Summary
An ML practitioner is grappling with Hyperparameter Optimization (HPO) for massive models that require a full day to train. To make HPO feasible, they've drastically cut down training epochs per trial, achieving trial times under 30 minutes with pruning. The core problem they're encountering during this process is "hyperparameter drift."
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