Version v0.6 of the documentation is no longer actively maintained. The site that you are currently viewing is an archived snapshot. For up-to-date documentation, see the latest version.
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
Follow the getting-started guide to set up your environment.
Then read the documentation to learn about the features of Kubeflow, including the following guides to Kubeflow components:
Kubeflow includes services for spawning and managing Jupyter notebooks. Project Jupyter is a non-profit, open source project that supports interactive data science and scientific computing across many programming languages.
Kubeflow Pipelines is a platform for building, deploying, and managing multi-step ML workflows based on Docker containers.
Kubeflow offers a number of components that you can use to build your ML training, hyperparameter tuning, and serving workloads across multiple platforms.
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 scripts 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, and service management.
You can choose to deploy your Kubernetes workloads locally 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 (laptop <-> ML rig <-> training cluster <-> production cluster)
- 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 entire machine learning pipelines.
Was this page helpful?
Glad to hear it! Please tell us how we can improve.
Sorry to hear that. Please tell us how we can improve.