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---
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: 1886
num_examples: 7
download_size: 29864
dataset_size: 3772
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
https://huggingface.co/spaces/meg/CalculateCarbon
From there, I run `python code/make_pretty_dataset.py` (included in this repository) to take the raw results and upload them to the dataset here. |