Description
In several cases, TensorFlow fails to detect or utilize available NVIDIA GPUs, even when the system is correctly configured with the appropriate hardware, drivers, CUDA, and cuDNN versions. This issue has been observed across different environments and setups.

Impact
This significantly limits model training performance and efficiency, especially for deep learning tasks that depend heavily on GPU acceleration. It leads to increased training time and resource usage.

Observed Behavior
tf.config.list_physical_devices('GPU') returns an empty list.

nvidia-smi detects the GPU and shows proper driver installation.

No explicit TensorFlow errors are thrown, making it hard to diagnose.

Expected Behavior
TensorFlow should detect and utilize the available NVIDIA GPU for training when all required dependencies and drivers are correctly installed.

System Example (can be modified by the user)
OS: Ubuntu 22.04 LTS

TensorFlow Version: 2.15.0

CUDA Version: 12.1

cuDNN Version: 8.x

GPU: NVIDIA RTX 3060, 12 GB

Installed via: pip

Suggested Improvements
Provide clearer diagnostic messages when GPU detection fails.

Add automated GPU environment checks with recommendations.

Consider offering a CLI or script to verify system compatibility before installation.