The Machine Learning Toolkit for Kubernetes

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What is Kubeflow?

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.

Kubeflow Components

Kubeflow Pipelines Logo

Kubeflow Pipelines (KFP) is a platform for building then deploying portable and scalable machine learning workflows using Kubernetes.

Jupyter + VSCode + RLang Logo

Kubeflow Notebooks lets you run web-based development environments on your Kubernetes cluster by running them inside Pods.

People Icon

Kubeflow Central Dashboard is our hub which connects the authenticated web interfaces of Kubeflow and other ecosystem components.

Katib Logo

Katib is a Kubernetes-native project for automated machine learning (AutoML) with support for hyperparameter tuning, early stopping and neural architecture search.

TensorFlow + PyTorch Logo
Model Training

Kubeflow Training Operator is a unified interface for model training and fine-tuning on Kubernetes. It runs scalable and distributed training jobs for popular frameworks including PyTorch, TensorFlow, MPI, MXNet, PaddlePaddle, and XGBoost.

KServe Logo
Model Serving

KServe (previously KFServing) solves production model serving on Kubernetes. It delivers high-abstraction and performant interfaces for frameworks like Tensorflow, XGBoost, ScikitLearn, PyTorch, and ONNX.

Join our Community

We are an open and welcoming community of software developers, data scientists, and organizations! Check out the weekly community call, get involved in discussions on the mailing list or chat with others on the Slack Workspace!

Cloud Native Computing Foundation Logo
We are a Cloud Native Computing Foundation project.