sashavor commited on
Commit
e416978
1 Parent(s): 1cf9ef7

making bigly changes

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Files changed (1) hide show
  1. app.py +3 -3
app.py CHANGED
@@ -158,13 +158,12 @@ with st.expander("Calculate the datacenter emissions of your model"):
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  else:
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  st.markdown('##### The PUE of the datacenter you used is: '+ str(pue) + ' [(source)]('+source+')')
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  pue_emissions = round((experimental_emissions+ dynamic_emissions)*pue)
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- st.metric(label="Dynamic and experimental emissions, considering PUE", value=str(pue_emissions)+' kilograms of CO2eq')
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  st.markdown('### Embodied Emissions 🖥️🔨')
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  st.markdown('##### These are the emissions associated with the materials and processes involved in producing'
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  ' the computing equipment needed for AI models.')
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  with st.expander("Calculate the embodied emissions of your model"):
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- st.markdown('These are the trickiest emissions to track down since a lot of the information needed is missing.')
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  st.markdown('##### Based on the number of GPUs and training time you indicated above, we can estimate that your model\'s embodied emissions are approximately: ')
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  hardware_type = TDP['type'][TDP['name'] == hardware].tolist()[0]
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  if hardware_type == 'cpu':
@@ -173,7 +172,8 @@ with st.expander("Calculate the embodied emissions of your model"):
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  embodied_type = embodied_gpu['Value'][embodied_gpu['Ratio']=='Manufacturing emissions per additionnal GPU Card (kgCO₂eq)'].tolist()[0]
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  embodied_emissions = round(int(embodied_type)*embodied_conversion_factor*float(num_gpus)*training_time/1000,1)
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  st.metric(label="Embodied emissions", value=str(embodied_emissions)+' kilograms of CO2eq')
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- st.markdown('This is a high-level estimate based on an hourly manufacturing emissions conversion factor (linearly ammortised) of 0.0289 [(source)](https://docs.google.com/spreadsheets/d/1DqYgQnEDLQVQm5acMAhLgHLD8xXCG9BIrk-_Nv6jF3k/).')
 
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  st.markdown('### Model Information ℹ️')
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  st.markdown('##### If you want to share the link to your model code or paper, please do so below! Otherwise, your submission will be anonymous.')
 
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  else:
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  st.markdown('##### The PUE of the datacenter you used is: '+ str(pue) + ' [(source)]('+source+')')
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  pue_emissions = round((experimental_emissions+ dynamic_emissions)*pue)
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+ st.metric(label="Your emissions, considering PUE", value=str(pue_emissions)+' kilograms of CO2eq')
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  st.markdown('### Embodied Emissions 🖥️🔨')
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  st.markdown('##### These are the emissions associated with the materials and processes involved in producing'
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  ' the computing equipment needed for AI models.')
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  with st.expander("Calculate the embodied emissions of your model"):
 
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  st.markdown('##### Based on the number of GPUs and training time you indicated above, we can estimate that your model\'s embodied emissions are approximately: ')
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  hardware_type = TDP['type'][TDP['name'] == hardware].tolist()[0]
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  if hardware_type == 'cpu':
 
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  embodied_type = embodied_gpu['Value'][embodied_gpu['Ratio']=='Manufacturing emissions per additionnal GPU Card (kgCO₂eq)'].tolist()[0]
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  embodied_emissions = round(int(embodied_type)*embodied_conversion_factor*float(num_gpus)*training_time/1000,1)
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  st.metric(label="Embodied emissions", value=str(embodied_emissions)+' kilograms of CO2eq')
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+ st.info('These are the trickiest emissions to track down since a lot of the information needed is missing. 🕵 '
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+ 'We are providing an estimate based on an hourly manufacturing emissions conversion factor [(source)](https://docs.google.com/spreadsheets/d/1DqYgQnEDLQVQm5acMAhLgHLD8xXCG9BIrk-_Nv6jF3k/).')
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  st.markdown('### Model Information ℹ️')
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  st.markdown('##### If you want to share the link to your model code or paper, please do so below! Otherwise, your submission will be anonymous.')