Comparison
Awesome-AutoDL vs penzai
Verdict
Pick Awesome-AutoDL if a curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques; pick penzai if penzai supports fine-tuning and interpretability features in neural network research through JAX.
Markdown twin · Awesome-AutoDL alternatives · penzai alternatives
GraphCanon updated 1d
vs
Trust & integrity
| Signal | Awesome-AutoDL | penzai |
|---|---|---|
| Maintenance | Dormant (1408d since push) As of 3w · github_public_v1 | Dormant (427d since push) As of 1d · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · github_public_v1 | Not a fork · Organization account As of 1d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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
- penzai
- A JAX research toolkit for building, editing, and visualizing neural networks.
Stars
- Awesome-AutoDL
- 2.3k
- penzai
- 1.9k
Forks
- Awesome-AutoDL
- 319
- penzai
- 70
Open issues
- Awesome-AutoDL
- 2
- penzai
- 21
Language
- Awesome-AutoDL
- Python
- penzai
- Python
Adopt for
- Awesome-AutoDL
- A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques.
- penzai
- Penzai supports fine-tuning and interpretability features in neural network research through JAX.
Persona
- Awesome-AutoDL
- -
- penzai
- -
Runtime
- Awesome-AutoDL
- -
- penzai
- -
License
- Awesome-AutoDL
- MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice.
- penzai
- Penzai operates under an Apache-2.0 license, offering permissive rights for software use, modification, and distribution.
Last pushed
- Awesome-AutoDL
- Sep 26, 2022
- penzai
- Jun 22, 2025
Categories
- Awesome-AutoDL
- Developer Tools, Model Training
- penzai
- Model Training
Trust and health
Days since push
- Awesome-AutoDL
- 1408d
- penzai
- 427d
Open issues (now)
- Awesome-AutoDL
- 2
- penzai
- 21
Stars delta
- Awesome-AutoDL
- Unknown
- penzai
- +9 (30d)
Open issues delta
- Awesome-AutoDL
- Unknown
- penzai
- +4 (30d)
Owner type
- Awesome-AutoDL
- User
- penzai
- Organization
Full report
- Awesome-AutoDL
- Trust report
- penzai
- Trust report
Choose Awesome-AutoDL if…
- License: Awesome-AutoDL is MIT, penzai 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 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 penzai if…
- License: penzai is Apache-2.0, Awesome-AutoDL is MIT.
- Requirements: Ensure compatibility with Python and JAX libraries as they are crucial for leveraging Penzai's toolkit..
- Tags unique to penzai: fine-tuning, interpretability, jax, neural-networks.
- When your AI development tasks require detailed visualization capabilities for neural networks, as Penzai integrates advanced visual components tailored to these needs.
When NOT to use penzai
- Avoid if you are strictly working with frameworks that do not support or are incompatible with JAX, as Penzai is specifically designed for use within the JAX ecosystem.
- Do not choose Penzai if your project requires a focus on backend model deployment rather than research-oriented functionalities like visualization and interpretability.
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 Aug 4, 2026
- GitHub forks (D-X-Y/Awesome-AutoDL) · observed Aug 4, 2026
- Last push (D-X-Y/Awesome-AutoDL) · observed Sep 26, 2022
- License file (MIT) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (google-deepmind/penzai) · observed Aug 24, 2026
- GitHub forks (google-deepmind/penzai) · observed Aug 24, 2026
- Last push (google-deepmind/penzai) · observed Jun 22, 2025
- License file (Apache-2.0) · observed Aug 24, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-AutoDL 2.3k · penzai 1.9k (synced Aug 4, 2026).
Common questions
- What is the difference between Awesome-AutoDL and penzai?
- Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. penzai: A JAX research toolkit for building, editing, and visualizing neural networks.. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-AutoDL over penzai?
- Choose Awesome-AutoDL over penzai when License: Awesome-AutoDL is MIT, penzai 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 penzai over Awesome-AutoDL?
- Choose penzai over Awesome-AutoDL when License: penzai is Apache-2.0, Awesome-AutoDL is MIT; Requirements: Ensure compatibility with Python and JAX libraries as they are crucial for leveraging Penzai's toolkit.; Tags unique to penzai: fine-tuning, interpretability, jax, neural-networks; When your AI development tasks require detailed visualization capabilities for neural networks, as Penzai integrates advanced visual components tailored to these needs.
- 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 penzai?
- Avoid if you are strictly working with frameworks that do not support or are incompatible with JAX, as Penzai is specifically designed for use within the JAX ecosystem. Do not choose Penzai if your project requires a focus on backend model deployment rather than research-oriented functionalities like visualization and interpretability.
- Is Awesome-AutoDL or penzai more popular on GitHub?
- Awesome-AutoDL has more GitHub stars (2,339 vs 1,901). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-AutoDL and penzai open source?
- Yes - both are open-source projects on GitHub (Awesome-AutoDL: MIT, penzai: Apache-2.0).
- Where can I find alternatives to Awesome-AutoDL or penzai?
- GraphCanon lists graph-backed alternatives at Awesome-AutoDL alternatives and penzai alternatives (Awesome-AutoDL markdown twin, penzai 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 penzai?
- Awesome-AutoDL: Dormant. penzai: 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 penzai?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-AutoDL trust report; penzai trust report.