File size: 2,762 Bytes
460fdc7 42e8f64 b7b78a8 2dc39dd b7b78a8 535ed01 2f8fc9d b7b78a8 13ef1b6 e5599c2 f7b4006 d4ded0a e5599c2 d4ded0a d8b9e17 ba0ef01 d4ded0a c76229c 7022131 f7b4006 7022131 7786ff5 7022131 7786ff5 f7b4006 7022131 f7b4006 2dc39dd c03ab70 2dc39dd b7b78a8 2dc39dd 7022131 f7b4006 2dc39dd b7b78a8 2dc39dd b7b78a8 2dc39dd 7022131 f7b4006 d4ded0a b7b78a8 d4ded0a b7b78a8 d4ded0a 7022131 f7b4006 2dc39dd b7b78a8 2dc39dd b7b78a8 2dc39dd 7022131 f7b4006 2dc39dd b7b78a8 2dc39dd b7b78a8 f7b4006 7022131 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
import gradio as gr
import pandas as pd
from huggingface_hub import list_models
import plotly.express as px
def get_plots(task):
#TO DO : hover text with energy efficiency number, parameters
task_df= pd.read_csv('data/energy/'+task)
params_df = pd.read_csv('data/params/'+task)
all_df = pd.merge(task_df, params_df, on='Link')
print(all_df.head())
all_df['Total GPU Energy (Wh)'] = all_df['total_gpu_energy']*1000
all_df = task_df.sort_values(by=['Total GPU Energy (Wh)'])
all_df['energy_star'] = pd.cut(all_df['Total GPU Energy (Wh)'], 3, labels=["⭐⭐⭐", "⭐⭐", "⭐"])
fig = px.scatter(all_df, x="model", y='Total GPU Energy (Wh)', height= 500, width= 800, color = 'energy_star', color_discrete_map={"⭐": 'red', "⭐⭐": "yellow", "⭐⭐⭐": "green"})
#fig.update_traces(mode="markers+lines", hovertemplate=None)
fig.update_layout(hovermode="y")
return fig
def get_model_names(task_data):
#TODO: add link to results in model card of each model
task_df= pd.read_csv(task_data)
model_names = task_df[['model']]
return model_names
demo = gr.Blocks()
with demo:
gr.Markdown(
"""# Energy Star Leaderboard
TODO """
)
with gr.Tabs():
with gr.TabItem("Text Generation 💬"):
with gr.Row():
with gr.Column():
#plot = gr.Plot(get_plots('text_generation.csv'))
with gr.Column():
table = gr.Dataframe(get_model_names('text_generation.csv'))
with gr.TabItem("Image Generation 📷"):
with gr.Row():
with gr.Column():
plot = gr.Plot(get_plots('image_generation.csv'))
with gr.Column():
table = gr.Dataframe(get_model_names('image_generation.csv'))
with gr.TabItem("Text Classification 🎭"):
with gr.Row():
with gr.Column():
plot = gr.Plot(get_plots('text_classification.csv'))
with gr.Column():
table = gr.Dataframe(get_model_names('text_classification.csv'))
with gr.TabItem("Image Classification 🖼️"):
with gr.Row():
with gr.Column():
plot = gr.Plot(get_plots('image_classification.csv'))
with gr.Column():
table = gr.Dataframe(get_model_names('image_classification.csv'))
with gr.TabItem("Extractive QA ❔"):
with gr.Row():
with gr.Column():
plot = gr.Plot(get_plots('question_answering.csv'))
with gr.Column():
table = gr.Dataframe(get_model_names('question_answering.csv'))
demo.launch()
|