--- title: "nas-env vs accelerate" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gomerudo-nas-env-vs-huggingface-accelerate" tools: ["gomerudo-nas-env", "huggingface-accelerate"] --- # nas-env vs accelerate *GraphCanon updated Aug 4, 2026* ## Verdict Pick nas-env if nas-env offers an OpenAI Gym environment for Neural Architecture Search in Python under MIT license; pick accelerate if tool: accelerate. [nas-env](https://github.com/gomerudo/nas-env) reports 31 GitHub stars, 3 forks, and 0 open issues, last pushed May 4, 2020. [accelerate](https://huggingface.co/docs/accelerate) has 9.8k stars, 1.4k forks, and 105 open issues, last pushed Jul 30, 2026. Figures are from public GitHub metadata via [nas-env's repository](https://github.com/gomerudo/nas-env) and [accelerate's repository](https://github.com/huggingface/accelerate). | | [nas-env](/tools/gomerudo-nas-env.md) | [accelerate](/tools/huggingface-accelerate.md) | | --- | --- | --- | | Tagline | Simple OpenAI Gym environment for Neural Architecture Search (NAS) | A tool for launching, training, and using PyTorch models with ease on various devices, configurations, including mixed precision support. | | Stars | 31 | 9,803 | | Forks | 3 | 1,425 | | Open issues | 0 | 105 | | Language | Python | Python | | Adopt for | nas-env offers an OpenAI Gym environment for Neural Architecture Search in Python under MIT license. | Tool: accelerate | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [nas-env](/tools/gomerudo-nas-env.md) | [accelerate](/tools/huggingface-accelerate.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 2282d | 3d | | Open issues (now) | 0 | 105 | | Owner type | User | Organization | | Full report | [trust report](/tools/gomerudo-nas-env/trust.md) | [trust report](/tools/huggingface-accelerate/trust.md) | ## Shared compatibility - **Python**: [nas-env](/tools/gomerudo-nas-env.md) - Python runtime; [accelerate](/tools/huggingface-accelerate.md) - Python runtime ## Decision facts: nas-env - **Adopt for:** nas-env offers an OpenAI Gym environment for Neural Architecture Search in Python under MIT license. ## Decision facts: accelerate - **Adopt for:** Tool: accelerate ## Choose when ### Choose nas-env if… - License: nas-env is MIT, accelerate 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 ### Choose accelerate if… - License: accelerate is Apache-2.0, nas-env is MIT. - Tags unique to accelerate: deepspeed, fsdp, mixed precision, pytorch. - Also covers Inference & Serving. - Easy mixed-precision support for PyTorch models ## 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 ## When NOT to use accelerate - Non-PyTorch projects do not benefit from this tool - Doesnt offer advanced auto-tuning features for other frameworks like TensorFlow - Limited to Python environments compatible with PyTorch 1.10.0+ ## Common questions ### What is the difference between nas-env and accelerate? nas-env: Simple OpenAI Gym environment for Neural Architecture Search (NAS). accelerate: A tool for launching, training, and using PyTorch models with ease on various devices, configurations, including mixed precision support.. See the comparison table for live GitHub stats and shared categories. ### When should I choose nas-env over accelerate? Choose nas-env over accelerate when License: nas-env is MIT, accelerate 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 choose accelerate over nas-env? Choose accelerate over nas-env when License: accelerate is Apache-2.0, nas-env is MIT; Tags unique to accelerate: deepspeed, fsdp, mixed precision, pytorch; Also covers Inference & Serving; Easy mixed-precision support for PyTorch models. ### 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 ### When should I avoid accelerate? Non-PyTorch projects do not benefit from this tool Doesnt offer advanced auto-tuning features for other frameworks like TensorFlow Limited to Python environments compatible with PyTorch 1.10.0+ ### Is nas-env or accelerate more popular on GitHub? accelerate has more GitHub stars (9,803 vs 31). Stars measure visibility, not whether either tool fits your constraints. ### Are nas-env and accelerate open source? Yes - both are open-source projects on GitHub (nas-env: MIT, accelerate: Apache-2.0). ### Where can I find alternatives to nas-env or accelerate? GraphCanon lists graph-backed alternatives at [nas-env alternatives](/tools/gomerudo-nas-env/alternatives) and [accelerate alternatives](/tools/huggingface-accelerate/alternatives) ([nas-env markdown twin](/tools/gomerudo-nas-env/alternatives.md), [accelerate markdown twin](/tools/huggingface-accelerate/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/gomerudo-nas-env-vs-huggingface-accelerate.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, nas-env or accelerate? nas-env: Dormant. accelerate: 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 nas-env and accelerate? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [nas-env trust report](/tools/gomerudo-nas-env/trust); [accelerate trust report](/tools/huggingface-accelerate/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=gomerudo-nas-env`](/api/graphcanon/graph?tool=gomerudo-nas-env) - 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/_