When using GPU resources in Notebook, how can I view the CUDA version information?

When using GPU resources in a Notebook environment, you can view the CUDA version information by executing specific commands in the terminal or a code cell within the Notebook. Here's how you can do it:

Using the Terminal: If your Notebook environment provides access to a terminal, you can open a terminal and type the following command:
```
nvcc --version
```
This command will display the installed CUDA compiler driver version, which corresponds to the CUDA toolkit version installed on your system.
Using Python Code: If you prefer to use Python within your Notebook, you can execute the following code snippet:
```
!nvcc --version
```
The exclamation mark (!) at the beginning of the command tells the Notebook to execute it as a shell command.
Using TensorFlow or PyTorch: If you are working with deep learning frameworks like TensorFlow or PyTorch, you can also check the CUDA version indirectly by checking the version of these libraries, as they are closely tied to specific CUDA versions. For example, in TensorFlow, you can use:
```
import tensorflow as tf
print("TensorFlow version:", tf.__version__)
print("GPU devices:", tf.config.list_physical_devices('GPU'))
```
Or in PyTorch:
```
import torch
print("PyTorch version:", torch.__version__)
print("CUDA available:", torch.cuda.is_available())
print("CUDA version:", torch.version.cuda)
```

These methods will help you determine the CUDA version that is currently available in your Notebook environment, which is crucial for ensuring compatibility with your GPU-accelerated applications.

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