GraphCanon updated Oct 2, 2026 · GitHub synced Oct 2, 2026
44views this month
Decision brief
hypertunity is a Python library that facilitates black-box hyperparameter optimisation using techniques such as Bayesian Optimization.
Good fit when
- When you are working with complex objective functions that are expensive to evaluate, and you need an automated way to optimize your model parameters.
- If you prefer tools that integrate well with Python's ecosystem and you are looking for visual feedback on optimization progress through Tensorboard.
Avoid when
- When the objective function evaluation is inexpensive or fast because hypertunity shines in scenarios where evaluations are costly, offering less benefit if evaluations can be easily repeated.
- If your project does not require advanced techniques such as Bayesian Optimization and you seek a simpler method with fewer dependencies.
- Requirements:
- Min 2 GB RAM; Support for SLURM is indicated in the topics, useful for HPC cluster management but not a hard requirement.
Observed Jul 17, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Dormant (2440d since push)
- As of Oct 2, 2026
- Provenance
- Not a fork · Personal 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 hypertunitySimilar 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
hypertunity is a Python library focused on automating the optimization of hyperparameters using techniques like Bayesian Optimization.
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)
```python import hypertunity as htSource link
Tags
README
Quick start Define the objective function to optimise. For example, it can take the hyperparameters as input and return a raw value as output: To define the valid ranges for the values of we create a object: Then we set up the optimiser: And we run the optimisation for 10 steps....
For agents
This page has a .md twin and JSON over the API.