archai
Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.
GraphCanon updated Sep 30, 2026 · GitHub synced Sep 30, 2026
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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
- Very active (1d since push)
- As of Sep 30, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 30, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
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·Jul 11, 2026
- Employees
- 221,000·Wikidata (P1128 employees)·Jul 11, 2026
- Commercial model
- Pure OSS·GitHub org profile (public repos)·Jul 11, 2026
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 · Sep 30, 2026
Categories
Graph entities
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 30, 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 or for optimal results. To install Archai via PyPI, the following command can be executed: Archai requires Python 3.8+ and PyTorch 1.7.0+ to function...
For agents
This page has a .md twin and JSON over the API.