When working with NVIDIA GPUs and CUDA for deep learning (e.g., with PyTorch), the primary concern is ensuring compatibility between your GPU driver and the CUDA version used by PyTorch. The CUDA Toolkit installed via conda or system-wise is often a secondary consideration unless building custom CUDA code.

1. Understanding CUDA Version Reporting

• nvidia-smi shows the maximum CUDA version that your NVIDIA GPU driver supports. This is the CUDA Driver API version. It reflects the highest CUDA runtime version the driver can work with and is key for compatibility.
• nvcc --version shows the CUDA Toolkit version installed on your system—that is, the version of the CUDA runtime API used for compiling CUDA programs.
• These versions can differ because the driver version is backward compatible with older toolkit versions. For example, a driver may support CUDA 12.0, but your toolkit might be 11.7.

2. Checking Versions for Compatibility

To ensure PyTorch runs properly with CUDA support, the driver CUDA version must be equal to or higher than the CUDA version PyTorch was built with. PyTorch bundles its own CUDA runtime (usually from conda cudatoolkit or pip wheels), so system CUDA Toolkit version often does not impact PyTorch except when building extensions.

3. How to Check These Versions with Python (including in Colab)

You can use this Python code snippet to fetch and print the driver CUDA version, toolkit CUDA version, and PyTorch CUDA version:

import subprocess
import torch
import re

def get_driver_version():
    try:
        output = subprocess.check_output(['nvidia-smi']).decode()
        match = re.search(r'CUDA Version: (\\d+\\.\\d+)', output)
        if match:
            return float(match.group(1))
    except Exception as e:
        return f"Error getting driver version: {e}"

def get_toolkit_version():
    try:
        output = subprocess.check_output(['nvcc', '--version']).decode()
        match = re.search(r'release (\\d+\\.\\d+)', output)
        if match:
            return float(match.group(1))
    except Exception as e:
        return f"Error getting toolkit version: {e}"

driver_version = get_driver_version()
toolkit_version = get_toolkit_version()
torch_cuda_version = torch.version.cuda
torch_cuda_is_available = torch.cuda.is_available()

print(f"CUDA Driver Version (nvidia-smi): {driver_version}")
print(f"CUDA Toolkit Version (nvcc): {toolkit_version}")
print(f"PyTorch CUDA Version: {torch_cuda_version}")
print(f"Is CUDA available for PyTorch? {torch_cuda_is_available}")

if isinstance(driver_version, float) and torch_cuda_version is not None:
    if driver_version < float(torch_cuda_version):
        print("Warning: CUDA driver version is older than PyTorch CUDA version. Update your GPU driver.")
    else:
        print("CUDA driver version is compatible with or newer than PyTorch CUDA version.")

4. Role of Conda CUDA Toolkit in PyTorch Usage

• PyTorch wheels come pre-packaged with a CUDA runtime (often installed via conda cudatoolkit).
• This conda CUDA Toolkit version does not affect runtime compatibility as long as the GPU driver supports the PyTorch CUDA version.
• You mostly consider conda CUDA Toolkit only if you compile custom CUDA extensions or run CUDA code unrelated to PyTorch.

5. Summary and Recommendations

6. Practical Takeaway:

• Always ensure your NVIDIA GPU driver supports the CUDA version PyTorch needs.
• PyTorch CUDA version ≤ driver CUDA version → compatible and will run models.
• Conda or system-installed CUDA Toolkit version is a side check, important only for compiling CUDA code.