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.
Getting started with Kubeflow
Read the architecture overview for an introduction to the architecture of Kubeflow and to see how you can use Kubeflow to manage your ML workflow.
Follow Installing Kubeflow to set up your environment and install Kubeflow.
Watch the following video which provides an 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:
- data preparation
- model training,
- prediction serving
- 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 production 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-premises cluster 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 to customize the stack based on user requirements (within reason) and let the system take care of the “boring stuff”. While we have started with a narrow set of technologies, we are working with many different projects to include additional tooling.
Ultimately, we want to have a set of simple manifests that give you an easy to use ML stack anywhere Kubernetes is already running, and that can self configure based on the cluster it deploys into.
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 end-to-end machine learning workflows.
To see what’s coming up in future versions of Kubeflow, refer to the Kubeflow roadmap.
The following components also have roadmaps:
There are many ways to contribute to Kubeflow, and we welcome contributions!
Read the contributor’s guide to get started on the code, and learn about the community on the community page.
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