mirror of https://github.com/kubeflow/website.git
90 lines
3.6 KiB
Markdown
90 lines
3.6 KiB
Markdown
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title = "Introduction"
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description = "An introduction to Kubeflow"
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weight = 1
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The Kubeflow project is dedicated to making deployments of machine learning (ML)
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workflows on Kubernetes simple, portable and scalable. Our goal is not to
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recreate other services, but to provide a straightforward way to deploy
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best-of-breed open-source systems for ML to diverse infrastructures. Anywhere
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you are running Kubernetes, you should be able to run Kubeflow.
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## Getting started with Kubeflow
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Read the [architecture overview](/docs/started/architecture/) for an
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introduction to the architecture of Kubeflow and to see how you can use Kubeflow
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to manage your ML workflow.
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Follow [Installing Kubeflow](/docs/started/installing-kubeflow/) to set up
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your environment and install Kubeflow.
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Watch the following video which provides an introduction to Kubeflow.
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{{< youtube id="cTZArDgbIWw" title="Introduction to Kubeflow">}}
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## What is Kubeflow?
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Kubeflow is _the machine learning toolkit for Kubernetes_.
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To use Kubeflow, the basic workflow is:
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- Download and run the Kubeflow deployment binary.
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- Customize the resulting configuration files.
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- Run the specified script to deploy your containers to your specific
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environment.
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You can adapt the configuration to choose the platforms and services that you
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want to use for each stage of the ML workflow:
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1. data preparation
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2. model training,
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3. prediction serving
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4. service management
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You can choose to deploy your Kubernetes workloads locally, on-premises, or to
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a cloud environment.
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## The Kubeflow mission
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Our goal is to make scaling machine learning (ML) models and deploying them to
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production as simple as possible, by letting Kubernetes do what it's great at:
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- Easy, repeatable, portable deployments on a diverse infrastructure
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(for example, experimenting on a laptop, then moving to an on-premises
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cluster or to the cloud)
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- Deploying and managing loosely-coupled microservices
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- Scaling based on demand
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Because ML practitioners use a diverse set of tools, one of the key goals is to
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customize the stack based on user requirements (within reason) and let the
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system take care of the "boring stuff". While we have started with a narrow set
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of technologies, we are working with many different projects to include
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additional tooling.
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Ultimately, we want to have a set of simple manifests that give you an easy to
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use ML stack _anywhere_ Kubernetes is already running, and that can self
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configure based on the cluster it deploys into.
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## History
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Kubeflow started as an open sourcing of the way Google ran [TensorFlow](https://www.tensorflow.org/) internally, based on a pipeline called [TensorFlow Extended](https://www.tensorflow.org/tfx/).
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It began as just a simpler way to run TensorFlow jobs on Kubernetes, but has since expanded to be a multi-architecture, multi-cloud framework for running end-to-end machine learning workflows.
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## Roadmaps
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To see what's coming up in future versions of Kubeflow, refer to the [Kubeflow roadmap](https://github.com/kubeflow/kubeflow/blob/master/ROADMAP.md).
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The following components also have roadmaps:
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- [Kubeflow Pipelines](https://github.com/kubeflow/pipelines/blob/master/ROADMAP.md)
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- [KF Serving](https://github.com/kubeflow/kfserving/blob/master/ROADMAP.md)
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- [Katib](https://github.com/kubeflow/katib/blob/master/ROADMAP.md)
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- [Training Operator](https://github.com/kubeflow/common/blob/master/ROADMAP.md)
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## Getting involved
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There are many ways to contribute to Kubeflow, and we welcome contributions!
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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/).
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