Comparison
Auto-PyTorch vs surogate
Verdict
Pick Auto-PyTorch if auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch; pick surogate if surogate is a C++-based repository that accelerates training and fine-tuning for generative AI models using CUDA on NVIDIA GPUs.
Markdown twin · Auto-PyTorch alternatives · surogate alternatives
GraphCanon updated Sep 30, 2026
10views this month
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Trust & integrity
| Signal | Auto-PyTorch | surogate |
|---|---|---|
| Maintenance | Dormant (903d since push) As of Sep 30, 2026 · github_public_v1 | Very active (0d since push) As of Sep 19, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 30, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 19, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · 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
- surogate
- Training/Fine-tuning at the speed of light
Stars
- Auto-PyTorch
- 2.5k
- surogate
- 813
Forks
- Auto-PyTorch
- 298
- surogate
- 10
Open issues
- Auto-PyTorch
- 75
- surogate
- 6
Language
- Auto-PyTorch
- Python
- surogate
- C++
Adopt for
- Auto-PyTorch
- Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch.
- surogate
- surogate is a C++-based repository that accelerates training and fine-tuning for generative AI models using CUDA on NVIDIA GPUs
Persona
- Auto-PyTorch
- -
- surogate
- -
Runtime
- Auto-PyTorch
- -
- surogate
- -
License
- Auto-PyTorch
- Apache-2.0
- surogate
- Apache-2.0
Last pushed
- Auto-PyTorch
- Apr 9, 2024
- surogate
- Sep 19, 2026
Categories
- Auto-PyTorch
- Data & Retrieval, Model Training
- surogate
- Model Training
Trust and health
Maintenance
- Auto-PyTorch
- Dormant (18%)
- surogate
- Very active (96%)
Days since push
- Auto-PyTorch
- 903d
- surogate
- 0d
Open issues (now)
- Auto-PyTorch
- 75
- surogate
- 6
Stars delta
- Auto-PyTorch
- -1 (30d)
- surogate
- +7 (30d)
OSV dependency advisories
- Auto-PyTorch
- Published findings
- surogate
- No lockfile (source not queried)
Full report
- Auto-PyTorch
- Trust report
- surogate
- Trust report
Shared compatibility
- Python · Auto-PyTorch: Python runtime · surogate: Python runtime
Choose Auto-PyTorch if…
- Auto-PyTorch is primarily Python; surogate is C++.
- Tags unique to Auto-PyTorch: automl, 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 surogate if…
- surogate is primarily C++; Auto-PyTorch is Python.
- Tags unique to surogate: cuda, fine-tuning, generative-ai, llama.
- When needing rapid training and fine-tuning capabilities for generative AI models that take full advantage of NVIDIA GPU acceleration via CUDA.
When NOT to use surogate
- If working in an environment without access to NVIDIA GPUs, as surogate leverages CUDA for its speed optimizations specifically designed for these hardware configurations.
- When looking to use a more accessible language like Python for training and fine-tuning, since surogate is based on C++ which may offer less ease-of-use.
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 Sep 30, 2026
- GitHub forks (automl/Auto-PyTorch) · observed Sep 30, 2026
- Last push (automl/Auto-PyTorch) · observed Apr 9, 2024
- License file (Apache-2.0) · observed Sep 30, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (invergent-ai/surogate) · observed Sep 20, 2026
- GitHub forks (invergent-ai/surogate) · observed Sep 20, 2026
- Last push (invergent-ai/surogate) · observed Sep 19, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Auto-PyTorch 2.5k · surogate 813 (synced Sep 30, 2026).
Common questions
- What is the difference between Auto-PyTorch and surogate?
- Auto-PyTorch: Automatic architecture search and hyperparameter optimization for PyTorch. surogate: Training/Fine-tuning at the speed of light. See the comparison table for live GitHub stats and shared categories.
- When should I choose Auto-PyTorch over surogate?
- Choose Auto-PyTorch over surogate when Auto-PyTorch is primarily Python; surogate is C++; Tags unique to Auto-PyTorch: automl, 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 surogate over Auto-PyTorch?
- Choose surogate over Auto-PyTorch when surogate is primarily C++; Auto-PyTorch is Python; Tags unique to surogate: cuda, fine-tuning, generative-ai, llama; When needing rapid training and fine-tuning capabilities for generative AI models that take full advantage of NVIDIA GPU acceleration via CUDA.
- 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 surogate?
- If working in an environment without access to NVIDIA GPUs, as surogate leverages CUDA for its speed optimizations specifically designed for these hardware configurations. When looking to use a more accessible language like Python for training and fine-tuning, since surogate is based on C++ which may offer less ease-of-use.
- Is Auto-PyTorch or surogate more popular on GitHub?
- Auto-PyTorch has more GitHub stars (2,540 vs 813). Stars measure visibility, not whether either tool fits your constraints.
- Are Auto-PyTorch and surogate open source?
- Yes - both are open-source projects on GitHub (Auto-PyTorch: Apache-2.0, surogate: Apache-2.0).
- Where can I find alternatives to Auto-PyTorch or surogate?
- GraphCanon lists graph-backed alternatives at Auto-PyTorch alternatives and surogate alternatives (Auto-PyTorch markdown twin, surogate 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 surogate?
- Auto-PyTorch: Dormant. surogate: Very active. 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 surogate?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Auto-PyTorch trust report; surogate trust report.