app.py

import streamlit as st
import pandas as pd
import os, csv
from huggingface_hub import hf_hub_download, HfApi
import math

HF_TOKEN = os.getenv('HUGGING_FACE_HUB_TOKEN')

CACHED_FILE_PATH = hf_hub_download(repo_id="sasha/co2_submissions", filename="co2_emissions.csv", repo_type="dataset")

api = HfApi()

def write_to_csv(hardware, training_time, provider, carbon_intensity, dynamic_emissions):
    with open(CACHED_FILE_PATH,'a', newline='') as f:
        writer = csv.writer(f)
        writer.writerow([hardware, training_time, provider, carbon_intensity, dynamic_emissions])
    api.upload_file(
        path_or_fileobj=CACHED_FILE_PATH,
        path_in_repo="co2_emissions.csv",
        repo_id="sasha/co2_submissions",
        repo_type="dataset",
    )

st.set_page_config(
    page_title="AI Carbon Calculator",
    layout="wide",
)



tdp_url = "https://raw.githubusercontent.com/mlco2/impact/master/data/gpus.csv"
compute_url = "https://raw.githubusercontent.com/mlco2/impact/master/data/impact.csv"


electricity_url = "https://raw.githubusercontent.com/mlco2/impact/master/data/2021-10-27yearly_averages.csv"

server_sheet_id = "1DqYgQnEDLQVQm5acMAhLgHLD8xXCG9BIrk-_Nv6jF3k"
server_sheet_name = "Server%20Carbon%20Footprint"
server_url = f"https://docs.google.com/spreadsheets/d/{server_sheet_id}/gviz/tq?tqx=out:csv&sheet={server_sheet_name}"


embodied_gpu_sheet_name = "Scope%203%20Ratios"
embodied_gpu_url = f"https://docs.google.com/spreadsheets/d/{server_sheet_id}/gviz/tq?tqx=out:csv&sheet={embodied_gpu_sheet_name}"

TDP =pd.read_csv(tdp_url)

instances = pd.read_csv(compute_url)
providers = [p.upper() for p in instances['provider'].unique().tolist()]
providers.append('Local/Private Infastructure')

kg_per_mile = 0.348

electricity = pd.read_csv(electricity_url)
servers = pd.read_csv(server_url)
#print(servers.columns)
embodied_gpu = pd.read_csv(embodied_gpu_url)
#print(embodied_gpu.columns)

#st.image('images/MIT_carbon_image_narrow.png', use_column_width=True, caption = 'Image credit: ')
st.title("AI Carbon Calculator")

st.markdown('## Estimate your AI model\'s CO2 carbon footprint! 🌎🖥️🌎')

st.markdown('##### The calculators below will help you calculate different aspects of your model\'s carbon footprint, as we did for'
            ' BLOOM 🌸, a 176-billion parameter language model [(see our preprint!)](https://arxiv.org/abs/2211.02001)')
st.markdown('##### Don\'t forget to share your data to help us get a better idea of AI model\'s carbon emissions!')

st.markdown('### Dynamic Emissions 🚀')
st.markdown('##### These are the emissions produced by generating the electricity necessary for powering model training.')
with st.expander("Calculate the dynamic emissions of your model"):
    col1, col2, col3, col4 = st.columns(4)
    with col1:
        hardware = st.selectbox('GPU used', TDP['name'].tolist())
        gpu_tdp = TDP['tdp_watts'][TDP['name'] == hardware].tolist()[0]
        st.markdown("Different GPUs have different TDP (Thermal Design Power), which impacts how much energy you use.")
    with col2:
       training_time = st.number_input('Total number of GPU hours')
       st.markdown('This is calculated by multiplying the number of GPUs you used by the training time: '
                   'i.e. if you used 100 GPUs for 10 hours, this is equal to 100x10 = 1,000 GPU hours.')
    with col3:
       provider = st.selectbox('Provider used', providers)
       st.markdown('If you can\'t find your provider here, select "Local/Private Infrastructure".')
    with col4:
        if provider != 'Local/Private Infastructure':
            provider_instances = instances['region'][instances['provider'] == provider.lower()].unique().tolist()
            region = st.selectbox('Provider used', provider_instances)
            carbon_intensity = instances['impact'][(instances['provider'] == provider.lower()) & (instances['region'] == region)].tolist()[0]

        else:
            carbon_intensity = st.number_input('Carbon intensity of your energy grid, in grams of CO2 per kWh')
            st.markdown('You can consult a resource like the [IEA](https://www.iea.org/countries) or '
                        ' [Electricity Map](https://app.electricitymaps.com/) to get this information.')
    dynamic_emissions = round(gpu_tdp * training_time * carbon_intensity/1000000)
    st.metric(label="Dynamic emissions", value=str(dynamic_emissions)+' kilograms of CO2eq')
    st.markdown('This is roughly equivalent to '+ str(round(dynamic_emissions/kg_per_mile,1)) + ' miles driven in an average US car'
    ' produced in 2021. [(Source: energy.gov)](https://www.energy.gov/eere/vehicles/articles/fotw-1223-january-31-2022-average-carbon-dioxide-emissions-2021-model-year)')

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.markdown('##### 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)'
                        ' 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(0.0)+' kilograms of CO2eq')

st.markdown('### Idle 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"):
    st.markdown('##### 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]
    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:')
            if provider == 'AWS':
                pue = 1.135
                st.markdown('#### ' + str(pue)+ " [(source)](https://www.cloudcarbonfootprint.org/docs/methodology/)")
            elif provider == 'GCP':
                pue = 1.1
                st.markdown('#### ' + str(pue) + " [(source)](https://www.google.ca/about/datacenters/efficiency/)")
            elif provider == 'AZURE':
                pue = 1.185
                st.markdown('#### ' + str(pue) + " [(source)](https://www.cloudcarbonfootprint.org/docs/methodology/)")
            elif provider == 'OVH':
                pue = 1.28
                st.markdown('#### ' + str(pue) + " [(source)](https://corporate.ovhcloud.com/en-ca/sustainability/environment/)")
            elif provider == 'SCALEWAY':
                pue = 1.35
                st.markdown('#### ' +str(pue) + " [(source)](https://pue.dc3.scaleway.com/en/)")

        else:
            st.markdown('##### Try to find the PUE of your local infrastructure. Otherwise, you can use the industry average, 1.58:')
            pue = st.number_input('Total number of GPU hours', value = 1.58)
    else:
        st.markdown('##### The PUE of the datacenter you used is: ')
        st.markdown('#### '+ str(pue))
    pue_emissions = round((experimental_emissions+ dynamic_emissions)*pue)
    st.metric(label="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'
            ' the computing equipment needed for AI models.')
with st.expander("Calculate the embodied emissions of your model"):
        st.markdown('##### These are the trickiest emissions to track down since a lot of the information needed is missing!')

m = st.markdown("""
<style>
div.stButton > button:first-child {
    background-color: rgb(80, 200, 120);
    background-image: none;
    font-size: 20px;
    height: 3em;
}
</style>""", unsafe_allow_html=True)
buttoncol1, cuttoncol2, buttoncol3 = st.columns(3)
with cuttoncol2:
    st.button(label="Anonymously share my data!", on_click = lambda *args: write_to_csv(hardware, training_time, provider, carbon_intensity, dynamic_emissions))

st.markdown('### Methodology')
with st.expander("More information about our Methodology"):
    st.markdown('Building on the work of the [ML CO2 Calculator](https://mlco2.github.io/impact/), this tool allows you to consider'
                ' other aspects of your model\'s carbon footprint based on the LCA methodology.')
    st.image('images/LCA_CO2.png', caption='The LCA methodology - the parts in green are those we focus on.')