--- title: "Auto-PyTorch vs pytorch-meta" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automl-auto-pytorch-vs-tristandeleu-pytorch-meta" tools: ["automl-auto-pytorch", "tristandeleu-pytorch-meta"] --- # Auto-PyTorch vs pytorch-meta *GraphCanon updated Aug 4, 2026* ## Verdict Pick Auto-PyTorch if auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch; pick pytorch-meta if pyTorch-Meta focuses on facilitating few-shot learning and meta-learning with PyTorch, offering extensions and data-loaders specifically for these tasks. [Auto-PyTorch](https://github.com/automl/Auto-PyTorch) reports 2.5k GitHub stars, 303 forks, and 75 open issues, last pushed Apr 9, 2024. [pytorch-meta](https://tristandeleu.github.io/pytorch-meta/) has 2.1k stars, 264 forks, and 61 open issues, last pushed Jul 17, 2023. Figures are from public GitHub metadata via [Auto-PyTorch's repository](https://github.com/automl/Auto-PyTorch) and [pytorch-meta's repository](https://github.com/tristandeleu/pytorch-meta). | | [Auto-PyTorch](/tools/automl-auto-pytorch.md) | [pytorch-meta](/tools/tristandeleu-pytorch-meta.md) | | --- | --- | --- | | Tagline | Automatic architecture search and hyperparameter optimization for PyTorch | Extensions and data-loaders for few-shot learning & meta-learning in PyTorch | | Stars | 2,541 | 2,062 | | Forks | 303 | 264 | | Open issues | 75 | 61 | | Language | Python | Python | | Adopt for | Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch. | PyTorch-Meta focuses on facilitating few-shot learning and meta-learning with PyTorch, offering extensions and data-loaders specifically for these tasks. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Data & Retrieval, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Auto-PyTorch](/tools/automl-auto-pytorch.md) | [pytorch-meta](/tools/tristandeleu-pytorch-meta.md) | | --- | --- | --- | | Days since push | 846d | 1113d | | Open issues (now) | 75 | 61 | | Owner type | Organization | User | | Full report | [trust report](/tools/automl-auto-pytorch/trust.md) | [trust report](/tools/tristandeleu-pytorch-meta/trust.md) | ## Shared compatibility - **Python**: [Auto-PyTorch](/tools/automl-auto-pytorch.md) - Python runtime; [pytorch-meta](/tools/tristandeleu-pytorch-meta.md) - Python runtime ## Decision facts: Auto-PyTorch - **Adopt for:** Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch. ## Decision facts: pytorch-meta - **Adopt for:** PyTorch-Meta focuses on facilitating few-shot learning and meta-learning with PyTorch, offering extensions and data-loaders specifically for these tasks. ## Choose when ### Choose Auto-PyTorch if… - License: Auto-PyTorch is Apache-2.0, pytorch-meta is MIT. - Tags unique to Auto-PyTorch: automl, deep-learning, 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. ### Choose pytorch-meta if… - License: pytorch-meta is MIT, Auto-PyTorch is Apache-2.0. - Tags unique to pytorch-meta: data-loaders, extensions, few-shot-learning, meta-learning. - When developing models that require handling few-shot learning scenarios where only a small amount of labeled data is available. ## 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. ## When NOT to use pytorch-meta - If your project requires extensive support for traditional deep learning tasks, as PyTorch-Meta does not offer comprehensive utilities beyond few-shot learning and meta-learning. - For those strictly adhering to a single ecosystem that does not include the PyTorch framework or its specific versions below 1.4. ## Common questions ### What is the difference between Auto-PyTorch and pytorch-meta? Auto-PyTorch: Automatic architecture search and hyperparameter optimization for PyTorch. pytorch-meta: Extensions and data-loaders for few-shot learning & meta-learning in PyTorch. See the comparison table for live GitHub stats and shared categories. ### When should I choose Auto-PyTorch over pytorch-meta? Choose Auto-PyTorch over pytorch-meta when License: Auto-PyTorch is Apache-2.0, pytorch-meta is MIT; Tags unique to Auto-PyTorch: automl, deep-learning, 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 pytorch-meta over Auto-PyTorch? Choose pytorch-meta over Auto-PyTorch when License: pytorch-meta is MIT, Auto-PyTorch is Apache-2.0; Tags unique to pytorch-meta: data-loaders, extensions, few-shot-learning, meta-learning; When developing models that require handling few-shot learning scenarios where only a small amount of labeled data is available. ### 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 pytorch-meta? If your project requires extensive support for traditional deep learning tasks, as PyTorch-Meta does not offer comprehensive utilities beyond few-shot learning and meta-learning. For those strictly adhering to a single ecosystem that does not include the PyTorch framework or its specific versions below 1.4. ### Is Auto-PyTorch or pytorch-meta more popular on GitHub? Auto-PyTorch has more GitHub stars (2,541 vs 2,062). Stars measure visibility, not whether either tool fits your constraints. ### Are Auto-PyTorch and pytorch-meta open source? Yes - both are open-source projects on GitHub (Auto-PyTorch: Apache-2.0, pytorch-meta: MIT). ### Where can I find alternatives to Auto-PyTorch or pytorch-meta? GraphCanon lists graph-backed alternatives at [Auto-PyTorch alternatives](/tools/automl-auto-pytorch/alternatives) and [pytorch-meta alternatives](/tools/tristandeleu-pytorch-meta/alternatives) ([Auto-PyTorch markdown twin](/tools/automl-auto-pytorch/alternatives.md), [pytorch-meta markdown twin](/tools/tristandeleu-pytorch-meta/alternatives.md)), 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](/compare/automl-auto-pytorch-vs-tristandeleu-pytorch-meta.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Auto-PyTorch or pytorch-meta? Auto-PyTorch: Dormant. pytorch-meta: 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 Auto-PyTorch and pytorch-meta? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Auto-PyTorch trust report](/tools/automl-auto-pytorch/trust); [pytorch-meta trust report](/tools/tristandeleu-pytorch-meta/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=automl-auto-pytorch`](/api/graphcanon/graph?tool=automl-auto-pytorch) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_