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Decision brief
AutoKeras simplifies deep learning model design through automated neural architecture search and is compatible with Python 3.7+ and TensorFlow 2.8.0+.
Good fit when
- When your project involves deep learning tasks requiring minimal manual intervention in designing models.
- For developers aiming to leverage advanced neural architecture searches without significant expertise in deep learning.
Avoid when
- When working with Python versions older than 3.7 or TensorFlow versions older than 2.8.0, as AutoKeras is not compatible.
- If your project emphasizes transparent, understandable model architecture over automated generation without human oversight.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Slowing (251d since push)
- As of 3w
- Provenance
- Not a fork · Organization account
- As of 3w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install autokerasSimilar 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
A Python-based AutoML library that simplifies the process of designing deep learning models through automated tasks like neural architecture search.
Capability facts
- Languages
- python
Source: github.language+pyproject.toml · Aug 4, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 4, 2026)
**Note:** Currently, AutoKeras is only compatible with **Python >= 3.7** and **TensorFlow >= 2.8.0**.Source link
Tags
README
Installation
To install the package, please use the pip installation as follows:
pip3 install autokeras
Please follow the installation guide for more details.
Note: Currently, AutoKeras is only compatible with Python >= 3.7 and TensorFlow >= 2.8.0.
For agents
This page has a .md twin and JSON over the API.