fixing PUE

app.py

@@ -116,9 +116,9 @@ st.markdown('### Experimental Emissions 👩‍🔬')
 st.markdown('##### These are the emissions produced by generating the electricity necessary for powering the experiments and tests needed to pick your final model architecture '
             'and parameters.')
 with st.expander("Calculate the experimental emissions of your model"):
-            st.info('Consult your training logs to figure out how many ablations, baselines and experiments were run before converging on the final model.')
-            experimentation_time = st.number_input(label='Number of hours of experimentation run', value=training_time)
-            st.markdown('##### As a baseline, language models such as [OPT](https://arxiv.org/pdf/2205.01068.pdf) and [BLOOM](https://arxiv.org/abs/2211.02001)'
+            #st.info('Consult your training logs to figure out how many ablations, baselines and experiments were run before converging on the final model.')
+            experimentation_time = st.number_input(label='Number of hours of experimentation (including ablations, baselines and evaluation)', value=training_time)
+            st.info('As a baseline, language models such as [OPT](https://arxiv.org/pdf/2205.01068.pdf) and [BLOOM](https://arxiv.org/abs/2211.02001)'
                         ' found that experimentation roughly doubles the amount of compute used by training the model itself.')
             experimental_emissions = round(gpu_tdp * (experimentation_time) * carbon_intensity/1000000)
             st.metric(label="Experimental emissions", value=str(experimental_emissions)+' kilograms of CO2eq')
@@ -126,10 +126,11 @@ with st.expander("Calculate the experimental emissions of your model"):
 st.markdown('### Datacenter (Overhead) Emissions 🌐')
 st.markdown('##### These are the emissions produced by generating the electricity needed to power the rest of the infrastructure'
             'used for model training -- the datacenter, network, heating/cooling, storage, etc.')
-with st.expander("Calculate the idle emissions of your model"):
+with st.expander("Calculate the datacenter emissions of your model"):
     st.info('A proxy often used to reflect idle emissions is PUE (Power Usage Effectiveness), which represents '
                 ' the ratio of energy used for computing overheads like cooling, which varies depending on the data center.')
     pue = instances['PUE'][(instances['provider'] == provider.lower()) & (instances['region'] == region)].tolist()[0]
+    source = instances['PUE source'][(instances['provider'] == provider.lower()) & (instances['region'] == region)].tolist()[0]
     if math.isnan(pue) == True:
         if provider != 'Local/Private Infastructure':
             st.markdown('##### The exact information isn\'t available for this datacenter! We will use your provider\'s average instead, which is:')
@@ -153,10 +154,9 @@ with st.expander("Calculate the idle emissions of your model"):
             st.markdown('##### Try to find the PUE of your local infrastructure. Otherwise, you can use the industry average, 1.58:')
             pue = st.slider('Total number of GPU hours', value = 1.58)
     else:
-        st.markdown('##### The PUE of the datacenter you used is: ')
-        st.markdown('#### '+ str(pue))
+        st.markdown('##### The PUE of the datacenter you used is: '+ str(pue) + ' [(source)]('+source+')')
     pue_emissions = round((experimental_emissions+ dynamic_emissions)*pue)
-    st.metric(label="Emissions considering PUE", value=str(pue_emissions)+' kilograms of CO2eq')
+    st.metric(label="Dynamic and experimental emissions, considering PUE", value=str(pue_emissions)+' kilograms of CO2eq')
 
 st.markdown('### Embodied Emissions 🖥️🔨')
 st.markdown('##### These are the emissions associated with the materials and processes involved in producing'