import gradio as gr import pandas as pd from huggingface_hub import list_models import plotly.express as px tasks = ['asr.csv', 'object_detection.csv', 'text_classification.csv', 'image_captioning.csv', 'question_answering.csv', 'text_generation.csv', 'image_classification.csv', 'sentence_similarity.csv', 'image_generation.csv', 'summarization.csv'] 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) params_df= params_df.rename(columns={"Link": "model"}) all_df = pd.merge(task_df, params_df, on='model') all_df['Total GPU Energy (Wh)'] = all_df['total_gpu_energy']*1000 all_df = all_df.sort_values(by=['Total GPU Energy (Wh)']) all_df['parameters'] = all_df['parameters'].apply(format_params) 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)', custom_data=['parameters'], height= 500, width= 800, color = 'energy_star', color_discrete_map={"⭐": 'red', "⭐⭐": "yellow", "⭐⭐⭐": "green"}) fig.update_traces( hovertemplate="
".join([ "Total Energy: %{y}", "Parameters: %{customdata[0]}"]) ) return fig def get_all_plots(): all_df = pd.DataFrame(columns= ['model', 'parameters', 'total_gpu_energy']) for task in tasks: task_df= pd.read_csv('data/energy/'+task) params_df = pd.read_csv('data/params/'+task) params_df= params_df.rename(columns={"Link": "model"}) tasks_df = pd.merge(task_df, params_df, on='model') tasks_df= tasks_df[['model', 'parameters', 'total_gpu_energy']] all_df = pd.concat([all_df, tasks_df]) all_df['Total GPU Energy (Wh)'] = all_df['total_gpu_energy']*1000 all_df = all_df.sort_values(by=['Total GPU Energy (Wh)']) all_df['parameters'] = all_df['parameters'].apply(format_params) 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)', custom_data=['parameters'], height= 500, width= 800, color = 'energy_star', color_discrete_map={"⭐": 'red', "⭐⭐": "yellow", "⭐⭐⭐": "green"}) fig.update_traces( hovertemplate="
".join([ "Total Energy: %{y}", "Parameters: %{customdata[0]}"]) ) return fig def make_link(mname): link = "["+ str(mname).split('/')[1] +'](https://huggingface.co/'+str(mname)+")" return link def get_model_names(task): task_df= pd.read_csv('data/params/'+task) energy_df= pd.read_csv('data/energy/'+task) task_df= task_df.rename(columns={"Link": "model"}) all_df = pd.merge(task_df, energy_df, on='model') all_df=all_df.drop_duplicates(subset=['model']) all_df['Parameters'] = all_df['parameters'].apply(format_params) all_df['Model'] = all_df['model'].apply(make_link) all_df['Total GPU Energy (Wh)'] = all_df['total_gpu_energy']*1000 all_df['Total GPU Energy (Wh)'] = all_df['Total GPU Energy (Wh)'].round(2) all_df['Rating'] = pd.cut(all_df['Total GPU Energy (Wh)'], 3, labels=["⭐⭐⭐", "⭐⭐", "⭐"]) all_df= all_df.sort_values('Total GPU Energy (Wh)') model_names = all_df[['Model','Rating','Total GPU Energy (Wh)', 'Parameters']] return model_names def get_all_model_names(): #TODO: add link to results in model card of each model all_df = pd.DataFrame(columns = ['model', 'parameters', 'total_gpu_energy']) for task in tasks: task_df= pd.read_csv('data/params/'+task) energy_df= pd.read_csv('data/energy/'+task) task_df= task_df.rename(columns={"Link": "model"}) tasks_df = pd.merge(task_df, energy_df, on='model') tasks_df= tasks_df[['model', 'parameters', 'total_gpu_energy']] all_df = pd.concat([all_df, tasks_df]) all_df=all_df.drop_duplicates(subset=['model']) all_df['Parameters'] = all_df['parameters'].apply(format_params) all_df['Model'] = all_df['model'].apply(make_link) all_df['Total GPU Energy (Wh)'] = all_df['total_gpu_energy']*1000 all_df['Total GPU Energy (Wh)'] = all_df['Total GPU Energy (Wh)'].round(2) all_df['Rating'] = pd.cut(all_df['Total GPU Energy (Wh)'], 3, labels=["⭐⭐⭐", "⭐⭐", "⭐"]) all_df= all_df.sort_values('Total GPU Energy (Wh)') model_names = all_df[['Model','Rating','Total GPU Energy (Wh)', 'Parameters']] return model_names def format_params(num): if num > 1000000000: if not num % 1000000000: return f'{num // 1000000000}B' return f'{round(num / 1000000000, 1)}B' return f'{num // 1000000}M' demo = gr.Blocks() with demo: gr.Markdown( """# Energy Star Leaderboard - v.0 (2024) 🌎 💻 🌟 ### Welcome to the leaderboard for the [AI Energy Star Project!](https://huggingface.co/EnergyStarAI) Click through the tasks below to see how different models measure up in terms of energy efficiency""" ) gr.Markdown( """Test your own models via the [submission portal (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'), datatype="markdown") 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'), datatype="markdown") 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'), datatype="markdown") 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'), datatype="markdown") with gr.TabItem("Image Captioning 📝"): with gr.Row(): with gr.Column(): plot = gr.Plot(get_plots('image_captioning.csv')) with gr.Column(): table = gr.Dataframe(get_model_names('image_captioning.csv'), datatype="markdown") with gr.TabItem("Summarization 📃"): with gr.Row(): with gr.Column(): plot = gr.Plot(get_plots('summarization.csv')) with gr.Column(): table = gr.Dataframe(get_model_names('summarization.csv'), datatype="markdown") with gr.TabItem("Automatic Speech Recognition 💬 "): with gr.Row(): with gr.Column(): plot = gr.Plot(get_plots('asr.csv')) with gr.Column(): table = gr.Dataframe(get_model_names('asr.csv'), datatype="markdown") with gr.TabItem("Object Detection 🚘"): with gr.Row(): with gr.Column(): plot = gr.Plot(get_plots('object_detection.csv')) with gr.Column(): table = gr.Dataframe(get_model_names('object_detection.csv'), datatype="markdown") with gr.TabItem("Sentence Similarity 📚"): with gr.Row(): with gr.Column(): plot = gr.Plot(get_plots('sentence_similarity.csv')) with gr.Column(): table = gr.Dataframe(get_model_names('sentence_similarity.csv'), datatype="markdown") 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'), datatype="markdown") with gr.TabItem("All Tasks 💡"): with gr.Row(): with gr.Column(): plot = gr.Plot(get_all_plots) with gr.Column(): table = gr.Dataframe(get_all_model_names, datatype="markdown") with gr.Accordion("Methodology", open = False): gr.Markdown( """For each of the ten tasks above, we created a custom dataset with 1,000 entries (see all of the datasets on our [org Hub page](https://huggingface.co/EnergyStarAI)). We then tested each of the models from the leaderboard on the appropriate task on Nvidia A100 GPUs, measuring the energy consumed using [Code Carbon](https://mlco2.github.io/codecarbon/), an open-source Python package for tracking the environmental impacts of code. We developed and used a [Docker container](https://github.com/huggingface/EnergyStarAI/) to maximize the reproducibility of results, and to enable members of the community to benchmark internal models. Reach out to us if you want to collaborate! """) gr.Markdown( """Last updated: September 20th, 2024 by [Sasha Luccioni](https://huggingface.co/sasha)""") demo.launch()