Comparison
Awesome-AutoDL vs AutoGL
Verdict
Pick Awesome-AutoDL if a curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques; pick AutoGL if autoGL is an AutoML framework for machine learning on graphs, specializing in automated hyperparameter optimization and neural architecture search for various graph data tasks.
Markdown twin · Awesome-AutoDL alternatives · AutoGL alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | Awesome-AutoDL | AutoGL |
|---|---|---|
| Maintenance | Dormant (1408d since push) As of 2w · github_public_v1 | Slowing (256d since push) As of 3w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 2w · github_public_v1 | Not a fork · Organization account As of 3w · 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
- AutoGL
- AutoML framework & toolkit for machine learning on graphs
Stars
- Awesome-AutoDL
- 2.3k
- AutoGL
- 1.1k
Forks
- Awesome-AutoDL
- 319
- AutoGL
- 123
Open issues
- Awesome-AutoDL
- 2
- AutoGL
- 20
Language
- Awesome-AutoDL
- Python
- AutoGL
- Python
Adopt for
- Awesome-AutoDL
- A curated list of resources and links for Automated Deep Learning including AutoDL, NAS, HPO techniques.
- AutoGL
- AutoGL is an AutoML framework for machine learning on graphs, specializing in automated hyperparameter optimization and neural architecture search for various graph data tasks.
Persona
- Awesome-AutoDL
- -
- AutoGL
- -
Runtime
- Awesome-AutoDL
- -
- AutoGL
- -
License
- Awesome-AutoDL
- MIT license provides flexibility in usage and modification, subject to inclusion of the copyright notice and permission notice.
- AutoGL
- Apache-2.0
Last pushed
- Awesome-AutoDL
- Sep 26, 2022
- AutoGL
- Nov 20, 2025
Categories
- Awesome-AutoDL
- Developer Tools, Model Training
- AutoGL
- Model Training
Trust and health
Maintenance
- Awesome-AutoDL
- Dormant (18%)
- AutoGL
- Slowing (36%)
Days since push
- Awesome-AutoDL
- 1408d
- AutoGL
- 256d
Open issues (now)
- Awesome-AutoDL
- 2
- AutoGL
- 20
Owner type
- Awesome-AutoDL
- User
- AutoGL
- Organization
Full report
- Awesome-AutoDL
- Trust report
- AutoGL
- Trust report
Choose Awesome-AutoDL if…
- License: Awesome-AutoDL is MIT, AutoGL is Apache-2.0.
- Tags unique to Awesome-AutoDL: autodl, awesome, 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 AutoGL if…
- License: AutoGL is Apache-2.0, Awesome-AutoDL is MIT.
- Requirements: Min 8 GB RAM; Requires Python version >= 3.6.0.; Must include a backend library for graph processing; either PyTorch Geometric (>=1.7.0) or Deep Graph Library (DGL, >=0.7.0).; PyTorch version should be >=1.6.0..
- Tags unique to AutoGL: graph-neural-networks, machine-learning, pytorch, pytorch-geometric.
- When you need to automate the process of optimizing hyperparameters and searching through different neural architectures for complex graph-based datasets.
When NOT to use AutoGL
- For scenarios where the dataset does not involve graph structures, as AutoGL is specifically designed to handle such data types, potentially leading to suboptimal results on non-graph datasets.
- If your project relies heavily on frameworks other than PyTorch or backends outside of PyTorch Geometric or Deep Graph Library, considering it may pose integration challenges or inefficiencies.
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 (THUMNLab/AutoGL) · observed Aug 4, 2026
- GitHub forks (THUMNLab/AutoGL) · observed Aug 4, 2026
- Last push (THUMNLab/AutoGL) · observed Nov 20, 2025
- License file (Apache-2.0) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Awesome-AutoDL 2.3k · AutoGL 1.1k (synced Aug 4, 2026).
Common questions
- What is the difference between Awesome-AutoDL and AutoGL?
- Awesome-AutoDL: Curated list of automated deep learning resources covering AutoDL, NAS, HPO. AutoGL: AutoML framework & toolkit for machine learning on graphs. See the comparison table for live GitHub stats and shared categories.
- When should I choose Awesome-AutoDL over AutoGL?
- Choose Awesome-AutoDL over AutoGL when License: Awesome-AutoDL is MIT, AutoGL is Apache-2.0; Tags unique to Awesome-AutoDL: autodl, awesome, 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 AutoGL over Awesome-AutoDL?
- Choose AutoGL over Awesome-AutoDL when License: AutoGL is Apache-2.0, Awesome-AutoDL is MIT; Requirements: Min 8 GB RAM; Requires Python version >= 3.6.0.; Must include a backend library for graph processing; either PyTorch Geometric (>=1.7.0) or Deep Graph Library (DGL, >=0.7.0).; PyTorch version should be >=1.6.0.; Tags unique to AutoGL: graph-neural-networks, machine-learning, pytorch, pytorch-geometric; When you need to automate the process of optimizing hyperparameters and searching through different neural architectures for complex graph-based datasets.
- 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 AutoGL?
- For scenarios where the dataset does not involve graph structures, as AutoGL is specifically designed to handle such data types, potentially leading to suboptimal results on non-graph datasets. If your project relies heavily on frameworks other than PyTorch or backends outside of PyTorch Geometric or Deep Graph Library, considering it may pose integration challenges or inefficiencies.
- Is Awesome-AutoDL or AutoGL more popular on GitHub?
- Awesome-AutoDL has more GitHub stars (2,339 vs 1,138). Stars measure visibility, not whether either tool fits your constraints.
- Are Awesome-AutoDL and AutoGL open source?
- Yes - both are open-source projects on GitHub (Awesome-AutoDL: MIT, AutoGL: Apache-2.0).
- Where can I find alternatives to Awesome-AutoDL or AutoGL?
- GraphCanon lists graph-backed alternatives at Awesome-AutoDL alternatives and AutoGL alternatives (Awesome-AutoDL markdown twin, AutoGL 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 AutoGL?
- Awesome-AutoDL: Dormant. AutoGL: 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 Awesome-AutoDL and AutoGL?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Awesome-AutoDL trust report; AutoGL trust report.