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import streamlit as st
import pandas as pd
import time
import random
def calculate_cost(num_pairs, num_shirts, num_pants, gpu_type):
if gpu_type == "Nvidia A100":
daily_rate = 28
time_per_pair = 1 # minute
elif gpu_type == "H100 80GB PCIe":
daily_rate = 78.96
time_per_pair = 0.5 # assuming it's twice as fast
else: # AWS p4d.24xlarge
daily_rate = 786.48
time_per_pair = 0.25 # assuming it's four times as fast due to 8 GPUs
total_items = num_pairs + num_shirts + num_pants
total_time_minutes = total_items * (time_per_pair / 2) # Divide by 2 as per the new logic
total_time_hours = total_time_minutes / 60
hourly_rate = daily_rate / 24
total_cost = total_time_hours * hourly_rate
return total_cost
def generate_random_case(gpu_type):
new_case = {
'pairs': random.randint(0, 9),
'shirts': random.randint(0, 19),
'pants': random.randint(0, 19)
}
new_case['price'] = calculate_cost(new_case['pairs'], new_case['shirts'], new_case['pants'], gpu_type)
return new_case
def main():
st.set_page_config(page_title="Automated GPU Cost Calculator", page_icon="🧮", layout="wide")
st.title("Automated GPU Cost Calculator")
col1, col2 = st.columns(2)
with col1:
is_automated = st.toggle("Automate case generation")
gpu_type = st.selectbox(
"Select GPU type:",
("Nvidia A100", "H100 80GB PCIe", "AWS p4d.24xlarge (8x A100)")
)
with col2:
if not is_automated:
num_pairs = st.number_input("Number of pairs:", min_value=0, value=0)
num_shirts = st.number_input("Number of shirts:", min_value=0, value=0)
num_pants = st.number_input("Number of pants:", min_value=0, value=0)
if st.button("Calculate Cost"):
cost = calculate_cost(num_pairs, num_shirts, num_pants, gpu_type)
st.write(f"Estimated cost: ${cost:.4f}")
else:
num_pairs = num_shirts = num_pants = 0
cases = []
cost_placeholder = st.empty()
cases_placeholder = st.empty()
while is_automated:
new_case = generate_random_case(gpu_type)
cases.append(new_case)
num_pairs += new_case['pairs']
num_shirts += new_case['shirts']
num_pants += new_case['pants']
total_cost = calculate_cost(num_pairs, num_shirts, num_pants, gpu_type)
cost_placeholder.write(f"Total cost: ${total_cost:.4f}")
cases_text = "**Generated Cases**\n"
for i, case in enumerate(cases[-10:], 1): # Show only the last 10 cases
cases_text += f"* Case {i}: {case['pairs']} pairs, {case['shirts']} shirts, {case['pants']} pants = ${case['price']:.4f}\n"
cases_placeholder.markdown(cases_text)
time.sleep(5) # Generate a new case every 5 seconds
st.subheader("GPU Information")
gpu_data = {
"Provider": ["H100 80GB PCIe", "AWS (p4d.24xlarge)", "GPU Mart"],
"GPU": ["Nvidia H100", "Nvidia A100 (8 GPUs)", "Nvidia A100"],
"vCPUs": [16, 96, "Dual 18-Core E5-2697v4"],
"RAM": ["125 GB", "1152 GiB", "256 GB"],
"GPU Memory": ["80 GB", "320 GB (8 x 40 GB)", "40 GB HBM2e"],
"Instance Storage": ["Network Storage: 10Pb+", "8 x 1000 GB NVMe SSD", "240 GB SSD + 2TB NVMe + 8TB SATA"],
"Network Bandwidth": ["Not Specified", "400 Gbps", "100Mbps - 1Gbps"],
"On-Demand Price/hr": ["$3.29", "$32.77", "N/A"],
"Daily Price": ["$78.96", "$786.48", "$28.00"],
"Monthly Price": ["$2,368.80", "$23,594.40", "$799.00"],
"1-Year Reserved (Hourly)": ["N/A", "$19.22", "N/A"],
"3-Year Reserved (Hourly)": ["N/A", "$11.57", "N/A"]
}
df = pd.DataFrame(gpu_data)
st.table(df)
if __name__ == "__main__":
main() |