Addressing Some of Challenges When Optimizing Long-to-Train-Models
summary
SigOpt provides an API that runs Bayesian optimization with acquisition functions, encodes previous runs as prior probability density functions, and supports asynchronous parallelization so GPUs that finish early request new suggestions immediately. The system targets hyperparameter spaces where single evaluations range from minutes to hours, uses warm starts and multi‑fidelity evaluations to transfer knowledge between re‑training rounds, and demonstrably reduces wall‑clock time to good models while using compute more efficiently.
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