--- title: "Awesome-AutoDL vs katib" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/d-x-y-awesome-autodl-vs-kubeflow-katib" tools: ["d-x-y-awesome-autodl", "kubeflow-katib"] --- # Awesome-AutoDL vs katib *GraphCanon updated Aug 4, 2026* ## 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. [Awesome-AutoDL](https://github.com/D-X-Y/Awesome-AutoDL) reports 2.3k GitHub stars, 319 forks, and 2 open issues, last pushed Sep 26, 2022. [katib](https://www.kubeflow.org/docs/components/katib) has 1.7k stars, 534 forks, and 106 open issues, last pushed Aug 4, 2026. Figures are from public GitHub metadata via [Awesome-AutoDL's repository](https://github.com/D-X-Y/Awesome-AutoDL) and [katib's repository](https://github.com/kubeflow/katib). | | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | [katib](/tools/kubeflow-katib.md) | | --- | --- | --- | | Tagline | Curated list of automated deep learning resources covering AutoDL, NAS, HPO | Automated Machine Learning on Kubernetes | | Stars | 2,339 | 1,694 | | Forks | 319 | 534 | | Open issues | 2 | 106 | | Language | Python | Python | | Adopt for | A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. | Katib is an automated machine learning solution for Kubernetes environments, focused on hyperparameter tuning and neural architecture search. | | Persona | - | - | | Runtime | - | - | | License | MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice. | Apache-2.0 | | Categories | Developer Tools, Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | [katib](/tools/kubeflow-katib.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 1408d | 0d | | Open issues (now) | 2 | 106 | | Owner type | User | Organization | | Full report | [trust report](/tools/d-x-y-awesome-autodl/trust.md) | [trust report](/tools/kubeflow-katib/trust.md) | ## Decision facts: Awesome-AutoDL - **Adopt for:** A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. - **License detail:** MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice. ## Decision facts: katib - **Adopt for:** Katib is an automated machine learning solution for Kubernetes environments, focused on hyperparameter tuning and neural architecture search. ## Choose when ### 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). ### 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 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 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. ## 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](/tools/d-x-y-awesome-autodl/alternatives) and [katib alternatives](/tools/kubeflow-katib/alternatives) ([Awesome-AutoDL markdown twin](/tools/d-x-y-awesome-autodl/alternatives.md), [katib markdown twin](/tools/kubeflow-katib/alternatives.md)), 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](/compare/d-x-y-awesome-autodl-vs-kubeflow-katib.md) 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](/tools/d-x-y-awesome-autodl/trust); [katib trust report](/tools/kubeflow-katib/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=d-x-y-awesome-autodl`](/api/graphcanon/graph?tool=d-x-y-awesome-autodl) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_