Comparison
devol vs keras-tuner
Verdict
Pick devol if devolution of neural network architectures through genetic algorithms in Keras for automating design; pick keras-tuner if kerasTuner is a hyperparameter tuning library for Keras focused on Python and TensorFlow environments.
Markdown twin · devol alternatives · keras-tuner alternatives
GraphCanon updated Oct 2, 2026
13views this month
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Trust & integrity
| Signal | devol | keras-tuner |
|---|---|---|
| Maintenance | Dormant (1223d since push) As of Sep 30, 2026 · github_public_v1 | Slowing (304d since push) As of Oct 2, 2026 · github_public_v1 |
| Provenance | Not a fork · Personal account As of Sep 30, 2026 · github_public_v1 | Not a fork · Organization account As of Oct 2, 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
- devol
- Genetic neural architecture search for deep learning models
- keras-tuner
- A Hyperparameter Tuning Library for Keras
Stars
- devol
- 953
- keras-tuner
- 2.9k
Forks
- devol
- 115
- keras-tuner
- 406
Open issues
- devol
- 7
- keras-tuner
- 240
Language
- devol
- Python
- keras-tuner
- Python
Adopt for
- devol
- Devolution of neural network architectures through genetic algorithms in Keras for automating design.
- keras-tuner
- KerasTuner is a hyperparameter tuning library for Keras focused on Python and TensorFlow environments.
Persona
- devol
- -
- keras-tuner
- -
Runtime
- devol
- -
- keras-tuner
- -
License
- devol
- MIT
- keras-tuner
- Apache-2.0
Last pushed
- devol
- May 25, 2023
- keras-tuner
- Dec 1, 2025
Categories
- devol
- Model Training
- keras-tuner
- Model Training
Trust and health
Maintenance
- devol
- Dormant (18%)
- keras-tuner
- Slowing (36%)
Days since push
- devol
- 1223d
- keras-tuner
- 304d
Open issues (now)
- devol
- 7
- keras-tuner
- 240
Stars delta
- devol
- +2 (30d)
- keras-tuner
- -2 (30d)
Owner type
- devol
- User
- keras-tuner
- Organization
Full report
- devol
- Trust report
- keras-tuner
- Trust report
Choose devol if…
- License: devol is MIT, keras-tuner is Apache-2.0.
- 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, neural-architecture-search.
- 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.
Choose keras-tuner if…
- License: keras-tuner is Apache-2.0, devol is MIT.
- Tags unique to keras-tuner: hyperparameter-optimization, tensorflow.
- - Use when you are working with TensorFlow 2.0+ and Python 3.8+, specifically if your project relies heavily on these technologies.
When NOT to use keras-tuner
- - Avoid if your current machine-learning stack does not include Python and TensorFlow 2.0+ as primary dependencies.
- - Not recommended if you seek hyperparameter tuning solutions that are more generic or compatible with a wider range of ML frameworks beyond Keras.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- 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 (keras-team/keras-tuner) · observed Oct 2, 2026
- GitHub forks (keras-team/keras-tuner) · observed Oct 2, 2026
- Last push (keras-team/keras-tuner) · observed Dec 1, 2025
- License file (Apache-2.0) · observed Oct 2, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: devol 953 · keras-tuner 2.9k (synced Sep 30, 2026).
Common questions
- What is the difference between devol and keras-tuner?
- devol: Genetic neural architecture search for deep learning models. keras-tuner: A Hyperparameter Tuning Library for Keras. See the comparison table for live GitHub stats and shared categories.
- When should I choose devol over keras-tuner?
- Choose devol over keras-tuner when License: devol is MIT, keras-tuner is Apache-2.0; 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, neural-architecture-search; 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 choose keras-tuner over devol?
- Choose keras-tuner over devol when License: keras-tuner is Apache-2.0, devol is MIT; Tags unique to keras-tuner: hyperparameter-optimization, tensorflow; - Use when you are working with TensorFlow 2.0+ and Python 3.8+, specifically if your project relies heavily on these technologies.
- 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.
- When should I avoid keras-tuner?
- - Avoid if your current machine-learning stack does not include Python and TensorFlow 2.0+ as primary dependencies. - Not recommended if you seek hyperparameter tuning solutions that are more generic or compatible with a wider range of ML frameworks beyond Keras.
- Is devol or keras-tuner more popular on GitHub?
- keras-tuner has more GitHub stars (2,921 vs 953). Stars measure visibility, not whether either tool fits your constraints.
- Are devol and keras-tuner open source?
- Yes - both are open-source projects on GitHub (devol: MIT, keras-tuner: Apache-2.0).
- Where can I find alternatives to devol or keras-tuner?
- GraphCanon lists graph-backed alternatives at devol alternatives and keras-tuner alternatives (devol markdown twin, keras-tuner 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, devol or keras-tuner?
- devol: Dormant. keras-tuner: Slowing. 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 devol and keras-tuner?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: devol trust report; keras-tuner trust report.