Comparison
Auto-PyTorch vs AutoGL
Verdict
Pick Auto-PyTorch if auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch; 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 · Auto-PyTorch alternatives · AutoGL alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | Auto-PyTorch | AutoGL |
|---|---|---|
| Maintenance | Dormant (846d since push) As of 2w · github_public_v1 | Slowing (256d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization 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
- Auto-PyTorch
- Automatic architecture search and hyperparameter optimization for PyTorch
- AutoGL
- AutoML framework & toolkit for machine learning on graphs
Stars
- Auto-PyTorch
- 2.5k
- AutoGL
- 1.1k
Forks
- Auto-PyTorch
- 303
- AutoGL
- 123
Open issues
- Auto-PyTorch
- 75
- AutoGL
- 20
Language
- Auto-PyTorch
- Python
- AutoGL
- Python
Adopt for
- Auto-PyTorch
- Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch.
- 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
- Auto-PyTorch
- -
- AutoGL
- -
Runtime
- Auto-PyTorch
- -
- AutoGL
- -
License
- Auto-PyTorch
- Apache-2.0
- AutoGL
- Apache-2.0
Last pushed
- Auto-PyTorch
- Apr 9, 2024
- AutoGL
- Nov 20, 2025
Categories
- Auto-PyTorch
- Data & Retrieval, Model Training
- AutoGL
- Model Training
Trust and health
Maintenance
- Auto-PyTorch
- Dormant (18%)
- AutoGL
- Slowing (36%)
Days since push
- Auto-PyTorch
- 846d
- AutoGL
- 256d
Open issues (now)
- Auto-PyTorch
- 75
- AutoGL
- 20
OSV dependency advisories
- Auto-PyTorch
- Published findings
- AutoGL
- No lockfile (source not queried)
Full report
- Auto-PyTorch
- Trust report
- AutoGL
- Trust report
Shared compatibility
- Python · Auto-PyTorch: Python runtime · AutoGL: Python runtime
Choose Auto-PyTorch if…
- Tags unique to Auto-PyTorch: tabular-data, time-series-forecasting.
- Also covers Data & Retrieval.
- Auto-PyTorch ships Docker support for self-hosted deployment.
- Use when you need to automate both architectural searches and hyperparameter tuning specifically for PyTorch-based deep learning models.
When NOT to use Auto-PyTorch
- Avoid using it if your AI development focuses on frameworks other than PyTorch.
- Do not use when the requirements do not involve deep learning models or you are not interested in automating architecture search and hyperparameter tuning.
Choose AutoGL if…
- 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, hyper-parameter-optimization, machine-learning, neural-architecture-search.
- 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 (automl/Auto-PyTorch) · observed Aug 4, 2026
- GitHub forks (automl/Auto-PyTorch) · observed Aug 4, 2026
- Last push (automl/Auto-PyTorch) · observed Apr 9, 2024
- 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 (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: Auto-PyTorch 2.5k · AutoGL 1.1k (synced Aug 4, 2026).
Common questions
- What is the difference between Auto-PyTorch and AutoGL?
- Auto-PyTorch: Automatic architecture search and hyperparameter optimization for PyTorch. AutoGL: AutoML framework & toolkit for machine learning on graphs. See the comparison table for live GitHub stats and shared categories.
- When should I choose Auto-PyTorch over AutoGL?
- Choose Auto-PyTorch over AutoGL when Tags unique to Auto-PyTorch: tabular-data, time-series-forecasting; Also covers Data & Retrieval; Auto-PyTorch ships Docker support for self-hosted deployment; Use when you need to automate both architectural searches and hyperparameter tuning specifically for PyTorch-based deep learning models.
- When should I choose AutoGL over Auto-PyTorch?
- Choose AutoGL over Auto-PyTorch when 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, hyper-parameter-optimization, machine-learning, neural-architecture-search; 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 Auto-PyTorch?
- Avoid using it if your AI development focuses on frameworks other than PyTorch. Do not use when the requirements do not involve deep learning models or you are not interested in automating architecture search and hyperparameter tuning.
- 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 Auto-PyTorch or AutoGL more popular on GitHub?
- Auto-PyTorch has more GitHub stars (2,541 vs 1,138). Stars measure visibility, not whether either tool fits your constraints.
- Are Auto-PyTorch and AutoGL open source?
- Yes - both are open-source projects on GitHub (Auto-PyTorch: Apache-2.0, AutoGL: Apache-2.0).
- Where can I find alternatives to Auto-PyTorch or AutoGL?
- GraphCanon lists graph-backed alternatives at Auto-PyTorch alternatives and AutoGL alternatives (Auto-PyTorch 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, Auto-PyTorch or AutoGL?
- Auto-PyTorch: 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 Auto-PyTorch and AutoGL?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Auto-PyTorch trust report; AutoGL trust report.