Kubeflow Katib
KubeflowKubernetes-native hyperparameter tuning and neural architecture search. An Experiment declares the search space, objective and algorithm; Katib's controllers create Trials, run them in parallel up to a declared limit, collect the objective metric from each and stop the Experiment when the goal or the trial budget is reached. It is framework-agnostic and tunes whatever the trial runs.
Openness
5 high confidence- license
- Apache-2.0(OSI)
- source
- public(kubeflow/katib is the whole controller set)
- core features withheld
- no — a 1,533-entry recursive tree carries one root LICENSE and no path matching enterprise, ee, commercial or proprietary
Katib is Apache-2.0, and its repository tree carries one root LICENSE and no enterprise, ee or commercial path, so no part of it is held back for a paid edition.
- https://api.github.com/repos/kubeflow/katib/git/trees/master?recursive=1 recorded 2026-09-15
Full untruncated recursive tree of the default branch, 1,533 entries. One root LICENSE. No path matches enterprise, ee, commercial or proprietary. A tree lists paths; it is cited for that and for nothing about the vendor's offerings.
- https://github.com/kubeflow/katib recorded 2026-09-15
Repository page for kubeflow/katib: Apache-2.0 license, public and unarchived, described as 'Automated Machine Learning on Kubernetes'. Establishes the license and that the source is public.
- https://raw.githubusercontent.com/kubeflow/community/master/committee-steering/charter.md recorded 2026-10-07
The Kubeflow Steering Committee charter: 'The Kubeflow Steering Committee (KSC) is the governing body of the Kubeflow project', and 'No more than one (1) seats may be held by employees of the same organization'.
Adoption
2 low confidenceKatib ships as container images and a Helm chart rather than through a package registry, so GitHub stars are the only adoption signal available. A star is not a use, so the signal shows attention rather than real usage.
- https://github.com/kubeflow/katib recorded 2026-09-15
Repository page showing 1,701 GitHub stars for kubeflow/katib.
Capability
3 medium confidenceKatib treats a tuning trial as a first-class job: it decides how many trials run in parallel and when they stop, but leaves where each trial's pods land to the cluster scheduler. That keeps it level with Kubeflow Trainer rather than higher.
- https://github.com/kubeflow/katib recorded 2026-09-15
Repository page and README for kubeflow/katib describing Experiments, Trials, parallel trial execution and objective-metric collection.
Verified 2026-09-15