adding other tasks

app.py

@@ -4,6 +4,7 @@ from huggingface_hub import list_models
 import plotly.express as px
 
 def get_plots(task_data):
+    #TO DO : hover text with energy efficiency number, parameters
     task_df= pd.read_csv(task_data)
     task_df['Total GPU Energy (Wh)'] = task_df['total_gpu_energy']*1000
     task_df = task_df.sort_values(by=['Total GPU Energy (Wh)'])
@@ -12,9 +13,9 @@ def get_plots(task_data):
     return task_df
 
 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']]
-    print(model_names)
     return model_names
 
 demo = gr.Blocks()
@@ -28,14 +29,18 @@ with demo:
     with gr.Tabs():
         with gr.TabItem("Text Generation 💬"):
             with gr.Row():
-                animal_data = gr.components.Dataframe(
-                    type="pandas", datatype=["number", "markdown", "markdown", "number"]
-                )
+                with gr.Column():
+                    plot = gr.Plot(get_plots('data/text_generation.csv'))
+                with gr.Column():
+                    table = gr.Dataframe(get_model_names('data/text_generation.csv'))
+
         with gr.TabItem("Image Generation 📷"):
             with gr.Row():
-                science_data = gr.components.Dataframe(
-                    type="pandas", datatype=["number", "markdown", "markdown", "number"]
-                )
+                with gr.Column():
+                    plot = gr.Plot(get_plots('data/image_generation.csv'))
+                with gr.Column():
+                    table = gr.Dataframe(get_model_names('data/image_generation.csv'))
+
         with gr.TabItem("Text Classification 🎭"):
             with gr.Row():
                 with gr.Column():
@@ -45,14 +50,16 @@ with demo:
 
         with gr.TabItem("Image Classification 🖼️"):
             with gr.Row():
-                landscape_data = gr.components.Dataframe(
-                    type="pandas", datatype=["number", "markdown", "markdown", "number"]
-                )
+                with gr.Column():
+                    plot = gr.Plot(get_plots('data/image_classification.csv'))
+                with gr.Column():
+                    table = gr.Dataframe(get_model_names('data/image_classification.csv'))
+
         with gr.TabItem("Extractive QA ❔"):
             with gr.Row():
-                wildcard_data = gr.components.Dataframe(
-                    type="pandas", datatype=["number", "markdown", "markdown", "number"]
-                )
-
+                with gr.Column():
+                    plot = gr.Plot(get_plots('data/question_answering.csv'))
+                with gr.Column():
+                    table = gr.Dataframe(get_model_names('data/question_answering.csv'))
 
 demo.launch()

data/image_classification.csv

@@ -0,0 +1,24 @@
+,timestamp,task,model,dataset,total_gpu_energy
+0,2024-09-05-15-55-25,image_classification,deepmind/vision-perceiver-conv,EnergyStarAI/image_classification,0.002641365890868874
+0,2024-09-04-22-54-51,image_classification,microsoft/beit-base-patch16-224,EnergyStarAI/image_classification,0.0007415063154269319
+0,2024-09-04-22-06-45,image_classification,microsoft/resnet-50,EnergyStarAI/image_classification,0.0003904286734538687
+0,2024-09-05-15-31-45,image_classification,microsoft/swin-base-patch4-window12-384,EnergyStarAI/image_classification,0.0015308166135410417
+0,2024-09-05-15-37-23,image_classification,microsoft/resnet-18,EnergyStarAI/image_classification,0.00029911504484738673
+0,2024-09-04-22-58-16,image_classification,microsoft/swinv2-large-patch4-window12-192-22k,EnergyStarAI/image_classification,0.001290521115749399
+0,2024-09-04-22-58-53,image_classification,microsoft/swinv2-large-patch4-window12-192-22k,EnergyStarAI/image_classification,0.0013860879144246495
+0,2024-09-05-15-50-25,image_classification,microsoft/swinv2-tiny-patch4-window8-256,EnergyStarAI/image_classification,0.0007677747808859881
+0,2024-09-04-22-44-06,image_classification,microsoft/swinv2-tiny-patch4-window16-256,EnergyStarAI/image_classification,0.000848116678493227
+0,2024-09-04-22-09-01,image_classification,microsoft/beit-base-patch16-224-pt22k-ft22k,EnergyStarAI/image_classification,0.0007789863176341782
+0,2024-09-04-22-49-34,image_classification,apple/mobilevit-small,EnergyStarAI/image_classification,0.0005120553818661943
+0,2024-09-04-22-09-45,image_classification,WinKawaks/vit-tiny-patch16-224,EnergyStarAI/image_classification,0.0003847505022445574
+0,2024-09-04-22-49-24,image_classification,facebook/convnext-base-384-22k-1k,EnergyStarAI/image_classification,0.0011152139755038703
+0,2024-09-04-22-44-45,image_classification,facebook/convnext-tiny-224,EnergyStarAI/image_classification,0.00045075399949183747
+0,2024-09-04-22-49-56,image_classification,facebook/convnext-large-224,EnergyStarAI/image_classification,0.0009730105839629744
+0,2024-09-05-15-47-07,image_classification,nvidia/mit-b5,EnergyStarAI/image_classification,0.0024879305181205067
+0,2024-09-05-15-57-37,image_classification,google/efficientnet-b7,EnergyStarAI/image_classification,0.0018499837577637735
+0,2024-09-04-22-54-33,image_classification,google/vit-base-patch32-384,EnergyStarAI/image_classification,0.0007589946349732202
+0,2024-09-04-22-08-50,image_classification,google/vit-base-patch16-384,EnergyStarAI/image_classification,0.0009914635153924322
+0,2024-09-04-22-08-57,image_classification,google/vit-large-patch32-384,EnergyStarAI/image_classification,0.0011631231249422003
+0,2024-09-04-22-54-36,image_classification,google/mobilenet_v2_1.0_224,EnergyStarAI/image_classification,0.0004064683251744028
+0,2024-09-04-22-07-11,image_classification,google/vit-base-patch16-224,EnergyStarAI/image_classification,0.0005825953549653207
+0,2024-09-04-22-09-33,image_classification,google/bit-50,EnergyStarAI/image_classification,0.0008317437209498735

data/image_generation.csv

@@ -0,0 +1,13 @@
+,timestamp,task,model,dataset,total_gpu_energy
+0,2024-09-04-17-08-46,image_generation_tiny_sd,Mitsua/mitsua-diffusion-one,EnergyStarAI/image_generation,0.18681191386607826
+0,2024-09-04-17-08-42,image_generation_tiny_sd,gsdf/Counterfeit-V2.5,EnergyStarAI/image_generation,0.18736638611521114
+0,2024-09-04-17-01-27,image_generation_tiny_sd,stabilityai/sd-turbo,EnergyStarAI/image_generation,0.18986494633628012
+0,2024-09-04-17-01-31,image_generation_tiny_sd,stabilityai/stable-cascade,EnergyStarAI/image_generation,1.2143555385669864
+0,2024-09-04-17-01-48,image_generation_tiny_sd,stabilityai/stable-diffusion-2-1,EnergyStarAI/image_generation,0.5344667333230447
+0,2024-09-04-17-01-38,image_generation_tiny_sd,stabilityai/sdxl-turbo,EnergyStarAI/image_generation,0.38556879787145704
+0,2024-09-04-16-52-34,image_generation_tiny_sd,stabilityai/stable-diffusion-xl-base-1.0,EnergyStarAI/image_generation,1.6398454721253273
+0,2024-09-04-17-08-19,image_generation_tiny_sd,dreamlike-art/dreamlike-photoreal-2.0,EnergyStarAI/image_generation,0.5813318923429195
+0,2024-09-04-17-01-18,image_generation_tiny_sd,prompthero/openjourney-v4,EnergyStarAI/image_generation,0.20336733122151288
+0,2024-09-04-17-01-27,image_generation_tiny_sd,prompthero/openjourney,EnergyStarAI/image_generation,0.1968802236707196
+0,2024-09-04-17-08-37,image_generation_tiny_sd,SimianLuo/LCM_Dreamshaper_v7,EnergyStarAI/image_generation,0.32204441110754656
+0,2024-09-04-17-08-08,image_generation_tiny_sd,Yntec/epiCPhotoGasm,EnergyStarAI/image_generation,0.5865744907036616

data/question_answering.csv

@@ -0,0 +1,19 @@
+,timestamp,task,model,dataset,total_gpu_energy
+0,2024-09-06-14-58-58,question_answering,distilbert/distilbert-base-cased-distilled-squad,EnergyStarAI/extractive_qa,0.0002399711641988489
+0,2024-09-05-22-57-19,question_answering,csarron/mobilebert-uncased-squad-v2,EnergyStarAI/extractive_qa,0.0009365245547769518
+0,2024-09-06-14-56-03,question_answering,Intel/dynamic_tinybert,EnergyStarAI/extractive_qa,0.00020857152796818212
+0,2024-09-06-01-22-50,question_answering,Falconsai/question_answering_v2,EnergyStarAI/extractive_qa,0.0002254597081453369
+0,2024-09-06-01-22-48,question_answering,allenai/longformer-large-4096-finetuned-triviaqa,EnergyStarAI/extractive_qa,0.003970963176768194
+0,2024-09-06-15-15-47,question_answering,mrm8488/longformer-base-4096-finetuned-squadv2,EnergyStarAI/extractive_qa,0.0017923508227684515
+0,2024-09-06-01-22-27,question_answering,mrm8488/bert-tiny-finetuned-squadv2,EnergyStarAI/extractive_qa,5.536551651470489e-05
+0,2024-09-06-14-58-51,question_answering,deepset/deberta-v3-large-squad2,EnergyStarAI/extractive_qa,0.0018766848346800202
+0,2024-09-06-14-58-22,question_answering,deepset/minilm-uncased-squad2,EnergyStarAI/extractive_qa,0.00025281523002980145
+0,2024-09-05-22-53-34,question_answering,deepset/bert-large-uncased-whole-word-masking-squad2,EnergyStarAI/extractive_qa,0.0010475373935861399
+0,2024-09-05-22-56-47,question_answering,deepset/deberta-v3-base-squad2,EnergyStarAI/extractive_qa,0.0008124823444305207
+0,2024-09-05-23-01-01,question_answering,deepset/electra-base-squad2,EnergyStarAI/extractive_qa,0.00046862068045072647
+0,2024-09-05-22-53-25,question_answering,timpal0l/mdeberta-v3-base-squad2,EnergyStarAI/extractive_qa,0.0008576797694729521
+0,2024-09-06-01-32-55,question_answering_t5,google-t5/t5-large,EnergyStarAI/extractive_qa,0.07227845601716326
+0,2024-09-06-01-32-31,question_answering_t5,google-t5/t5-11b,EnergyStarAI/extractive_qa,0.17813010925397316
+0,2024-09-06-01-24-52,question_answering_t5,google-t5/t5-3b,EnergyStarAI/extractive_qa,0.08769785824156423
+0,2024-09-06-01-22-36,question_answering_t5,google-t5/t5-base,EnergyStarAI/extractive_qa,0.03436274993462298
+0,2024-09-06-01-24-00,question_answering_t5,google-t5/t5-small,EnergyStarAI/extractive_qa,0.020635068646930498

data/text_generation.csv

@@ -0,0 +1,52 @@
+,timestamp,task,model,dataset,total_gpu_energy
+0,2024-09-03-20-36-27,text_generation,berkeley-nest/Starling-LM-7B-alpha,EnergyStarAI/text_generation,0.04829386649617336
+0,2024-09-03-17-53-29,text_generation,distilbert/distilgpt2,EnergyStarAI/text_generation,0.001425080278951185
+0,2024-09-03-19-35-39,text_generation,distilbert/distilgpt2,EnergyStarAI/text_generation,0.0014059794303420859
+0,2024-09-03-18-16-33,text_generation,openai-community/gpt2,EnergyStarAI/text_generation,0.002368695394954301
+0,2024-09-03-19-37-40,text_generation,openai-community/gpt2,EnergyStarAI/text_generation,0.002339770899592963
+0,2024-09-03-20-32-52,text_generation,openai-community/gpt2-xl,EnergyStarAI/text_generation,0.013101479925619941
+0,2024-09-03-18-24-01,text_generation,openai-community/gpt2-medium,EnergyStarAI/text_generation,0.004465207961052364
+0,2024-09-03-19-43-32,text_generation,openai-community/gpt2-medium,EnergyStarAI/text_generation,0.004732256035802606
+0,2024-09-03-19-51-07,text_generation,openai-community/gpt2-large,EnergyStarAI/text_generation,0.007908214937677993
+0,2024-09-03-18-23-56,text_generation,openai-community/gpt2-large,EnergyStarAI/text_generation,0.00795154244456233
+0,2024-09-03-18-01-05,text_generation,openai-community/openai-gpt,EnergyStarAI/text_generation,0.004929003832088341
+0,2024-09-03-19-35-45,text_generation,openai-community/openai-gpt,EnergyStarAI/text_generation,0.004807277651374564
+0,2024-09-03-19-49-03,text_generation,microsoft/phi-1,EnergyStarAI/text_generation,0.011351599942383217
+0,2024-09-03-18-23-24,text_generation,microsoft/phi-1,EnergyStarAI/text_generation,0.010815714652564878
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+0,2024-09-03-19-50-04,text_generation,microsoft/phi-1_5,EnergyStarAI/text_generation,0.010834772973367013
+0,2024-09-03-20-03-08,text_generation,microsoft/phi-2,EnergyStarAI/text_generation,0.019046106403539653
+0,2024-09-04-19-48-08,text2text_generation_aya,CohereForAI/aya-101,EnergyStarAI/text_generation,0.0319398923852269
+0,2024-09-04-13-47-55,text_generation_mixtral-8x22B,CohereForAI/aya-23-35B,EnergyStarAI/text_generation,0.4242194862086494
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