Comparison
Awesome-AutoDL vs MOE
Verdict
Pick Awesome-AutoDL if a curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques; pick MOE if mOE optimizes real-world metrics via automated black-box processes. It is written in C++.
Markdown twin · Awesome-AutoDL alternatives · MOE alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | Awesome-AutoDL | MOE |
|---|---|---|
| Maintenance | Dormant (1408d since push) As of 2w · github_public_v1 | Archived (1228d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | Published findings 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
- MOE
- A global, black box optimization engine for real world metric optimization
Stars
- Awesome-AutoDL
- 2.3k
- MOE
- 1.3k
Forks
- Awesome-AutoDL
- 319
- MOE
- 139
Open issues
- Awesome-AutoDL
- 2
- MOE
- 175
Language
- Awesome-AutoDL
- Python
- MOE
- C++
Adopt for
- Awesome-AutoDL
- A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques.
- MOE
- MOE optimizes real-world metrics via automated black-box processes. It is written in C++.
Persona
- Awesome-AutoDL
- -
- MOE
- -
Runtime
- Awesome-AutoDL
- -
- MOE
- -
License
- Awesome-AutoDL
- MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice.
- MOE
- Licensed under the Apache License, Version 2.0.
Last pushed
- Awesome-AutoDL
- Sep 26, 2022
- MOE
- Mar 24, 2023
Categories
- Awesome-AutoDL
- Developer Tools, Model Training
- MOE
- Model Training
Trust and health
Maintenance
- Awesome-AutoDL
- Dormant (18%)
- MOE
- Archived (8%)
Days since push
- Awesome-AutoDL
- 1408d
- MOE
- 1228d
Archived on GitHub
- Awesome-AutoDL
- No
- MOE
- Yes
Open issues (now)
- Awesome-AutoDL
- 2
- MOE
- 175
Owner type
- Awesome-AutoDL
- User
- MOE
- Organization
OSV dependency advisories
- Awesome-AutoDL
- No lockfile (source not queried)
- MOE
- Published findings
Full report
- Awesome-AutoDL
- Trust report
- MOE
- Trust report
Choose Awesome-AutoDL if…
- Awesome-AutoDL is primarily Python; MOE is C++.
- License: Awesome-AutoDL is MIT, MOE is Other.
- 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 MOE if…
- MOE is primarily C++; Awesome-AutoDL is Python.
- License: MOE is Other, Awesome-AutoDL is MIT.
- Tags unique to MOE: c++, docker, rest server.
- MOE ships Docker support for self-hosted deployment.
- When you require an optimization engine that operates as a global, isolated system through Docker containers.
When NOT to use MOE
- If your team lacks the knowledge or experience to configure and run Docker environments.
- Not suitable for projects where real-time interaction with optimization processes is needed, as MOE focuses on batch processing scenarios.
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 (YelpArchive/MOE) · observed Aug 4, 2026
- GitHub forks (YelpArchive/MOE) · observed Aug 4, 2026
- Last push (YelpArchive/MOE) · observed Mar 24, 2023
- License file (Other) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-AutoDL 2.3k · MOE 1.3k (synced Aug 4, 2026).
Common questions
- What is the difference between Awesome-AutoDL and MOE?
- Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. MOE: A global, black box optimization engine for real world metric optimization. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-AutoDL over MOE?
- Choose Awesome-AutoDL over MOE when Awesome-AutoDL is primarily Python; MOE is C++; License: Awesome-AutoDL is MIT, MOE is Other; 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 MOE over Awesome-AutoDL?
- Choose MOE over Awesome-AutoDL when MOE is primarily C++; Awesome-AutoDL is Python; License: MOE is Other, Awesome-AutoDL is MIT; Tags unique to MOE: c++, docker, rest server; MOE ships Docker support for self-hosted deployment; When you require an optimization engine that operates as a global, isolated system through Docker containers.
- 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 MOE?
- If your team lacks the knowledge or experience to configure and run Docker environments. Not suitable for projects where real-time interaction with optimization processes is needed, as MOE focuses on batch processing scenarios.
- Is Awesome-AutoDL or MOE more popular on GitHub?
- Awesome-AutoDL has more GitHub stars (2,339 vs 1,321). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-AutoDL and MOE open source?
- Yes - both are open-source projects on GitHub (Awesome-AutoDL: MIT, MOE: Other).
- Where can I find alternatives to Awesome-AutoDL or MOE?
- GraphCanon lists graph-backed alternatives at Awesome-AutoDL alternatives and MOE alternatives (Awesome-AutoDL markdown twin, MOE 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 MOE?
- Awesome-AutoDL: Dormant. MOE: 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 MOE?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-AutoDL trust report; MOE trust report.