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import streamlit as st
import pandas as pd
import os, csv
from huggingface_hub import hf_hub_download, HfApi
HF_TOKEN = os.getenv('HUGGING_FACE_HUB_TOKEN')
CACHED_FILE_PATH = hf_hub_download(repo_id="sasha/co2_submissions", filename="dynamic_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="dynamic_emissions.csv",
path_in_repo="dynamic_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_id = "1DqYgQnEDLQVQm5acMAhLgHLD8xXCG9BIrk-_Nv6jF3k"
embodied_gpu_sheet_name = "Scope%203%20Ratios"
embodied_gpu_url = f"https://docs.google.com/spreadsheets/d/{embodied_gpu_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)
embodied_gpu = pd.read_csv(embodied_gpu_url)
st.title("AI Carbon Calculator")
st.markdown('## Estimate your model\'s CO2 carbon footprint!')
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.markdown('We will consider 3 aspects of your model: the dynamic emissions, idle emissions embodied emissions.')
st.markdown('### Dynamic Emissions')
with st.expander("Calculate the emissions produced by energy consumption of model training"):
with st.form(key='dynamic_emissions', clear_on_submit=True):
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.form_submit_button(label="Share my data", help="Submit the data from your model anonymously for research purposes!",\
on_click = lambda *args: write_to_csv(hardware, training_time, provider, carbon_intensity, dynamic_emissions))
st.markdown('### Idle Emissions')
st.markdown('Do you know what the PUE (Power Usage Effectiveness) of your infrastructure is?')
st.markdown('### Embodied Emissions')
st.markdown('Choose your hardware, runtime and cloud provider/physical infrastructure to estimate the carbon impact of your research.')
st.markdown('#### More information about our Methodology')
st.image('images/LCA_CO2.png', caption='The LCA methodology - the parts in green are those we focus on.')
modelname = st.selectbox('Choose a model to test', TDP)
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