Create app.py

app.py

@@ -0,0 +1,58 @@
+import streamlit as st
+import pandas as pd
+
+def calculate_cost(num_pairs, 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_time_minutes = num_pairs * time_per_pair
+    total_time_hours = total_time_minutes / 60
+    hourly_rate = daily_rate / 24
+    total_cost = total_time_hours * hourly_rate
+    
+    return total_cost
+
+st.set_page_config(page_title="GPU Cost Calculator", page_icon="🧮", layout="wide")
+
+st.title("GPU Cost Calculator")
+
+# Input for number of pairs
+num_pairs = st.number_input("Enter the number of pairs to process:", min_value=1, value=5)
+
+# Select GPU type
+gpu_type = st.selectbox(
+    "Select GPU type:",
+    ("Nvidia A100", "H100 80GB PCIe", "AWS p4d.24xlarge (8x A100)")
+)
+
+# Calculate button
+if st.button("Calculate Cost"):
+    cost = calculate_cost(num_pairs, gpu_type)
+    st.write(f"Estimated cost for processing {num_pairs} pairs on {gpu_type}: ${cost:.4f}")
+
+# Display GPU information
+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)