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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() |