Any Docker workload
HyperOptimizer is not tied to one framework. If your program can run in Docker and expose parameters through CLI flags, it can be optimized.
The universal contract
Receive
Parse arguments such as --hpo-threshold=0.7.
Run
Execute one model training job, simulation, backtest, or data pipeline run.
Measure
Compute the objective and supporting metrics.
Emit hpo.metrics.* lines to stdout.
Good workload fits
Machine learning
Tune learning rates, regularization, model dimensions, and data settings.
Trading research
Tune strategy thresholds, position sizing, and risk controls.
Simulations
Tune model assumptions, scenario settings, and objective tradeoffs.
Pipelines
Tune thresholds, batch sizes, and scoring rules for repeatable jobs.
LLM workflows
Tune prompts, retrieval settings, reranking weights, and evaluation thresholds.
Custom algorithms
Tune any executable program that reports a numeric result.
Integration checklist
- The workload can run headlessly in a container.
- Every tunable value can be passed through a
--hpo-*flag. - The program emits at least one metric for every successful trial.
- Invalid parameter combinations fail clearly or emit a penalty metric.
Start with the Quickstart.