cupy-cuda13x

import cupy as cp import numpy as np # Check if a GPU is available if cp.cuda.is_available(): print(f"CuPy is available. Current device: {cp.cuda.Device().id}") # Create a CuPy array on the GPU x_gpu = cp.arange(10, dtype=cp.float32).reshape(2, 5) print(f"GPU array:\n{x_gpu}") print(f"Type of GPU array: {type(x_gpu)}") # Perform a computation on the GPU y_gpu = x_gpu * 2 + 1 print(f"Result of computation on GPU:\n{y_gpu}") # Transfer the result back to CPU NumPy array y_cpu = cp.asnumpy(y_gpu) print(f"CPU array (from GPU):\n{y_cpu}") print(f"Type of CPU array: {type(y_cpu)}") # Demonstrate a simple NumPy-like operation sum_gpu = x_gpu.sum(axis=1) print(f"Sum along axis 1 on GPU: {sum_gpu}") print(f"Type of sum on GPU: {type(sum_gpu)}") # Ensure all GPU operations complete before proceeding (useful for timing) cp.cuda.Stream.null.synchronize() else: print("No NVIDIA GPU found or CuPy is not properly installed for CUDA.") print("Falling back to NumPy for demonstration.") x_cpu = np.arange(10, dtype=np.float32).reshape(2, 5) print(f"CPU array:\n{x_cpu}")