GraphCanon updated Oct 2, 2026 · GitHub synced Oct 2, 2026
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Decision brief
HpBandSter is noted for its robust approach to hyperparameter optimization and neural architecture search through distributed computing capabilities.
Good fit when
- HpBandSter is best used when conducting large-scale experiments on multiple machines that require efficient resource management across different environments.
- It is ideal for scenarios where a wide range of configurations needs to be tested rapidly, leveraging parallel processing power.
Avoid when
- If your project involves smaller datasets or less complex models where individual hyperparameter tuning can be done manually, HpBandSter might be an overkill due to its advanced distributed settings.
- Avoid using HpBandSter if you need a tool that heavily relies on Bayesian optimization techniques, as it specializes more in Hyperband methodology.
- Pricing:
- freemium - HpBandSter is open-source software under a permissive BSD-3-Clause License, allowing unrestricted usage for personal or commercial purposes without any direct costs.
- Requirements:
- Min 4 GB RAM; Requires Python environment. No Docker required.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (1446d since push)
- As of Oct 2, 2026
- Provenance
- Not a fork · Organization account
- As of Oct 2, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install HpBandSterSimilar 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 tool for automated machine learning focusing on hyperparameter optimization and neural architecture search using distributed computing.
Capability facts
- Languages
- python
Source: github.language · Oct 2, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Oct 2, 2026)
python3 setup.py develop --userSource link
Tags
README
How to install We try to keep the package on PyPI up to date. So you should be able to install it via: If you want to develop on the code you could install it via:
For agents
This page has a .md twin and JSON over the API.