Comparison
katib vs hyperband
Verdict
Pick katib if katib is an automated machine learning solution for Kubernetes environments, focused on hyperparameter tuning and neural architecture search; pick hyperband if hyperband optimizes hyperparameters quickly with an efficient bandit-based approach, supporting several models from scikit-learn and polylearn.
Markdown twin · katib alternatives · hyperband alternatives
GraphCanon updated 2w
Trust & integrity
| Signal | katib | hyperband |
|---|---|---|
| Maintenance | Very active (0d since push) As of 2w · github_public_v1 | Dormant (2910d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- katib
- Automated Machine Learning on Kubernetes
- hyperband
- Tuning hyperparams fast with Hyperband
Stars
- katib
- 1.7k
- hyperband
- 599
Forks
- katib
- 534
- hyperband
- 73
Open issues
- katib
- 106
- hyperband
- 9
Language
- katib
- Python
- hyperband
- Python
Adopt for
- katib
- Katib is an automated machine learning solution for Kubernetes environments, focused on hyperparameter tuning and neural architecture search.
- hyperband
- Hyperband optimizes hyperparameters quickly with an efficient bandit-based approach, supporting several models from scikit-learn and polylearn.
Persona
- katib
- -
- hyperband
- -
Runtime
- katib
- -
- hyperband
- -
License
- katib
- Apache-2.0
- hyperband
- Other
Last pushed
- katib
- Aug 4, 2026
- hyperband
- Aug 15, 2018
Categories
- katib
- Evaluation & Observability, Model Training
- hyperband
- Model Training
Trust and health
Maintenance
- katib
- Very active (96%)
- hyperband
- Dormant (18%)
Days since push
- katib
- 0d
- hyperband
- 2910d
Open issues (now)
- katib
- 106
- hyperband
- 9
Owner type
- katib
- Organization
- hyperband
- User
OSV dependency advisories
- katib
- Published findings
- hyperband
- No lockfile (source not queried)
Full report
- katib
- Trust report
- hyperband
- Trust report
Shared compatibility
- Python · katib: Python runtime · hyperband: Python runtime
Choose katib if…
- License: katib is Apache-2.0, hyperband is Other.
- Tags unique to katib: ai, automl, hyperparameter-tuning, neural-architecture-search.
- Also covers Evaluation & Observability.
- When you need to perform comprehensive hyperparameter tuning tasks within a Kubernetes cluster setup.
When NOT to use katib
- Avoid using Katib if you do not have a Kubernetes cluster setup, as it heavily relies on this platform for operation.
- If your project's requirements do not extend beyond simple model training tasks and you lack the resources to support a complex CI/CD pipeline like Kubeflow with Katib.
- Not suitable when working in environments with strict constraints preventing the use of open-source tools under Apache-2.0 licenses.
Choose hyperband if…
- License: hyperband is Other, katib is Apache-2.0.
- Tags unique to hyperband: classification, gradient-boosting, hyperparameter-optimization, machine-learning.
- Use Hyperband when you need fast optimization of hyperparameters for classifiers such as gradient boosting or regressors like factorization machines from polylearn.
When NOT to use hyperband
- Avoid Hyperband if you require custom data formats that differ significantly from scikit-learn conventions, as this will necessitate extensive customization of the load_data modules.
- Do not use Hyperband when the models you need for hyperparameter tuning are not among the eight pre-supported models; additional support is required outside what comes built-in.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (kubeflow/katib) · observed Aug 4, 2026
- GitHub forks (kubeflow/katib) · observed Aug 4, 2026
- Last push (kubeflow/katib) · observed Aug 4, 2026
- License file (Apache-2.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (zygmuntz/hyperband) · observed Aug 4, 2026
- GitHub forks (zygmuntz/hyperband) · observed Aug 4, 2026
- Last push (zygmuntz/hyperband) · observed Aug 15, 2018
- License file (Other) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: katib 1.7k · hyperband 599 (synced Aug 4, 2026).
Common questions
- What is the difference between katib and hyperband?
- katib: Automated Machine Learning on Kubernetes. hyperband: Tuning hyperparams fast with Hyperband. See the comparison table for live GitHub stats and shared categories.
- When should I choose katib over hyperband?
- Choose katib over hyperband when License: katib is Apache-2.0, hyperband is Other; Tags unique to katib: ai, automl, hyperparameter-tuning, neural-architecture-search; Also covers Evaluation & Observability; When you need to perform comprehensive hyperparameter tuning tasks within a Kubernetes cluster setup.
- When should I choose hyperband over katib?
- Choose hyperband over katib when License: hyperband is Other, katib is Apache-2.0; Tags unique to hyperband: classification, gradient-boosting, hyperparameter-optimization, machine-learning; Use Hyperband when you need fast optimization of hyperparameters for classifiers such as gradient boosting or regressors like factorization machines from polylearn.
- When should I avoid katib?
- Avoid using Katib if you do not have a Kubernetes cluster setup, as it heavily relies on this platform for operation. If your project's requirements do not extend beyond simple model training tasks and you lack the resources to support a complex CI/CD pipeline like Kubeflow with Katib. Not suitable when working in environments with strict constraints preventing the use of open-source tools under Apache-2.0 licenses.
- When should I avoid hyperband?
- Avoid Hyperband if you require custom data formats that differ significantly from scikit-learn conventions, as this will necessitate extensive customization of the load_data modules. Do not use Hyperband when the models you need for hyperparameter tuning are not among the eight pre-supported models; additional support is required outside what comes built-in.
- Is katib or hyperband more popular on GitHub?
- katib has more GitHub stars (1,694 vs 599). Stars measure visibility, not whether either tool fits your constraints.
- Are katib and hyperband open source?
- Yes - both are open-source projects on GitHub (katib: Apache-2.0, hyperband: Other).
- Where can I find alternatives to katib or hyperband?
- GraphCanon lists graph-backed alternatives at katib alternatives and hyperband alternatives (katib markdown twin, hyperband markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, katib or hyperband?
- katib: Very active. hyperband: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for katib and hyperband?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: katib trust report; hyperband trust report.