--- title: "Awesome-AutoDL vs hypertunity" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/d-x-y-awesome-autodl-vs-gdikov-hypertunity" tools: ["d-x-y-awesome-autodl", "gdikov-hypertunity"] --- # Awesome-AutoDL vs hypertunity *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 hypertunity if hypertunity is a Python library that facilitates black-box hyperparameter optimisation using techniques such as Bayesian Optimization. [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. [hypertunity](https://hypertunity.readthedocs.io) has 137 stars, 10 forks, and 0 open issues, last pushed Jan 26, 2020. Figures are from public GitHub metadata via [Awesome-AutoDL's repository](https://github.com/D-X-Y/Awesome-AutoDL) and [hypertunity's repository](https://github.com/gdikov/hypertunity). | | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | [hypertunity](/tools/gdikov-hypertunity.md) | | --- | --- | --- | | Tagline | Curated list of automated deep learning resources covering AutoDL, NAS, HPO | A toolset for black-box hyperparameter optimisation | | Stars | 2,339 | 137 | | Forks | 319 | 10 | | Open issues | 2 | 0 | | Language | Python | Python | | Adopt for | A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. | hypertunity is a Python library that facilitates black-box hyperparameter optimisation using techniques such as Bayesian Optimization. | | 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 | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | [hypertunity](/tools/gdikov-hypertunity.md) | | --- | --- | --- | | Days since push | 1408d | 2381d | | Open issues (now) | 2 | 0 | | Full report | [trust report](/tools/d-x-y-awesome-autodl/trust.md) | [trust report](/tools/gdikov-hypertunity/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: hypertunity - **Requirements:** Min 2 GB RAM; Support for SLURM is indicated in the topics, useful for HPC cluster management but not a hard requirement. - **Adopt for:** hypertunity is a Python library that facilitates black-box hyperparameter optimisation using techniques such as Bayesian Optimization. ## Choose when ### Choose Awesome-AutoDL if… - License: Awesome-AutoDL is MIT, hypertunity is Apache-2.0. - Tags unique to Awesome-AutoDL: autodl, automl, awesome, deep-learning. - 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 hypertunity if… - License: hypertunity is Apache-2.0, Awesome-AutoDL is MIT. - Requirements: Min 2 GB RAM; Support for SLURM is indicated in the topics, useful for HPC cluster management but not a hard requirement.. - Tags unique to hypertunity: bayesian-optimization, gpyopt, hyperparameter-optimization, slurm. - When you are working with complex objective functions that are expensive to evaluate, and you need an automated way to optimize your model parameters. ## 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 hypertunity - When the objective function evaluation is inexpensive or fast because hypertunity shines in scenarios where evaluations are costly, offering less benefit if evaluations can be easily repeated. - If your project does not require advanced techniques such as Bayesian Optimization and you seek a simpler method with fewer dependencies. ## Common questions ### What is the difference between Awesome-AutoDL and hypertunity? Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. hypertunity: A toolset for black-box hyperparameter optimisation. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-AutoDL over hypertunity? Choose Awesome-AutoDL over hypertunity when License: Awesome-AutoDL is MIT, hypertunity is Apache-2.0; Tags unique to Awesome-AutoDL: autodl, automl, awesome, deep-learning; 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 hypertunity over Awesome-AutoDL? Choose hypertunity over Awesome-AutoDL when License: hypertunity is Apache-2.0, Awesome-AutoDL is MIT; Requirements: Min 2 GB RAM; Support for SLURM is indicated in the topics, useful for HPC cluster management but not a hard requirement.; Tags unique to hypertunity: bayesian-optimization, gpyopt, hyperparameter-optimization, slurm; When you are working with complex objective functions that are expensive to evaluate, and you need an automated way to optimize your model parameters. ### 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 hypertunity? When the objective function evaluation is inexpensive or fast because hypertunity shines in scenarios where evaluations are costly, offering less benefit if evaluations can be easily repeated. If your project does not require advanced techniques such as Bayesian Optimization and you seek a simpler method with fewer dependencies. ### Is Awesome-AutoDL or hypertunity more popular on GitHub? Awesome-AutoDL has more GitHub stars (2,339 vs 137). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-AutoDL and hypertunity open source? Yes - both are open-source projects on GitHub (Awesome-AutoDL: MIT, hypertunity: Apache-2.0). ### Where can I find alternatives to Awesome-AutoDL or hypertunity? GraphCanon lists graph-backed alternatives at [Awesome-AutoDL alternatives](/tools/d-x-y-awesome-autodl/alternatives) and [hypertunity alternatives](/tools/gdikov-hypertunity/alternatives) ([Awesome-AutoDL markdown twin](/tools/d-x-y-awesome-autodl/alternatives.md), [hypertunity markdown twin](/tools/gdikov-hypertunity/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-gdikov-hypertunity.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-AutoDL or hypertunity? Awesome-AutoDL: Dormant. hypertunity: 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 Awesome-AutoDL and hypertunity? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-AutoDL trust report](/tools/d-x-y-awesome-autodl/trust); [hypertunity trust report](/tools/gdikov-hypertunity/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/_