{"@type": "dcat:Dataset", "DOI": "10.25984/2329316", "accessLevel": "public", "bureauCode": ["019:20"], "contactPoint": {"@type": "vcard:Contact", "fn": "Charles Tripp", "hasEmail": "mailto:charles.tripp@nrel.gov"}, "dataQuality": true, "description": "The BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset adds node-level energy consumption data from watt-meters to the primary sweep of the BUTTER - Empirical Deep Learning Dataset. This dataset contains energy consumption and performance data from 63,527 individual experimental runs spanning 30,582 distinct configurations: 13 datasets, 20 sizes (number of trainable parameters), 8 network \"shapes\", and 14 depths on both CPU and GPU hardware collected using node-level watt-meters. This dataset reveals the complex relationship between dataset size, network structure, and energy use, and highlights the impact of cache effects. \n\nBUTTER-E is intended to be joined with the BUTTER dataset (see \"BUTTER - Empirical Deep Learning Dataset on OEDI\" resource below) which characterizes the performance of 483k distinct fully connected neural networks but does not include energy measurements.", "distribution": [{"@type": "dcat:Distribution", "description": "Metadata concerning each training run", "downloadURL": "https://data.openei.org/files/5991/butter_e_metadata.csv.zip", "format": "zip", "mediaType": "application/zip", "title": "BUTTER-E Metadata.zip"}, {"@type": "dcat:Distribution", "description": "1-minute raw time series power data corresponding to the runs in the \"BUTTER-E Metadata\" resource.", "downloadURL": "https://data.openei.org/files/5991/butter_e_energy.zip", "format": "zip", "mediaType": "application/zip", "title": "BUTTER-E Energy.zip"}, {"@type": "dcat:Distribution", "description": "Power data joined to run data, including extra columns for standardized energy data as described in the paper.", "downloadURL": "https://data.openei.org/files/5991/runs_with_standardized_energy.csv.zip", "format": "zip", "mediaType": "application/zip", "title": "Runs with Standardized Energy.zip"}, {"@type": "dcat:Distribution", "description": "Characteristics of each compute node used to generate the BUTTER-E data set.", "downloadURL": "https://data.openei.org/files/5991/node_sinfo.csv", "format": "csv", "mediaType": "text/csv", "title": "Node Info.csv"}, {"@type": "dcat:Distribution", "description": "Power consumption quantiles for each node used to generate the BUTTER-E Dataset.", "downloadURL": "https://data.openei.org/files/5991/node_power_dist.csv", "format": "csv", "mediaType": "text/csv", "title": "Node Power Distribution.csv"}, {"@type": "dcat:Distribution", "description": "Training losses related to the BUTTER-E dataset, re-summarized from the BUTTER dataset.", "downloadURL": "https://data.openei.org/files/5991/summary_by_epoch.tar", "format": "tar", "mediaType": "application/octet-stream", "title": "Summary by Epoch.tar"}, {"@type": "dcat:Distribution", "accessURL": "https://data.openei.org/submissions/5708", "description": "Link to the OEDI submission for the BUTTER dataset which includes a link to the original BUTTER data on AWS, data descriptions, and a tutorial Jupyter notebook for using the data.", "format": "HTML", "mediaType": "text/html", "title": "BUTTER - Empirical Deep Learning Dataset on OEDI"}, {"@type": "dcat:Distribution", "accessURL": "https://arxiv.org/html/2403.08151v1#S3", "description": "Paper detailing the BUTTER-E project and dataset.", "format": "08151v1#S3", "mediaType": "application/octet-stream", "title": "BUTTER-E Paper"}, {"@type": "dcat:Distribution", "accessURL": "https://github.com/NREL/BUTTER-E-Empirical-analysis-of-energy-trends-in-neural-networks-supplementary-code/blob/main/Readme%20for%20Data.md", "description": "README document describing the columns, schema, size, and format of the data contained in this submission.", "format": "md", "mediaType": "application/octet-stream", "title": "BUTTER-E GitHub ReadMe"}], "identifier": "https://data.openei.org/submissions/5991", "issued": "2022-12-30T07:00:00Z", "keyword": ["energy", "power", "green computing", "neural networks", "machine learning", "training", "benchmark", "deep learning", "empirical deep learning", "empirical machine learning", "energy consumption", "training efficiency", "energy efficiency", "efficient", "power consumption", "BUTTER", "model", "BUTTER-E", "node-level", "network structure", "energy use", "computational science"], "landingPage": "https://data.openei.org/submissions/5991", "license": "https://creativecommons.org/licenses/by/4.0/", "modified": "2024-10-07T15:12:02Z", "programCode": ["019:023"], "projectNumber": "GO0028308", "projectTitle": "National Renewable Energy Laboratory (NREL) Lab Directed Research and Development (LDRD)", "publisher": {"@type": "org:Organization", "name": "National Renewable Energy Laboratory"}, "spatial": "{\"type\":\"Polygon\",\"coordinates\":[[[-180,-83],[180,-83],[180,83],[-180,83],[-180,-83]]]}", "title": "BUTTER-E - Energy Consumption Data for the BUTTER Empirical Deep Learning Dataset"}