nvidia-cufile-cu12

# The 'nvidia-cufile-cu12' package provides the underlying C/C++ cuFile libraries. # To interact with cuFile from Python, you typically use the 'cuda-python' package. # Install cuda-python: pip install cuda-python numpy import numpy as np from cuda.bindings import cufile, driver # NOTE: This quickstart is conceptual and requires a system with GPUDirect Storage # enabled, a compatible filesystem, and appropriate NVIDIA hardware/driver setup. # A simple 'hello world' is not feasible without such infrastructure. def conceptual_cufile_usage(): print("Initializing CUDA driver and cuFile (conceptual)...") try: # Initialize CUDA driver (required for cuFile operations) driver.cuInit(0) # Open cuFile driver cufile.driver_open() # --- Example: Hypothetical buffered read/write setup --- # In a real scenario, you'd perform operations like: # 1. Allocate GPU memory (e.g., using CuPy or PyTorch on device) # 2. Register the buffer with cuFile (cufile.buf_register) # 3. Open a file for GPUDirect Storage (e.g., POSIX open on a supported filesystem) # 4. Register the file handle with cuFile (cufile.handle_register) # 5. Perform I/O operations (cufile.read, cufile.write) # 6. Deregister handles and buffers (cufile.handle_deregister, cufile.buf_deregister) print("cuFile driver opened successfully. Real I/O requires extensive setup.") print("Please refer to NVIDIA GPUDirect Storage documentation for full usage.") except Exception as e: print(f"An error occurred during conceptual cuFile initialization: {e}") finally: try: # Close cuFile driver cufile.driver_close() print("cuFile driver closed (conceptual).") except Exception as e: print(f"Error closing cuFile driver: {e}") if __name__ == "__main__": conceptual_cufile_usage()