README.md

---
dataset_info:
  features:
  - name: task
    dtype: string
  - name: org
    dtype: string
  - name: model
    dtype: string
  - name: hardware
    dtype: string
  - name: date
    dtype: string
  - name: prefill
    struct:
    - name: efficency
      struct:
      - name: unit
        dtype: string
      - name: value
        dtype: float64
    - name: energy
      struct:
      - name: cpu
        dtype: float64
      - name: gpu
        dtype: float64
      - name: ram
        dtype: float64
      - name: total
        dtype: float64
      - name: unit
        dtype: string
  - name: decode
    struct:
    - name: efficiency
      struct:
      - name: unit
        dtype: string
      - name: value
        dtype: float64
    - name: energy
      struct:
      - name: cpu
        dtype: float64
      - name: gpu
        dtype: float64
      - name: ram
        dtype: float64
      - name: total
        dtype: float64
      - name: unit
        dtype: string
  - name: preprocess
    struct:
    - name: efficiency
      struct:
      - name: unit
        dtype: string
      - name: value
        dtype: float64
    - name: energy
      struct:
      - name: cpu
        dtype: float64
      - name: gpu
        dtype: float64
      - name: ram
        dtype: float64
      - name: total
        dtype: float64
      - name: unit
        dtype: string
  splits:
  - name: benchmark_results
    num_bytes: 1886
    num_examples: 7
  - name: train
    num_bytes: 2446
    num_examples: 9
  download_size: 75548
  dataset_size: 4332
configs:
- config_name: default
  data_files:
  - split: benchmark_results
    path: data/train-*
  - split: train
    path: data/train-*
---

# Analysis of energy usage for HUGS models

Based on the [energy_star branch](https://github.com/huggingface/optimum-benchmark/tree/energy_star_dev) of [optimum-benchmark](https://github.com/huggingface/optimum-benchmark), and using [codecarbon](https://pypi.org/project/codecarbon/2.1.4/).

# Fields
- **task**: Task the model was benchmarked on.
- **org**: Organization hosting the model.
- **model**: The specific model. Model names at HF are usually constructed with {org}/{model}.
- **date**: The date that the benchmark was run.
- **prefill**: The esimated energy and efficiency for prefilling.
- **decode**: The estimated energy and efficiency for decoding.
- **preprocess**: The estimated energy and efficiency for preprocessing.

# Code to Reproduce

As I'm devving, I'm hopping between https://huggingface.co/spaces/AIEnergyScore/benchmark-hugs-models and https://huggingface.co/spaces/meg/CalculateCarbon

From there, `python code/make_pretty_dataset.py` (included in this repository) takes the raw results and uploads them to the dataset here.