GPU Training Setup and Measurement

Count a dense layer, grouped Conv2D layer or explicitly specified conventional decoder with bias, grouping and weight-sharing choices.

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Add explicit resident weight and runtime components, compare a device budget and reserve, and retain byte-rounding and measurement assumptions.

Inspect a fixed-shape linear tensor-storage budget and its whole-batch limit with explicit fixed memory, element size and reserve.

Calculate throughput from non-overlapping serial batch stages or observed end-to-end completed samples and elapsed time. Keep measurement scopes explicit.

Estimate a compatible training run from measured samples per second, explicit incomplete-batch handling and separate per-epoch and fixed overhead.

Allocate exact whole-sample train, validation and test counts using documented rounding, preserving the total and flagging empty splits.

Count larger and smaller validation folds and their training sizes without losing remainder samples or claiming to select an optimal fold count.

Score paired actual and predicted numeric values with R² and RMSE, retaining negative scores and explicitly undefined constant-target R².

Budget explicit label assignments, review events and setup using stated rates and working time. Keep sub-cent rates until final USD rounding.

Compare game work with available team hours or observed team throughput, using explicit scope allowances and clearly labeled time equivalents.