Kubeflow

An introduction to Kubeflow

The Kubeflow project is dedicated to making deployments of machine learning (ML)workflows on Kubernetes simple, portable and scalable. Our goal is not torecreate other services, but to provide a straightforward way to deploybest-of-breed open-source systems for ML to diverse infrastructures. Anywhereyou are running Kubernetes, you should be able to run Kubeflow.

Getting started with Kubeflow

Read the Kubeflow overview for anintroduction to the Kubeflow architecture and to see how you can use Kubeflowto manage your ML workflow.

Follow the getting-started guide to set upyour environment and install 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 specificenvironment.

You can adapt the configuration to choose the platforms and services that youwant to use for each stage of the ML workflow: data preparation, model training,prediction serving, and service management.

You can choose to deploy your Kubernetes workloads locally, on-premises, or toa cloud environment.

Read the Kubeflow overview for more details.

The Kubeflow mission

Our goal is to make scaling machine learning (ML) models and deploying them toproduction as simple as possible, by letting Kubernetes do what it’s great at:

• Easy, repeatable, portable deployments on a diverse infrastructure(for example, experimenting on a laptop, then moving to an on-premisescluster or to the cloud)
• Deploying and managing loosely-coupled microservices
• Scaling based on demand

Because ML practitioners use a diverse set of tools, one of the key goals is tocustomize the stack based on user requirements (within reason) and let thesystem take care of the “boring stuff”. While we have started with a narrow setof technologies, we are working with many different projects to includeadditional tooling.

Ultimately, we want to have a set of simple manifests that give you an easy touse ML stack anywhere Kubernetes is already running, and that can selfconfigure based on the cluster it deploys into.

History

Kubeflow started as an open sourcing of the way Google ran TensorFlow internally, based on a pipeline called TensorFlow Extended. 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 entire machine learning pipelines.

Getting involved

There are many ways to contribute to Kubeflow, and we welcome contributions!Read the contributor’s guide to get started on thecode, and get to know the community in thecommunity guide.