---
title: "Awesome-AutoDL vs scikit-optimize"
type: "comparison"
canonical_url: "https://www.graphcanon.com/compare/d-x-y-awesome-autodl-vs-scikit-optimize-scikit-optimize"
tools: ["d-x-y-awesome-autodl", "scikit-optimize-scikit-optimize"]
---

# Awesome-AutoDL vs scikit-optimize

*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 scikit-optimize if scikit-Optimize is built for minimizing noisy and expensive black-box functions using sequential model-based methods, and it provides a convenient interface with scipy.optimize.

[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. [scikit-optimize](https://scikit-optimize.github.io) has 2.8k stars, 559 forks, and 318 open issues, last pushed Feb 23, 2024. Figures are from public GitHub metadata via [Awesome-AutoDL's repository](https://github.com/D-X-Y/Awesome-AutoDL) and [scikit-optimize's repository](https://github.com/scikit-optimize/scikit-optimize).

| | [Awesome-AutoDL](/tools/d-x-y-awesome-autodl.md) | [scikit-optimize](/tools/scikit-optimize-scikit-optimize.md) |
| --- | --- | --- |
| Tagline | Curated list of automated deep learning resources covering AutoDL, NAS, HPO | Sequential model-based optimization library with scipy.optimize interface |
| Stars | 2,339 | 2,829 |
| Forks | 319 | 559 |
| Open issues | 2 | 318 |
| Language | Python | Python |
| Adopt for | A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques. | Scikit-Optimize is built for minimizing noisy and expensive black-box functions using sequential model-based methods, and it provides a convenient interface with scipy.optimize. |
| Persona | - | - |
| Runtime | - | - |
| License | MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice. | BSD-3-Clause |
| 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) | [scikit-optimize](/tools/scikit-optimize-scikit-optimize.md) |
| --- | --- | --- |
| Maintenance | Dormant (18%) | Archived (8%) |
| Days since push | 1408d | 893d |
| Archived on GitHub | No | Yes |
| Open issues (now) | 2 | 318 |
| Owner type | User | Organization |
| Full report | [trust report](/tools/d-x-y-awesome-autodl/trust.md) | [trust report](/tools/scikit-optimize-scikit-optimize/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: scikit-optimize

- **Adopt for:** Scikit-Optimize is built for minimizing noisy and expensive black-box functions using sequential model-based methods, and it provides a convenient interface with scipy.optimize.

## Choose when

### Choose Awesome-AutoDL if…

- License: Awesome-AutoDL is MIT, scikit-optimize is BSD-3-Clause.
- 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 scikit-optimize if…

- License: scikit-optimize is BSD-3-Clause, Awesome-AutoDL is MIT.
- Tags unique to scikit-optimize: bayesian-optimization, hyperparameter-tuning, machine-learning, scikit-learn.
- Use Scikit-Optimize when dealing with optimization problems where function evaluations are expensive or noisy, making traditional derivative-based approaches less effective.

## 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 scikit-optimize

- Avoid using Scikit-Optimize if your optimization function can be efficiently evaluated with a high number of gradients, as it does not perform gradient-based optimization and could be less efficient.
- Do not select this tool when you need real-time or online learning updates, as its sequential model-based approaches are better suited for batch processing environments.
- Steer clear if the problems you face have analytical solutions or can be easily solved with traditional gradient descent methods, as Scikit-Optimize’s overhead may not be justified.

## Common questions

### What is the difference between Awesome-AutoDL and scikit-optimize?

Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. scikit-optimize: Sequential model-based optimization library with scipy.optimize interface. See the comparison table for live GitHub stats and shared categories.

### When should I choose Awesome-AutoDL over scikit-optimize?

Choose Awesome-AutoDL over scikit-optimize when License: Awesome-AutoDL is MIT, scikit-optimize is BSD-3-Clause; 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 scikit-optimize over Awesome-AutoDL?

Choose scikit-optimize over Awesome-AutoDL when License: scikit-optimize is BSD-3-Clause, Awesome-AutoDL is MIT; Tags unique to scikit-optimize: bayesian-optimization, hyperparameter-tuning, machine-learning, scikit-learn; Use Scikit-Optimize when dealing with optimization problems where function evaluations are expensive or noisy, making traditional derivative-based approaches less effective.

### 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 scikit-optimize?

Avoid using Scikit-Optimize if your optimization function can be efficiently evaluated with a high number of gradients, as it does not perform gradient-based optimization and could be less efficient. Do not select this tool when you need real-time or online learning updates, as its sequential model-based approaches are better suited for batch processing environments. Steer clear if the problems you face have analytical solutions or can be easily solved with traditional gradient descent methods, as Scikit-Optimize’s overhead may not be justified.

### Is Awesome-AutoDL or scikit-optimize more popular on GitHub?

scikit-optimize has more GitHub stars (2,829 vs 2,339). Stars measure visibility, not whether either tool fits your constraints.

### Are Awesome-AutoDL and scikit-optimize open source?

Yes - both are open-source projects on GitHub (Awesome-AutoDL: MIT, scikit-optimize: BSD-3-Clause).

### Where can I find alternatives to Awesome-AutoDL or scikit-optimize?

GraphCanon lists graph-backed alternatives at [Awesome-AutoDL alternatives](/tools/d-x-y-awesome-autodl/alternatives) and [scikit-optimize alternatives](/tools/scikit-optimize-scikit-optimize/alternatives) ([Awesome-AutoDL markdown twin](/tools/d-x-y-awesome-autodl/alternatives.md), [scikit-optimize markdown twin](/tools/scikit-optimize-scikit-optimize/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-scikit-optimize-scikit-optimize.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.

### Which is better maintained, Awesome-AutoDL or scikit-optimize?

Awesome-AutoDL: Dormant. scikit-optimize: Archived. 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 scikit-optimize?

GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-AutoDL trust report](/tools/d-x-y-awesome-autodl/trust); [scikit-optimize trust report](/tools/scikit-optimize-scikit-optimize/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/_