nvidia-cudnn-cu12

import tensorflow as tf import os # Ensure TensorFlow doesn't pre-allocate all GPU memory os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true' # Check if TensorFlow can detect and use GPUs gpus = tf.config.list_physical_devices('GPU') if gpus: print(f"TensorFlow detected the following GPUs: {gpus}") try: # Limit GPU memory growth to avoid allocating all memory at once (alternative to env var) for gpu in gpus: tf.config.experimental.set_memory_growth(gpu, True) print("GPU memory growth set to True.") except RuntimeError as e: # Memory growth must be set before GPUs have been initialized print(f"Error setting memory growth: {e}") print(f"TensorFlow is built with CUDA: {tf.test.is_built_with_cuda()}") # TensorFlow's built-in cuDNN version (indicates what TF was compiled with) print(f"TensorFlow's built-in cuDNN version: {tf.sysconfig.get_build_info().get('CUDNN_VERSION', 'N/A')}") # A small operation to trigger GPU usage if available try: with tf.device('/GPU:0'): a = tf.constant([[1.0, 2.0], [3.0, 4.0]]) b = tf.constant([[1.0, 1.0], [1.0, 1.0]]) c = tf.matmul(a, b) print(f"Simple matrix multiplication on GPU: {c.numpy()}") except RuntimeError as e: print(f"Could not run on GPU: {e}. Running on CPU instead.") a = tf.constant([[1.0, 2.0], [3.0, 4.0]]) b = tf.constant([[1.0, 1.0], [1.0, 1.0]]) c = tf.matmul(a, b) print(f"Simple matrix multiplication on CPU: {c.numpy()}") else: print("TensorFlow did not detect any GPUs. Please ensure CUDA and cuDNN are correctly installed and configured.")