Comparison
Awesome-AutoDL vs devol
Verdict
Pick Awesome-AutoDL if a curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques; pick devol if devolution of neural network architectures through genetic algorithms in Keras for automating design.
Markdown twin · Awesome-AutoDL alternatives · devol alternatives
GraphCanon updated Oct 4, 2026
7views this month
vs
Trust & integrity
| Signal | Awesome-AutoDL | devol |
|---|---|---|
| Maintenance | Dormant (1469d since push) As of Oct 4, 2026 · github_public_v1 | Dormant (1223d since push) As of Sep 30, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Oct 4, 2026 · github_public_v1 | Not a fork · Personal account As of Sep 30, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- devol
- Genetic neural architecture search for deep learning models
Stars
- Awesome-AutoDL
- 2.3k
- devol
- 953
Forks
- Awesome-AutoDL
- 317
- devol
- 115
Open issues
- Awesome-AutoDL
- 2
- devol
- 7
Language
- Awesome-AutoDL
- Python
- devol
- Python
Adopt for
- Awesome-AutoDL
- A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques.
- devol
- Devolution of neural network architectures through genetic algorithms in Keras for automating design.
Persona
- Awesome-AutoDL
- -
- devol
- -
Runtime
- Awesome-AutoDL
- -
- devol
- -
License
- Awesome-AutoDL
- MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice.
- devol
- MIT
Last pushed
- Awesome-AutoDL
- Sep 26, 2022
- devol
- May 25, 2023
Categories
- Awesome-AutoDL
- Developer Tools, Model Training
- devol
- Model Training
Trust and health
Days since push
- Awesome-AutoDL
- 1469d
- devol
- 1223d
Open issues (now)
- Awesome-AutoDL
- 2
- devol
- 7
Stars delta
- Awesome-AutoDL
- +5 (30d)
- devol
- +2 (30d)
Full report
- Awesome-AutoDL
- Trust report
- devol
- Trust report
Choose Awesome-AutoDL if…
- Tags unique to Awesome-AutoDL: autodl, awesome, hyper-parameter-optimization, nas.
- 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 devol if…
- Pricing: Available under MIT license meaning it is free for personal and commercial use but the author cannot be held responsible or liable from damages caused by using DEvol..
- Tags unique to devol: computer-vision, genetic-algorithm, keras, machine-learning.
- Use DEvol when you need an early proof-of-concept tool to automate the design of neural network architectures with limited parameters, focusing specifically on classification problems.
When NOT to use devol
- Avoid using DEvol in situations requiring deep or highly complex architectures due to the significant computational expense associated with evolutionary search over such a large parameter space.
- Do not use if you lack the infrastructure for parallel processing or do not want to optimize for shorter training epochs, as this can affect model accuracy and fitness evaluations.
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 Oct 4, 2026
- GitHub forks (D-X-Y/Awesome-AutoDL) · observed Oct 4, 2026
- Last push (D-X-Y/Awesome-AutoDL) · observed Sep 26, 2022
- License file (MIT) · observed Oct 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (joeddav/devol) · observed Sep 30, 2026
- GitHub forks (joeddav/devol) · observed Sep 30, 2026
- Last push (joeddav/devol) · observed May 25, 2023
- License file (MIT) · observed Sep 30, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-AutoDL 2.3k · devol 953 (synced Oct 4, 2026).
Common questions
- What is the difference between Awesome-AutoDL and devol?
- Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. devol: Genetic neural architecture search for deep learning models. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-AutoDL over devol?
- Choose Awesome-AutoDL over devol when Tags unique to Awesome-AutoDL: autodl, awesome, hyper-parameter-optimization, nas; 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 devol over Awesome-AutoDL?
- Choose devol over Awesome-AutoDL when Pricing: Available under MIT license meaning it is free for personal and commercial use but the author cannot be held responsible or liable from damages caused by using DEvol.; Tags unique to devol: computer-vision, genetic-algorithm, keras, machine-learning; Use DEvol when you need an early proof-of-concept tool to automate the design of neural network architectures with limited parameters, focusing specifically on classification problems.
- 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 devol?
- Avoid using DEvol in situations requiring deep or highly complex architectures due to the significant computational expense associated with evolutionary search over such a large parameter space. Do not use if you lack the infrastructure for parallel processing or do not want to optimize for shorter training epochs, as this can affect model accuracy and fitness evaluations.
- Is Awesome-AutoDL or devol more popular on GitHub?
- Awesome-AutoDL has more GitHub stars (2,344 vs 953). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-AutoDL and devol open source?
- Yes - both are open-source projects on GitHub (Awesome-AutoDL: MIT, devol: MIT).
- Where can I find alternatives to Awesome-AutoDL or devol?
- GraphCanon lists graph-backed alternatives at Awesome-AutoDL alternatives and devol alternatives (Awesome-AutoDL markdown twin, devol 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 devol?
- Awesome-AutoDL: Dormant. devol: 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 devol?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-AutoDL trust report; devol trust report.