--- title: "autokeras vs pytorch-meta" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/keras-team-autokeras-vs-tristandeleu-pytorch-meta" tools: ["keras-team-autokeras", "tristandeleu-pytorch-meta"] --- # autokeras vs pytorch-meta *GraphCanon updated Aug 4, 2026* ## Verdict Pick autokeras if autoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+; 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. [autokeras](http://autokeras.com/) reports 9.3k GitHub stars, 1.4k forks, and 161 open issues, last pushed Nov 25, 2025. [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 [autokeras's repository](https://github.com/keras-team/autokeras) and [pytorch-meta's repository](https://github.com/tristandeleu/pytorch-meta). | | [autokeras](/tools/keras-team-autokeras.md) | [pytorch-meta](/tools/tristandeleu-pytorch-meta.md) | | --- | --- | --- | | Tagline | AutoML library for deep learning | Extensions and data-loaders for few-shot learning & meta-learning in PyTorch | | Stars | 9,328 | 2,062 | | Forks | 1,393 | 264 | | Open issues | 161 | 61 | | Language | Python | Python | | Adopt for | AutoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+. | 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 | Developer Tools, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [autokeras](/tools/keras-team-autokeras.md) | [pytorch-meta](/tools/tristandeleu-pytorch-meta.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 251d | 1113d | | Open issues (now) | 161 | 61 | | Owner type | Organization | User | | Full report | [trust report](/tools/keras-team-autokeras/trust.md) | [trust report](/tools/tristandeleu-pytorch-meta/trust.md) | ## Shared compatibility - **Python**: [autokeras](/tools/keras-team-autokeras.md) - Python runtime; [pytorch-meta](/tools/tristandeleu-pytorch-meta.md) - Python runtime ## Decision facts: autokeras - **Adopt for:** AutoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+. ## 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 autokeras if… - License: autokeras is Apache-2.0, pytorch-meta is MIT. - Tags unique to autokeras: autodl, automl, deep-learning, keras. - Also covers Developer Tools. - When your project involves deep learning tasks requiring minimal manual intervention in designing models. ### Choose pytorch-meta if… - License: pytorch-meta is MIT, autokeras 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 autokeras - When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible. - If your project emphasizes transparent, understandable model architecture over automated generation without human oversight. ## 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 autokeras and pytorch-meta? autokeras: AutoML library for deep learning. 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 autokeras over pytorch-meta? Choose autokeras over pytorch-meta when License: autokeras is Apache-2.0, pytorch-meta is MIT; Tags unique to autokeras: autodl, automl, deep-learning, keras; Also covers Developer Tools; When your project involves deep learning tasks requiring minimal manual intervention in designing models. ### When should I choose pytorch-meta over autokeras? Choose pytorch-meta over autokeras when License: pytorch-meta is MIT, autokeras 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 autokeras? When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible. If your project emphasizes transparent, understandable model architecture over automated generation without human oversight. ### 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 autokeras or pytorch-meta more popular on GitHub? autokeras has more GitHub stars (9,328 vs 2,062). Stars measure visibility, not whether either tool fits your constraints. ### Are autokeras and pytorch-meta open source? Yes - both are open-source projects on GitHub (autokeras: Apache-2.0, pytorch-meta: MIT). ### Where can I find alternatives to autokeras or pytorch-meta? GraphCanon lists graph-backed alternatives at [autokeras alternatives](/tools/keras-team-autokeras/alternatives) and [pytorch-meta alternatives](/tools/tristandeleu-pytorch-meta/alternatives) ([autokeras markdown twin](/tools/keras-team-autokeras/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/keras-team-autokeras-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, autokeras or pytorch-meta? autokeras: Slowing. 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 autokeras and pytorch-meta? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [autokeras trust report](/tools/keras-team-autokeras/trust); [pytorch-meta trust report](/tools/tristandeleu-pytorch-meta/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=keras-team-autokeras`](/api/graphcanon/graph?tool=keras-team-autokeras) - 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/_