Comparison
Awesome-AutoDL vs katib
Verdict
Pick Awesome-AutoDL if a curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques; pick katib if katib is an automated machine learning solution for Kubernetes environments, focused on hyperparameter tuning and neural architecture search.
Markdown twin · Awesome-AutoDL alternatives · katib alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | Awesome-AutoDL | katib |
|---|---|---|
| Maintenance | Dormant (1408d since push) As of 2w · github_public_v1 | Very active (0d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings 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
- Awesome-AutoDL
- Curated list of automated deep learning resources covering AutoDL, NAS, HPO
- katib
- Automated Machine Learning on Kubernetes
Stars
- Awesome-AutoDL
- 2.3k
- katib
- 1.7k
Forks
- Awesome-AutoDL
- 319
- katib
- 534
Open issues
- Awesome-AutoDL
- 2
- katib
- 106
Language
- Awesome-AutoDL
- Python
- katib
- Python
Adopt for
- Awesome-AutoDL
- A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques.
- katib
- Katib is an automated machine learning solution for Kubernetes environments, focused on hyperparameter tuning and neural architecture search.
Persona
- Awesome-AutoDL
- -
- katib
- -
Runtime
- Awesome-AutoDL
- -
- katib
- -
License
- Awesome-AutoDL
- MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice.
- katib
- Apache-2.0
Last pushed
- Awesome-AutoDL
- Sep 26, 2022
- katib
- Aug 4, 2026
Categories
- Awesome-AutoDL
- Developer Tools, Model Training
- katib
- Evaluation & Observability, Model Training
Trust and health
Maintenance
- Awesome-AutoDL
- Dormant (18%)
- katib
- Very active (96%)
Days since push
- Awesome-AutoDL
- 1408d
- katib
- 0d
Open issues (now)
- Awesome-AutoDL
- 2
- katib
- 106
Owner type
- Awesome-AutoDL
- User
- katib
- Organization
OSV dependency advisories
- Awesome-AutoDL
- No lockfile (source not queried)
- katib
- Published findings
Full report
- Awesome-AutoDL
- Trust report
- katib
- Trust report
Choose Awesome-AutoDL if…
- License: Awesome-AutoDL is MIT, katib is Apache-2.0.
- Tags unique to Awesome-AutoDL: autodl, awesome, deep-learning, hyper-parameter-optimization.
- Also covers Developer Tools.
- Use this resource when you require an exhaustive compilation of AutoDL tools that include Hyper-parameter Optimization (HPO) and Neural Architecture Search (NAS).
When NOT to use Awesome-AutoDL
- Avoid using Awesome-AutoDL if you are looking for hands-on code implementation examples or tutorials specific to each tool mentioned.
- Do not rely on this repository alone for practical use cases in AutoDL without further investigation into the individual libraries listed, as it primarily serves as a reference guide.
Choose katib if…
- License: katib is Apache-2.0, Awesome-AutoDL is MIT.
- Tags unique to katib: ai, hyperparameter-tuning.
- 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (D-X-Y/Awesome-AutoDL) · observed Aug 4, 2026
- GitHub forks (D-X-Y/Awesome-AutoDL) · observed Aug 4, 2026
- Last push (D-X-Y/Awesome-AutoDL) · observed Sep 26, 2022
- License file (MIT) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- 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 on cards: Awesome-AutoDL 2.3k · katib 1.7k (synced Aug 4, 2026).
Common questions
- What is the difference between Awesome-AutoDL and katib?
- Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. katib: Automated Machine Learning on Kubernetes. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-AutoDL over katib?
- Choose Awesome-AutoDL over katib when License: Awesome-AutoDL is MIT, katib is Apache-2.0; Tags unique to Awesome-AutoDL: autodl, awesome, deep-learning, hyper-parameter-optimization; Also covers Developer Tools; Use this resource when you require an exhaustive compilation of AutoDL tools that include Hyper-parameter Optimization (HPO) and Neural Architecture Search (NAS).
- When should I choose katib over Awesome-AutoDL?
- Choose katib over Awesome-AutoDL when License: katib is Apache-2.0, Awesome-AutoDL is MIT; Tags unique to katib: ai, hyperparameter-tuning; Also covers Evaluation & Observability; When you need to perform comprehensive hyperparameter tuning tasks within a Kubernetes cluster setup.
- When should I avoid Awesome-AutoDL?
- Avoid using Awesome-AutoDL if you are looking for hands-on code implementation examples or tutorials specific to each tool mentioned. Do not rely on this repository alone for practical use cases in AutoDL without further investigation into the individual libraries listed, as it primarily serves as a reference guide.
- 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.
- Is Awesome-AutoDL or katib more popular on GitHub?
- Awesome-AutoDL has more GitHub stars (2,339 vs 1,694). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-AutoDL and katib open source?
- Yes - both are open-source projects on GitHub (Awesome-AutoDL: MIT, katib: Apache-2.0).
- Where can I find alternatives to Awesome-AutoDL or katib?
- GraphCanon lists graph-backed alternatives at Awesome-AutoDL alternatives and katib alternatives (Awesome-AutoDL markdown twin, katib 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, Awesome-AutoDL or katib?
- Awesome-AutoDL: Dormant. katib: Very active. 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 Awesome-AutoDL and katib?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-AutoDL trust report; katib trust report.