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sashavor
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e416978
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Parent(s):
1cf9ef7
making bigly changes
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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="
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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':
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@@ -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.
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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.')
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