Comparison
autoai vs Awesome-AutoDL
Verdict
Pick autoai if python based framework for automated machine learning focused on numerical data, providing model search, hyper-parameter tuning, and Jupyter Notebook code generation; pick Awesome-AutoDL if a curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques.
Markdown twin · autoai alternatives · Awesome-AutoDL alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | autoai | Awesome-AutoDL |
|---|---|---|
| Maintenance | Dormant (496d since push) As of 2w · github_public_v1 | Dormant (1408d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Personal account As of 2w · github_public_v1 |
| OSV dependency advisories | Published findings 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
- autoai
- Python based framework for Automatic AI enabling model search, hyper-parameter tuning and Jupyter Notebook code generation
- Awesome-AutoDL
- Curated list of automated deep learning resources covering AutoDL, NAS, HPO
Stars
- autoai
- 186
- Awesome-AutoDL
- 2.3k
Forks
- autoai
- 46
- Awesome-AutoDL
- 319
Open issues
- autoai
- 9
- Awesome-AutoDL
- 2
Language
- autoai
- Python
- Awesome-AutoDL
- Python
Adopt for
- autoai
- Python based framework for automated machine learning focused on numerical data, providing model search, hyper-parameter tuning, and Jupyter Notebook code generation.
- Awesome-AutoDL
- A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques.
Persona
- autoai
- -
- Awesome-AutoDL
- -
Runtime
- autoai
- -
- Awesome-AutoDL
- -
License
- autoai
- Apache-2.0
- Awesome-AutoDL
- MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice.
Last pushed
- autoai
- Mar 25, 2025
- Awesome-AutoDL
- Sep 26, 2022
Categories
- autoai
- Model Training
- Awesome-AutoDL
- Developer Tools, Model Training
Trust and health
Days since push
- autoai
- 496d
- Awesome-AutoDL
- 1408d
Open issues (now)
- autoai
- 9
- Awesome-AutoDL
- 2
Owner type
- autoai
- Organization
- Awesome-AutoDL
- User
OSV dependency advisories
- autoai
- Published findings
- Awesome-AutoDL
- No lockfile (source not queried)
Full report
- autoai
- Trust report
- Awesome-AutoDL
- Trust report
Choose autoai if…
- License: autoai is Apache-2.0, Awesome-AutoDL is MIT.
- Tags unique to autoai: ai, autoai, codegen, machine-learning.
- Use AutoAI when you need a tool that can handle both regression and classification tasks specifically over numerical datasets.
When NOT to use autoai
- Avoid using AutoAI if your dataset includes non-numerical data exclusively as the framework is tailored for numerical data processing.
- Do not use if generating model training scripts in formats other than Jupyter Notebooks is required, as this tool only supports Python code output within a Jupyter format.
Choose Awesome-AutoDL if…
- License: Awesome-AutoDL is MIT, autoai is Apache-2.0.
- 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (blobcity/autoai) · observed Aug 4, 2026
- GitHub forks (blobcity/autoai) · observed Aug 4, 2026
- Last push (blobcity/autoai) · observed Mar 25, 2025
- License file (Apache-2.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- 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 on cards: autoai 186 · Awesome-AutoDL 2.3k (synced Aug 4, 2026).
Common questions
- What is the difference between autoai and Awesome-AutoDL?
- autoai: Python based framework for Automatic AI enabling model search, hyper-parameter tuning and Jupyter Notebook code generation. Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. See the comparison table for live GitHub stats and shared categories.
- When should I choose autoai over Awesome-AutoDL?
- Choose autoai over Awesome-AutoDL when License: autoai is Apache-2.0, Awesome-AutoDL is MIT; Tags unique to autoai: ai, autoai, codegen, machine-learning; Use AutoAI when you need a tool that can handle both regression and classification tasks specifically over numerical datasets.
- When should I choose Awesome-AutoDL over autoai?
- Choose Awesome-AutoDL over autoai when License: Awesome-AutoDL is MIT, autoai is Apache-2.0; 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 avoid autoai?
- Avoid using AutoAI if your dataset includes non-numerical data exclusively as the framework is tailored for numerical data processing. Do not use if generating model training scripts in formats other than Jupyter Notebooks is required, as this tool only supports Python code output within a Jupyter format.
- 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.
- Is autoai or Awesome-AutoDL more popular on GitHub?
- Awesome-AutoDL has more GitHub stars (2,339 vs 186). Stars measure visibility, not whether either tool fits your constraints.
- Are autoai and Awesome-AutoDL open source?
- Yes - both are open-source projects on GitHub (autoai: Apache-2.0, Awesome-AutoDL: MIT).
- Where can I find alternatives to autoai or Awesome-AutoDL?
- GraphCanon lists graph-backed alternatives at autoai alternatives and Awesome-AutoDL alternatives (autoai markdown twin, Awesome-AutoDL 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, autoai or Awesome-AutoDL?
- autoai: Dormant. Awesome-AutoDL: 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 autoai and Awesome-AutoDL?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: autoai trust report; Awesome-AutoDL trust report.