nvidia-cudnn-cu11

import os # This package primarily installs runtime libraries for deep learning frameworks. # Verification usually involves running a framework that utilizes cuDNN. # For example, with PyTorch: try: import torch print(f"PyTorch version: {torch.__version__}") if torch.cuda.is_available(): print(f"CUDA is available: {torch.cuda.is_available()}") print(f"CUDA device name: {torch.cuda.get_device_name(0)}") print(f"cuDNN enabled in PyTorch: {torch.backends.cudnn.enabled}") # Attempt a simple operation that would use cuDNN if available x = torch.randn(1, 3, 224, 224, device='cuda') conv = torch.nn.Conv2d(3, 64, 3, device='cuda') _ = conv(x) print("Successfully ran a simple CUDA/cuDNN operation with PyTorch.") else: print("CUDA is not available. cuDNN will not be used.") except ImportError: print("PyTorch not installed. Install it with 'pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118' (adjust CUDA version if needed).") except Exception as e: print(f"An error occurred during PyTorch verification: {e}") # If using TensorFlow, a similar check would apply: try: import tensorflow as tf print(f"TensorFlow version: {tf.__version__}") print(f"TensorFlow built with CUDA: {tf.test.is_built_with_cuda()}") print(f"TensorFlow GPU devices: {tf.config.list_physical_devices('GPU')}") if tf.test.is_built_with_cuda() and tf.config.list_physical_devices('GPU'): print("Successfully detected GPU and CUDA support in TensorFlow.") else: print("TensorFlow not using GPU/CUDA. Check installation.") except ImportError: print("TensorFlow not installed. Install it with 'pip install tensorflow[and-cuda]' (or specific versions).") except Exception as e: print(f"An error occurred during TensorFlow verification: {e}")