Katib is a Kubernetes-native project for automated machine learning (AutoML).
Katib supports
Hyperparameter Tuning,
Early Stopping and
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, Apache MXNet,
PyTorch, XGBoost, and others.

Katib can perform training jobs using any Kubernetes
Custom Resources
with out of the box support for Kubeflow Training Operator,
Argo Workflows, Tekton Pipelines
and many more.

Katib stands for secretary in Arabic.

Search Algorithms

Katib supports several search algorithms. Follow the
Kubeflow documentation
to know more about each algorithm and check the
Suggestion service guide 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 above algorithms Katib supports the following frameworks:

Installation

For the various Katib installs check the
Kubeflow guide.
Follow the next steps to install Katib standalone.

Prerequisites

This is the minimal requirements to install Katib:

Kubernetes >= 1.21
kubectl >= 1.21

Latest Version

For the latest Katib version run this command:

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

Release Version

For the specific Katib release (for example v0.13.0) run this command:

kubectl apply -k "github.com/kubeflow/katib.git/manifests/v1beta1/installs/katib-standalone?ref=v0.13.0"

Make sure that all Katib components are running:

$ kubectl get pods -n kubeflow

NAME                                READY   STATUS      RESTARTS   AGE
katib-cert-generator-rw95w          0/1     Completed   0          35s
katib-controller-566595bdd8-hbxgf   1/1     Running     0          36s
katib-db-manager-57cd769cdb-4g99m   1/1     Running     0          36s
katib-mysql-7894994f88-5d4s5        1/1     Running     0          36s
katib-ui-5767cfccdc-pwg2x           1/1     Running     0          36s

For the Katib Experiments check the complete examples list.

Documentation

Run your first Katib Experiment in the
getting started guide.
Learn about Katib Concepts in this
guide.
Learn about Katib Interfaces in this
guide.
Learn about Katib Components in this
guide.
Know more about Katib in the presentations and demos list.

Community

We are always growing our community and invite new users and AutoML enthusiasts
to contribute to the Katib project. The following links provide information
about getting involved in the community:

Subscribe to the
AutoML calendar
to attend Working Group bi-weekly community meetings.
Check the
AutoML and Training Working Group meeting notes.
If you use Katib, please update the adopters list.

Contributing

Please feel free to test the system! Developer guide
is a good starting point for our developers.

Blog posts

Kubeflow Katib: Scalable, Portable and Cloud Native System for AutoML
(by Andrey Velichkevich)

Events

AutoML and Training WG Summit. 16th of July 2021

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, 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}
}

anencore94/katib