BUTTER - Empirical Deep Learning Dataset

Published by National Renewable Energy Laboratory | Department of Energy | Metadata Last Checked: June 28, 2025 | Last Modified: 2024-01-02T19:42:28Z

The BUTTER Empirical Deep Learning Dataset represents an empirical study of the deep learning phenomena on dense fully connected networks, scanning across thirteen datasets, eight network shapes, fourteen depths, twenty-three network sizes (number of trainable parameters), four learning rates, six minibatch sizes, four levels of label noise, and fourteen levels of L1 and L2 regularization each. Multiple repetitions (typically 30, sometimes 10) of each combination of hyperparameters were preformed, and statistics including training and test loss (using a 80% / 20% shuffled train-test split) are recorded at the end of each training epoch. In total, this dataset covers 178 thousand distinct hyperparameter settings ("experiments"), 3.55 million individual training runs (an average of 20 repetitions of each experiments), and a total of 13.3 billion training epochs (three thousand epochs were covered by most runs). Accumulating this dataset consumed 5,448.4 CPU core-years, 17.8 GPU-years, and 111.2 node-years.

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Complete Metadata

bureauCode	[ "019:20" ]
dataQuality	true
DOI	10.25984/1872441
identifier	https://data.openei.org/submissions/5708
issued	2022-05-20T06:00:00Z
landingPage	https://data.openei.org/submissions/5708
programCode	[ "019:023" ]
projectNumber	GO0028308
projectTitle	National Renewable Energy Laboratory (NREL) Lab Directed Research and Development (LDRD)
spatial	{"type":"Polygon","coordinates":[[[-180,-83],[180,-83],[180,83],[-180,83],[-180,-83]]]}

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