--- title: "Auto-PyTorch vs nas-env" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/automl-auto-pytorch-vs-gomerudo-nas-env" tools: ["automl-auto-pytorch", "gomerudo-nas-env"] --- # Auto-PyTorch vs nas-env *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 nas-env if nas-env offers an OpenAI Gym environment for Neural Architecture Search in Python under MIT license. [Auto-PyTorch](https://github.com/automl/Auto-PyTorch) reports 2.5k GitHub stars, 303 forks, and 75 open issues, last pushed Apr 9, 2024. [nas-env](https://github.com/gomerudo/nas-env) has 31 stars, 3 forks, and 0 open issues, last pushed May 4, 2020. Figures are from public GitHub metadata via [Auto-PyTorch's repository](https://github.com/automl/Auto-PyTorch) and [nas-env's repository](https://github.com/gomerudo/nas-env). | | [Auto-PyTorch](/tools/automl-auto-pytorch.md) | [nas-env](/tools/gomerudo-nas-env.md) | | --- | --- | --- | | Tagline | Automatic architecture search and hyperparameter optimization for PyTorch | Simple OpenAI Gym environment for Neural Architecture Search (NAS) | | Stars | 2,541 | 31 | | Forks | 303 | 3 | | Open issues | 75 | 0 | | Language | Python | Python | | Adopt for | Auto-PyTorch specializes in automatic architecture search and hyperparameter optimization for deep-learning models using PyTorch. | nas-env offers an OpenAI Gym environment for Neural Architecture Search in Python under MIT license. | | 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) | [nas-env](/tools/gomerudo-nas-env.md) | | --- | --- | --- | | Days since push | 846d | 2282d | | Open issues (now) | 75 | 0 | | Owner type | Organization | User | | Full report | [trust report](/tools/automl-auto-pytorch/trust.md) | [trust report](/tools/gomerudo-nas-env/trust.md) | ## Shared compatibility - **Python**: [Auto-PyTorch](/tools/automl-auto-pytorch.md) - Python runtime; [nas-env](/tools/gomerudo-nas-env.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: nas-env - **Adopt for:** nas-env offers an OpenAI Gym environment for Neural Architecture Search in Python under MIT license. ## Choose when ### Choose Auto-PyTorch if… - License: Auto-PyTorch is Apache-2.0, nas-env is MIT. - Tags unique to Auto-PyTorch: automl, deep-learning, pytorch, tabular-data. - 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 nas-env if… - License: nas-env is MIT, Auto-PyTorch is Apache-2.0. - Tags unique to nas-env: neural-architecture-search, openai-gym, python, reinforcement-learning. - When you need to implement NAS algorithms using reinforcement learning with compatibility to OpenAI Gym ## 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 nas-env - If you require a fully documented package as documentation for nas-env remains under development - During production phases when stability is crucial because nas-env is still undergoing architectural changes ## Common questions ### What is the difference between Auto-PyTorch and nas-env? Auto-PyTorch: Automatic architecture search and hyperparameter optimization for PyTorch. nas-env: Simple OpenAI Gym environment for Neural Architecture Search (NAS). See the comparison table for live GitHub stats and shared categories. ### When should I choose Auto-PyTorch over nas-env? Choose Auto-PyTorch over nas-env when License: Auto-PyTorch is Apache-2.0, nas-env is MIT; Tags unique to Auto-PyTorch: automl, deep-learning, pytorch, tabular-data; 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 nas-env over Auto-PyTorch? Choose nas-env over Auto-PyTorch when License: nas-env is MIT, Auto-PyTorch is Apache-2.0; Tags unique to nas-env: neural-architecture-search, openai-gym, python, reinforcement-learning; When you need to implement NAS algorithms using reinforcement learning with compatibility to OpenAI Gym. ### 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 nas-env? If you require a fully documented package as documentation for nas-env remains under development During production phases when stability is crucial because nas-env is still undergoing architectural changes ### Is Auto-PyTorch or nas-env more popular on GitHub? Auto-PyTorch has more GitHub stars (2,541 vs 31). Stars measure visibility, not whether either tool fits your constraints. ### Are Auto-PyTorch and nas-env open source? Yes - both are open-source projects on GitHub (Auto-PyTorch: Apache-2.0, nas-env: MIT). ### Where can I find alternatives to Auto-PyTorch or nas-env? GraphCanon lists graph-backed alternatives at [Auto-PyTorch alternatives](/tools/automl-auto-pytorch/alternatives) and [nas-env alternatives](/tools/gomerudo-nas-env/alternatives) ([Auto-PyTorch markdown twin](/tools/automl-auto-pytorch/alternatives.md), [nas-env markdown twin](/tools/gomerudo-nas-env/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-gomerudo-nas-env.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Auto-PyTorch or nas-env? Auto-PyTorch: Dormant. nas-env: 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 nas-env? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Auto-PyTorch trust report](/tools/automl-auto-pytorch/trust); [nas-env trust report](/tools/gomerudo-nas-env/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/_