflow - Read the [architecture overview](/docs/started/architecture/) for an introduction to the architecture of Kubeflow and to see how you can use Kubeflow to manage your ML workflow. @@ -30,28 +61,6 @@ Watch the following video which provides an introduction to Kubeflow. {{< youtube id="cTZArDgbIWw" title="Introduction to Kubeflow">}} -## What is Kubeflow? - -Kubeflow is _the machine learning toolkit for Kubernetes_. - -To use Kubeflow, the basic workflow is: - -- Download and run the Kubeflow deployment binary. -- Customize the resulting configuration files. -- Run the specified script to deploy your containers to your specific - environment. - -You can adapt the configuration to choose the platforms and services that you -want to use for each stage of the ML workflow: - -1. data preparation -2. model training, -3. prediction serving -4. service management - -You can choose to deploy your Kubernetes workloads locally, on-premises, or to -a cloud environment. - ## The Kubeflow mission Our goal is to make scaling machine learning (ML) models and deploying them to @@ -85,12 +94,17 @@ To see what's coming up in future versions of Kubeflow, refer to the [Kubeflow r The following components also have roadmaps: - [Kubeflow Pipelines](https://github.com/kubeflow/pipelines/blob/master/ROADMAP.md) -- [KF Serving](https://github.com/kubeflow/kfserving/blob/master/ROADMAP.md) +- [KServe](https://github.com/kserve/kserve/blob/master/ROADMAP.md) - [Katib](https://github.com/kubeflow/katib/blob/master/ROADMAP.md) -- [Training Operator](https://github.com/kubeflow/common/blob/master/ROADMAP.md) +- [Training Operator](https://github.com/kubeflow/training-operator/blob/master/docs/roadmap.md) ## Getting involved There are many ways to contribute to Kubeflow, and we welcome contributions! Read the [contributor's guide](/docs/about/contributing/) to get started on the code, and learn about the community on the [community page](/docs/about/community/). + +## Next Steps + +- Follow [the installation guide](/docs/started/installing-kubeflow) to deploy standalone + Kubeflow components or Kubeflow Platform.