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更新时间 2026-11-13 21:39:29
Overview | Kubeflow
The Katib project is deprecated. Users should migrate to the OptimizationJob APIs, which are released as part of the Kubeflow Trainer project under alpha feature gate.
OptimizationJob is the next step for hyperparameter optimization in Kubeflow. It is natively integrated with the TrainJob API, so every trial can use Trainer runtimes and distributed training features out of the box. Metrics are pushed through the Kubeflow SDK and Trainer status server, so you no longer need a separate database or a metrics collector sidecar.
Work on the remaining features is tracked in kubeflow/trainer#3562.
Katib is a Kubernetes-native project for automated machine learning (AutoML). Katib supports hyperparameter tuning, early stopping and neural architecture search (NAS). Learn more about AutoML at fast.ai, Google Cloud, Microsoft Azure or Amazon SageMaker.
Katib is the project which is agnostic to machine learning (ML) frameworks. It can tune hyperparameters of applications written in any language of the users’ choice and natively supports many ML frameworks, such as TensorFlow, MXNet, PyTorch, XGBoost, and others.
Katib supports a lot of various AutoML algorithms, such as Bayesian optimization, Tree of Parzen Estimators, Random Search, Covariance Matrix Adaptation Evolution Strategy, Hyperband, Efficient Neural Architecture Search, Differentiable Architecture Search and many more. Additional algorithm support is coming soon.
The Katib project is open source. The developer guide is a good starting point for developers who want to contribute to the project.
Katib addresses AutoML step for hyperparameter optimization or Neural Architecture Search in AI/ML lifecycle as shown on that diagram:
Katib is integrated with Kubeflow Training Operator jobs such as PyTorchJob, which allows to optimize hyperparameters for large models of any size.
In addition to that, Katib can orchestrate workflows such as Argo Workflows and Tekton Pipelines for more advanced optimization use-cases.
Katib runs Kubernetes containers to perform hyperparameter tuning job, which allows to use Katib with any ML training framework.
Users can even use Katib to optimize non-ML tasks as long as optimization metrics can be collected.
Katib is integrated with many optimization frameworks such as Hyperopt and Optuna which implements most of the state of the art optimization algorithms.
Users can leverage Katib control plane to implement and benchmark their own optimization algorithms
Follow the installation guide to deploy Katib.
Run examples from getting started guide.
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© 2026 The Kubeflow Authors.Documentation distributed under CC BY 4.0
The Linux Foundation® (TLF) has registered trademarks and uses trademarks. For a list of TLF trademarks, see Trademark Usage.
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OptimizationJob is the next step for hyperparameter optimization in Kubeflow. It is natively integrated with the TrainJob API, so every trial can use Trainer runtimes and distributed training features out of the box. Metrics are pushed through the Kubeflow SDK and Trainer status server, so you no longer need a separate database or a metrics collector sidecar.
Work on the remaining features is tracked in kubeflow/trainer#3562.
Katib is a Kubernetes-native project for automated machine learning (AutoML). Katib supports hyperparameter tuning, early stopping and neural architecture search (NAS). Learn more about AutoML at fast.ai, Google Cloud, Microsoft Azure or Amazon SageMaker.
Katib is the project which is agnostic to machine learning (ML) frameworks. It can tune hyperparameters of applications written in any language of the users’ choice and natively supports many ML frameworks, such as TensorFlow, MXNet, PyTorch, XGBoost, and others.
Katib supports a lot of various AutoML algorithms, such as Bayesian optimization, Tree of Parzen Estimators, Random Search, Covariance Matrix Adaptation Evolution Strategy, Hyperband, Efficient Neural Architecture Search, Differentiable Architecture Search and many more. Additional algorithm support is coming soon.
The Katib project is open source. The developer guide is a good starting point for developers who want to contribute to the project.
Katib addresses AutoML step for hyperparameter optimization or Neural Architecture Search in AI/ML lifecycle as shown on that diagram:
Katib is integrated with Kubeflow Training Operator jobs such as PyTorchJob, which allows to optimize hyperparameters for large models of any size.
In addition to that, Katib can orchestrate workflows such as Argo Workflows and Tekton Pipelines for more advanced optimization use-cases.
Katib runs Kubernetes containers to perform hyperparameter tuning job, which allows to use Katib with any ML training framework.
Users can even use Katib to optimize non-ML tasks as long as optimization metrics can be collected.
Katib is integrated with many optimization frameworks such as Hyperopt and Optuna which implements most of the state of the art optimization algorithms.
Users can leverage Katib control plane to implement and benchmark their own optimization algorithms
Follow the installation guide to deploy Katib.
Run examples from getting started guide.
Was this page helpful?
Thank you for your feedback!
We're sorry this page wasn't helpful. If you have a moment, please share your feedback so we can improve.
© 2026 The Kubeflow Authors.Documentation distributed under CC BY 4.0
The Linux Foundation® (TLF) has registered trademarks and uses trademarks. For a list of TLF trademarks, see Trademark Usage.
Website Privacy Policy
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