--- title: "hypertunity vs archai" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/gdikov-hypertunity-vs-microsoft-archai" tools: ["gdikov-hypertunity", "microsoft-archai"] --- # hypertunity vs archai *GraphCanon updated Aug 4, 2026* ## Verdict Pick hypertunity if hypertunity is a Python library that facilitates black-box hyperparameter optimisation using techniques such as Bayesian Optimization; pick archai if archai expedites Neural Architecture Search (NAS) research by providing fast, reproducible, modular tools for automated machine learning and hyperparameter optimization with Python and PyTorch. [hypertunity](https://hypertunity.readthedocs.io) reports 137 GitHub stars, 10 forks, and 0 open issues, last pushed Jan 26, 2020. [archai](https://microsoft.github.io/archai) has 485 stars, 93 forks, and 4 open issues, last pushed Nov 24, 2025. Figures are from public GitHub metadata via [hypertunity's repository](https://github.com/gdikov/hypertunity) and [archai's repository](https://github.com/microsoft/archai). | | [hypertunity](/tools/gdikov-hypertunity.md) | [archai](/tools/microsoft-archai.md) | | --- | --- | --- | | Tagline | A toolset for black-box hyperparameter optimisation | Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research. | | Stars | 137 | 485 | | Forks | 10 | 93 | | Open issues | 0 | 4 | | Language | Python | Python | | Adopt for | hypertunity is a Python library that facilitates black-box hyperparameter optimisation using techniques such as Bayesian Optimization. | Archai expedites Neural Architecture Search (NAS) research by providing fast, reproducible, modular tools for automated machine learning and hyperparameter optimization with Python and PyTorch. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [hypertunity](/tools/gdikov-hypertunity.md) | [archai](/tools/microsoft-archai.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 2381d | 252d | | Open issues (now) | 0 | 4 | | Owner type | User | Organization | | Full report | [trust report](/tools/gdikov-hypertunity/trust.md) | [trust report](/tools/microsoft-archai/trust.md) | ## Shared compatibility - **Python**: [hypertunity](/tools/gdikov-hypertunity.md) - Python runtime; [archai](/tools/microsoft-archai.md) - Python runtime ## Decision facts: hypertunity - **Requirements:** Min 2 GB RAM; Support for SLURM is indicated in the topics, useful for HPC cluster management but not a hard requirement. - **Adopt for:** hypertunity is a Python library that facilitates black-box hyperparameter optimisation using techniques such as Bayesian Optimization. ## Decision facts: archai - **Adopt for:** Archai expedites Neural Architecture Search (NAS) research by providing fast, reproducible, modular tools for automated machine learning and hyperparameter optimization with Python and PyTorch. ## Choose when ### Choose hypertunity if… - License: hypertunity is Apache-2.0, archai is MIT. - Requirements: Min 2 GB RAM; Support for SLURM is indicated in the topics, useful for HPC cluster management but not a hard requirement.. - Tags unique to hypertunity: bayesian-optimization, gpyopt, slurm, tensorboard. - When you are working with complex objective functions that are expensive to evaluate, and you need an automated way to optimize your model parameters. ### Choose archai if… - License: archai is MIT, hypertunity is Apache-2.0. - Tags unique to archai: automated-machine-learning, automl, darts, deep-learning. - Need rapid iteration in NAS projects while ensuring reproducibility ## When NOT to use hypertunity - When the objective function evaluation is inexpensive or fast because hypertunity shines in scenarios where evaluations are costly, offering less benefit if evaluations can be easily repeated. - If your project does not require advanced techniques such as Bayesian Optimization and you seek a simpler method with fewer dependencies. ## When NOT to use archai - Project requires specific GPU support not aligned with PyTorch 1.7.0+ versions - Development occurs outside Python 3.8+, limiting the application of Archai tools ## Common questions ### What is the difference between hypertunity and archai? hypertunity: A toolset for black-box hyperparameter optimisation. archai: Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.. See the comparison table for live GitHub stats and shared categories. ### When should I choose hypertunity over archai? Choose hypertunity over archai when License: hypertunity is Apache-2.0, archai is MIT; Requirements: Min 2 GB RAM; Support for SLURM is indicated in the topics, useful for HPC cluster management but not a hard requirement.; Tags unique to hypertunity: bayesian-optimization, gpyopt, slurm, tensorboard; When you are working with complex objective functions that are expensive to evaluate, and you need an automated way to optimize your model parameters. ### When should I choose archai over hypertunity? Choose archai over hypertunity when License: archai is MIT, hypertunity is Apache-2.0; Tags unique to archai: automated-machine-learning, automl, darts, deep-learning; Need rapid iteration in NAS projects while ensuring reproducibility. ### When should I avoid hypertunity? When the objective function evaluation is inexpensive or fast because hypertunity shines in scenarios where evaluations are costly, offering less benefit if evaluations can be easily repeated. If your project does not require advanced techniques such as Bayesian Optimization and you seek a simpler method with fewer dependencies. ### When should I avoid archai? Project requires specific GPU support not aligned with PyTorch 1.7.0+ versions Development occurs outside Python 3.8+, limiting the application of Archai tools ### Is hypertunity or archai more popular on GitHub? archai has more GitHub stars (485 vs 137). Stars measure visibility, not whether either tool fits your constraints. ### Are hypertunity and archai open source? Yes - both are open-source projects on GitHub (hypertunity: Apache-2.0, archai: MIT). ### Where can I find alternatives to hypertunity or archai? GraphCanon lists graph-backed alternatives at [hypertunity alternatives](/tools/gdikov-hypertunity/alternatives) and [archai alternatives](/tools/microsoft-archai/alternatives) ([hypertunity markdown twin](/tools/gdikov-hypertunity/alternatives.md), [archai markdown twin](/tools/microsoft-archai/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/gdikov-hypertunity-vs-microsoft-archai.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, hypertunity or archai? hypertunity: Dormant. archai: Slowing. 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 hypertunity and archai? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [hypertunity trust report](/tools/gdikov-hypertunity/trust); [archai trust report](/tools/microsoft-archai/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=gdikov-hypertunity`](/api/graphcanon/graph?tool=gdikov-hypertunity) - 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/_