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import gradio as gr
import spaces
import torch
import time
import numpy as np
print(f"torch_version: {torch.__version__}")
# Define two matrices using NumPy arrays
#A = np.array([[1, 2], [3, 4]])
#B = np.array([[5, 6], [7, 8]])
# Define large matrices
A = np.random.rand(10000, 10000) # Random 10000x10000 matrix
B = np.random.rand(10000, 10000)
# Start the timer
start_time = time.time()
# Perform matrix multiplication
result = np.dot(A, B)
# End the timer
end_time = time.time()
# Calculate and print the time taken
print(f"Time taken for matrix multiplication with NumPy: {end_time - start_time:.6f} seconds")
# Define two matrices
#A = torch.tensor([[1, 2], [3, 4]])
#B = torch.tensor([[5, 6], [7, 8]])
# Define large matrices
A = torch.rand(10000, 10000) # Random 10000x10000 matrix
B = torch.rand(10000, 10000)
# Start the timer
start_time = time.time()
# Perform matrix multiplication
result = torch.matmul(A, B)
# End the timer
end_time = time.time()
# Calculate and print the time taken
print(f"Time taken for matrix multiplication with PyTorch: {end_time - start_time:.6f} seconds")
@spaces.GPU
def zeroGPU_test(text):
# Define two matrices
#A = torch.tensor([[1, 2], [3, 4]])
#B = torch.tensor([[5, 6], [7, 8]])
# Define large matrices
A = torch.rand(10000, 10000).to('cuda') # Random 10000x10000 matrix
B = torch.rand(10000, 10000).to('cuda')
# Start the timer
start_time = time.time()
# Perform matrix multiplication
result = torch.matmul(A, B)
# End the timer
end_time = time.time()
print(f"Time taken for matrix multiplication with GPU: {end_time - start_time:.6f} seconds")
return f"Time: {end_time - start_time:.6f} seconds"
demo = gr.Interface(fn=zeroGPU_test, inputs=gr.Text(), outputs=gr.Text())
demo.launch()
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