{
  "@type": "dcat:Dataset",
  "DOI": "10.25984/2329316",
  "accessLevel": "public",
  "bureauCode": [
    "019:20"
  ],
  "contactPoint": {
    "@type": "vcard:Contact",
    "fn": "Charles Tripp",
    "hasEmail": "mailto:charles.tripp@nlr.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",
      "accessURL": "https://arxiv.org/html/2403.08151v1#S3",
      "description": "Paper detailing the BUTTER-E project and dataset.",
      "format": "08151v1",
      "mediaType": "application/octet-stream",
      "title": "BUTTER-E Paper"
    },
    {
      "@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://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"
    },
    {
      "@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": "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": "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": "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": "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": "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"
    }
  ],
  "identifier": "https://data.openei.org/submissions/5991",
  "issued": "2022-12-30T07:00:00Z",
  "keyword": [
    "BUTTER",
    "BUTTER-E",
    "benchmark",
    "computational science",
    "deep learning",
    "efficient",
    "empirical deep learning",
    "empirical machine learning",
    "energy",
    "energy consumption",
    "energy efficiency",
    "energy use",
    "green computing",
    "machine learning",
    "model",
    "network structure",
    "neural networks",
    "node-level",
    "power",
    "power consumption",
    "training",
    "training efficiency"
  ],
  "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"
}