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Decision brief
NNI is an AutoML toolkit that supports feature engineering, neural architecture search, model compression, and hyperparameter tuning with the flexibility of Python programming.
Good fit when
- You need to automate extensive parts of your machine learning lifecycle from preprocessing to deployment.
- Your team prefers working in a Python environment but lacks resources for manual hyperparameter optimization or neural architecture design tasks.
Avoid when
- You require real-time automated tuning capabilities, as NNI focuses on batch processing and model training scenarios.
- If your project demands direct integration with specific deep learning frameworks beyond PyTorch and TensorFlow, NNI support is limited to these two environments.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Archived (762d 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 nniSimilar 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
This repository provides an automated machine learning (AutoML) framework that supports feature engineering, neural architecture search, model compression, and hyper-parameter tuning.
Capability facts
- Deploy
- Self-host
Source: dockerfile:Dockerfile · Aug 4, 2026
- Docker
- Dockerfile present
Source: dockerfile:Dockerfile · Aug 4, 2026
- Languages
- python
Source: github.language · Aug 4, 2026
Categories
Graph entities
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Tags
README
Installation
See the NNI installation guide to install from pip, or build from source.
To install the current release:
$ pip install nni
To update NNI to the latest version, add --upgrade flag to the above commands.
License
The entire codebase is under MIT license.
For agents
This page has a .md twin and JSON over the API.