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https://github.com/kubeflow/katib

Automated Machine Learning on Kubernetes
https://github.com/kubeflow/katib

ai automl huggingface hyperparameter-tuning jax kubeflow kubernetes llm machine-learning mlops neural-architecture-search pytorch scikit-learn tensorflow

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Automated Machine Learning on Kubernetes

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README 

        


    logo

  





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Katib is a Kubernetes-native project for automated machine learning (AutoML).

Katib supports

[Hyperparameter Tuning](https://en.wikipedia.org/wiki/Hyperparameter_optimization),

[Early Stopping](https://en.wikipedia.org/wiki/Early_stopping) and

[Neural Architecture Search](https://en.wikipedia.org/wiki/Neural_architecture_search).



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](https://www.tensorflow.org/), [PyTorch](https://pytorch.org/), [XGBoost](https://xgboost.readthedocs.io/en/latest/), and others.



Katib can perform training jobs using any Kubernetes

[Custom Resources](https://www.kubeflow.org/docs/components/katib/trial-template/)

with out of the box support for [Kubeflow Training Operator](https://github.com/kubeflow/training-operator),

[Argo Workflows](https://github.com/argoproj/argo-workflows), [Tekton Pipelines](https://github.com/tektoncd/pipeline)

and many more.



Katib stands for `secretary` in Arabic.



## Search Algorithms



Katib supports several search algorithms. Follow the

[Kubeflow documentation](https://www.kubeflow.org/docs/components/katib/user-guides/hp-tuning/configure-algorithm/#hp-tuning-algorithms)

to know more about each algorithm and check the

[this guide](https://www.kubeflow.org/docs/components/katib/user-guides/hp-tuning/configure-algorithm/#use-custom-algorithm-in-katib)

to implement your custom algorithm.



  

    

      

        Hyperparameter Tuning

      

      

        Neural Architecture Search

      

      

        Early Stopping

      

    

    

      

        Random Search

      

      

        ENAS

      

      

        Median Stop

      

    

    

      

        Grid Search

      

      

        DARTS

      

      

      

    

    

      

        Bayesian Optimization

      

      

      

      

      

    

    

      

        TPE

      

      

      

      

      

    

    

      

        Multivariate TPE

      

      

      

      

      

    

    

      

        CMA-ES

      

      

      

      

      

    

    

      

        Sobol's Quasirandom Sequence

      

      

      

      

      

    

    

      

        HyperBand

      

      

      

      

      

    

    

      

        Population Based Training

      

      

      

      

      

    

  



To perform the above algorithms Katib supports the following frameworks:



- [Goptuna](https://github.com/c-bata/goptuna)

- [Hyperopt](https://github.com/hyperopt/hyperopt)

- [Optuna](https://github.com/optuna/optuna)

- [Scikit Optimize](https://github.com/scikit-optimize/scikit-optimize)



## Prerequisites



Please check [the official Kubeflow documentation](https://www.kubeflow.org/docs/components/katib/installation/#prerequisites)

for prerequisites to install Katib.



## Installation



Please follow [the Kubeflow Katib guide](https://www.kubeflow.org/docs/components/katib/installation/#installing-katib)

for the detailed instructions on how to install Katib.



### Installing the Control Plane



Run the following command to install the latest stable release of Katib control plane:



```

kubectl apply -k "github.com/kubeflow/katib.git/manifests/v1beta1/installs/katib-standalone?ref=v0.17.0"

```



Run the following command to install the latest changes of Katib control plane:



```

kubectl apply -k "github.com/kubeflow/katib.git/manifests/v1beta1/installs/katib-standalone?ref=master"

```



For the Katib Experiments check the [complete examples list](./examples/v1beta1).



### Installing the Python SDK



Katib implements [a Python SDK](https://pypi.org/project/kubeflow-katib/) to simplify creation of

hyperparameter tuning jobs for Data Scientists.



Run the following command to install the latest stable release of Katib SDK:



```sh

pip install -U kubeflow-katib

```



## Getting Started



Please refer to [the getting started guide](https://www.kubeflow.org/docs/components/katib/getting-started/#getting-started-with-katib-python-sdk)

to quickly create your first hyperparameter tuning Experiment using the Python SDK.



## Community



The following links provide information on how to get involved in the community:



- Attend [the bi-weekly AutoML and Training Working Group](https://bit.ly/2PWVCkV)

  community meeting.

- Join our [`#kubeflow-katib`](https://www.kubeflow.org/docs/about/community/#kubeflow-slack-channels)

  Slack channel.

- Check out [who is using Katib](ADOPTERS.md) and [presentations about Katib project](docs/presentations.md).



## Contributing



Please refer to the [CONTRIBUTING guide](CONTRIBUTING.md).



## Citation



If you use Katib in a scientific publication, we would appreciate

citations to the following paper:



[A Scalable and Cloud-Native Hyperparameter Tuning System](https://arxiv.org/abs/2006.02085), George _et al._, arXiv:2006.02085, 2020.



Bibtex entry:



```

@misc{george2020katib,

    title={A Scalable and Cloud-Native Hyperparameter Tuning System},

    author={Johnu George and Ce Gao and Richard Liu and Hou Gang Liu and Yuan Tang and Ramdoot Pydipaty and Amit Kumar Saha},

    year={2020},

    eprint={2006.02085},

    archivePrefix={arXiv},

    primaryClass={cs.DC}

}

```