archai
Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.
GraphCanon updated 2w · GitHub synced 2w · 29 views this month
Decision brief
Archai expedites Neural Architecture Search (NAS) research by providing fast, reproducible, modular tools for automated machine learning and hyperparameter optimization with Python and PyTorch.
Good fit when
- Need rapid iteration in NAS projects while ensuring reproducibility
- Favor modular design to easily integrate new components or experiment setups
Avoid when
- 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
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Slowing (252d since push)
- As of 2w
- Provenance
- Not a fork · Organization account
- As of 2w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Backing
Company context for Microsoft. Display-only - separate from trust and ranking.
- Company
- Microsoft·GitHub org profile·1mo
- Employees
- 221,000·Wikidata (P1128 employees)·1mo
- Commercial model
- Pure OSS·GitHub org profile (public repos)·1mo
Install
pip install archaiSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Archai is a tool to accelerate Neural Architecture Search by providing fast, reproducible, and modular research capabilities in automated machine learning and hyperparameter optimization.
Capability facts
- Languages
- python
Source: github.language+pyproject.toml · Aug 4, 2026
Categories
Graph entities
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 4, 2026)
**Archai requires Python 3.8+ and PyTorch 1.7.0+ to function properly.**Source link
Tags
README
Installation
Archai can be installed through various methods, however, it is recommended to utilize a virtual environment such as conda or pyenv for optimal results.
To install Archai via PyPI, the following command can be executed:
pip install archai
Archai requires Python 3.8+ and PyTorch 1.7.0+ to function properly.
For further information, please consult the installation guide.
License
This project is released under the MIT License. Please review the file for more details.
For agents
This page has a .md twin and JSON over the API.