"Womp Womp! Your browser does not support canvas :'("

BUTTER - Empirical Deep Learning Dataset

Publicly accessible License 

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.

Citation Formats

National Renewable Energy Laboratory. (2022). BUTTER - Empirical Deep Learning Dataset [data set]. Retrieved from https://dx.doi.org/10.25984/1872441.
Export Citation to RIS
Tripp, Charles, Perr-Sauer, Jordan, Hayne, Lucas, and Lunacek, Monte. BUTTER - Empirical Deep Learning Dataset. United States: N.p., 20 May, 2022. Web. doi: 10.25984/1872441.
Tripp, Charles, Perr-Sauer, Jordan, Hayne, Lucas, & Lunacek, Monte. BUTTER - Empirical Deep Learning Dataset. United States. https://dx.doi.org/10.25984/1872441
Tripp, Charles, Perr-Sauer, Jordan, Hayne, Lucas, and Lunacek, Monte. 2022. "BUTTER - Empirical Deep Learning Dataset". United States. https://dx.doi.org/10.25984/1872441. https://data.openei.org/submissions/5708.
@div{oedi_5708, title = {BUTTER - Empirical Deep Learning Dataset}, author = {Tripp, Charles, Perr-Sauer, Jordan, Hayne, Lucas, and Lunacek, Monte.}, abstractNote = {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.}, doi = {10.25984/1872441}, url = {https://data.openei.org/submissions/5708}, journal = {}, number = , volume = , place = {United States}, year = {2022}, month = {05}}
https://dx.doi.org/10.25984/1872441

Details

Data from May 20, 2022

Last updated Jun 15, 2022

Submitted Jun 15, 2022

Organization

National Renewable Energy Laboratory

Contact

Charles Edison Tripp

303.275.4082

Authors

Charles Tripp

National Renewable Energy Laboratory

Jordan Perr-Sauer

National Renewable Energy Laboratory NREL

Lucas Hayne

National Renewable Energy Laboratory NREL

Monte Lunacek

National Renewable Energy Laboratory NREL

DOE Project Details

Project Name National Renewable Energy Laboratory (NREL) Lab Directed Research and Development (LDRD)

Project Number GO0028308

Share

Submission Downloads