sashavor commited on
Commit
795ccdc
1 Parent(s): 17b42b8

removing image for now

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Files changed (1) hide show
  1. app.py +10 -4
app.py CHANGED
@@ -54,16 +54,17 @@ electricity = pd.read_csv(electricity_url)
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  servers = pd.read_csv(server_url)
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  embodied_gpu = pd.read_csv(embodied_gpu_url)
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- st.image('images/MIT_carbon_image_narrow.png', use_column_width=True)
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  st.title("AI Carbon Calculator")
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  st.markdown('## Estimate your model\'s CO2 carbon footprint!')
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  st.markdown('##### You can use this tool to calculate different aspects of your model\'s carbon footprint.')
 
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  st.markdown('### Dynamic Emissions')
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- st.markdown('##### These are the emissions produced by generating the electricity needed to train your model.')
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- with st.expander("Calculate the emissions produced by energy consumption of model training"):
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  col1, col2, col3, col4 = st.columns(4)
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  with col1:
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  hardware = st.selectbox('GPU used', TDP['name'].tolist())
@@ -93,12 +94,17 @@ with st.expander("Calculate the emissions produced by energy consumption of mode
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  st.button(label="Anonymously share my data", help="Share the data from your model anonymously for research purposes!",\
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  on_click = lambda *args: write_to_csv(hardware, training_time, provider, carbon_intensity, dynamic_emissions))
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  st.markdown('### Idle Emissions')
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  st.markdown('##### These are the emissions produced by generating the electricity needed to power the rest of the infrastructure'
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  'used for model training -- the datacenter, network, heating/cooling, storage, etc.')
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- st.markdown('Do you know what the PUE (Power Usage Effectiveness) of your infrastructure is?')
 
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  servers = pd.read_csv(server_url)
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  embodied_gpu = pd.read_csv(embodied_gpu_url)
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+ #st.image('images/MIT_carbon_image_narrow.png', use_column_width=True, caption = 'Image credit: ')
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  st.title("AI Carbon Calculator")
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  st.markdown('## Estimate your model\'s CO2 carbon footprint!')
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  st.markdown('##### You can use this tool to calculate different aspects of your model\'s carbon footprint.')
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+ st.markdown('##### Share your data to help us get a better idea of AI model\'s carbon emissions.')
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  st.markdown('### Dynamic Emissions')
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+ st.markdown('##### These are the carbon emissions produced by generating the electricity necessary for powering model training')
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+ with st.expander("Calculate the dynamic emissions of your model"):
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  col1, col2, col3, col4 = st.columns(4)
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  with col1:
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  hardware = st.selectbox('GPU used', TDP['name'].tolist())
 
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  st.button(label="Anonymously share my data", help="Share the data from your model anonymously for research purposes!",\
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  on_click = lambda *args: write_to_csv(hardware, training_time, provider, carbon_intensity, dynamic_emissions))
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+ st.markdown('### Idle Emissions')
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+ st.markdown('##### These are the emissions produced by generating the electricity needed to power the rest of the infrastructure'
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+ 'used for model training -- the datacenter, network, heating/cooling, storage, etc.')
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+
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  st.markdown('### Idle Emissions')
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  st.markdown('##### These are the emissions produced by generating the electricity needed to power the rest of the infrastructure'
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  'used for model training -- the datacenter, network, heating/cooling, storage, etc.')
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+ with st.expander("Calculate the idle emissions of your model"):
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+ st.markdown('Do you know what the PUE (Power Usage Effectiveness) of your infrastructure is?')
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