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Katib automates machine learning on Kubernetes, including hyperparameter tuning and neural architecture search.
About Katib
Katib is an open-source, Kubernetes-native project for automated machine learning (AutoML). It supports hyperparameter tuning, early stopping, and neural architecture search. The project is framework-agnostic and can tune hyperparameters of applications written in any language, with native support for frameworks such as TensorFlow, PyTorch, and XGBoost. Training jobs can run using any Kubernetes Custom Resources, with out-of-box support for Kubeflow Training Operator, Argo Workflows, and Tekton Pipelines. Katib offers multiple search algorithms and algorithm frameworks. Installation requires Kubernetes and Kubeflow prerequisites; the control plane is installed with kubectl, and a Python SDK is available via pip. Pricing information is not provided on the homepage.
Key features
- Kubernetes-native automated machine learning for hyperparameter tuning
- Supports early stopping and neural architecture search
- Framework-agnostic; tunes applications in any language
- Out-of-box support for Kubeflow Training Operator, Argo Workflows, Tekton Pipelines
- Search algorithms include Bayesian Optimization, HyperBand, Population Based Training
- Python SDK simplifies creating hyperparameter tuning jobs for data scientists
Best for: Data scientists and ML engineers running Kubernetes who need to automate hyperparameter tuning, early stopping, or neural architecture search.
Good for
- Open source project with community meetings and a Slack channel
- Framework-agnostic and natively supports many ML frameworks
- Offers multiple search algorithms and algorithm frameworks
Watch out for
- Installation requires Kubernetes and additional Kubeflow prerequisites
Katib at a glance
- Website
- github.com/kubeflow/katib
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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Katib FAQ
What types of automated machine learning does Katib support?
Katib supports hyperparameter tuning, early stopping, and neural architecture search.
Does Katib work with different machine learning frameworks?
Yes, Katib is framework-agnostic and natively supports frameworks such as TensorFlow, PyTorch, XGBoost, and others.
How can I install Katib?
Install the control plane using kubectl apply with the provided manifests, or use the Python SDK via pip install -U kubeflow-katib. Prerequisites are in the Kubeflow documentation.
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