Identifying Your CUDA Version

CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model developed by NVIDIA. When working with CUDA, it’s crucial to know the version installed on your system to ensure compatibility with your code, libraries (like PyTorch or TensorFlow), and hardware. This tutorial explains how to reliably identify your CUDA version using various methods.

Why is Knowing the CUDA Version Important?

Compatibility: CUDA libraries and applications are often compiled against specific CUDA versions. Using an incompatible version can lead to runtime errors or performance issues.
Library Support: Machine learning frameworks like PyTorch and TensorFlow provide CUDA-enabled versions for specific CUDA versions. Selecting the correct version ensures that your applications can utilize your GPU effectively.
Troubleshooting: When encountering issues, knowing the CUDA version is a key piece of information for debugging and seeking assistance.

Methods to Identify CUDA Version

Here are several ways to determine your CUDA version:

1. Using nvcc --version

The most straightforward method is to use the NVIDIA CUDA Compiler Driver (nvcc). Open a terminal and run the following command:

nvcc --version

This command will display the CUDA compiler version, which directly corresponds to the installed CUDA toolkit version. The output will show information like the compiler version, build date, and CUDA release version.

2. Using nvidia-smi

The nvidia-smi (NVIDIA System Management Interface) tool provides information about your NVIDIA GPUs. While it doesn’t directly display the CUDA toolkit version, it shows the highest CUDA version supported by your installed NVIDIA driver. This is useful information, but remember that the driver supports a range of CUDA versions.

nvidia-smi

Look for the "CUDA Version" field in the output.

3. Checking the version.txt file

The CUDA toolkit typically includes a version.txt file that contains the installed version information. You can view this file using a text editor or the cat command in a terminal:

cat /usr/local/cuda/version.txt

The output will typically be in the format "CUDA Version X.Y", where X and Y are the major and minor version numbers.

4. Using ls -l to List CUDA Installations

If you have multiple CUDA versions installed, you can use ls -l to list the available installations:

ls -l /usr/local | grep cuda

This will show symbolic links to different CUDA versions, allowing you to identify which version is currently active (the one pointed to by the cuda link).

Combining Methods for Robustness

To ensure accuracy and handle potential scenarios where one method fails, you can combine these approaches in a script. Here’s an example bash script to determine the CUDA version:

if nvcc --version 2>&1 > /dev/null; then
    # Determine CUDA version using default nvcc binary
    CUDA_VERSION=$(nvcc --version | sed -n 's/^.*release \([0-9]\+\.[0-9]\+\).*$/\1/p');

elif /usr/local/cuda/bin/nvcc --version 2>&1 > /dev/null; then
    # Determine CUDA version using /usr/local/cuda/bin/nvcc binary
    CUDA_VERSION=$(/usr/local/cuda/bin/nvcc --version | sed -n 's/^.*release \([0-9]\+\.[0-9]\+\).*$/\1/p');

elif [ -f "/usr/local/cuda/version.txt" ]; then
    # Determine CUDA version using /usr/local/cuda/version.txt file
    CUDA_VERSION=$(cat /usr/local/cuda/version.txt | sed 's/.* \([0-9]\+\.[0-9]\+\).*/\1/')

else
    CUDA_VERSION=""

fi

echo "CUDA Version: $CUDA_VERSION"

This script first tries nvcc --version, then checks for nvcc in a specific directory, and finally reads the version.txt file if available. This provides a robust way to determine the CUDA version even in complex environments.

Using the CUDA Version in Scripts

Once you’ve determined the CUDA version, you can use it in scripts to configure your environment or install compatible libraries. For example, you might use it to install the correct version of PyTorch or TensorFlow:

# Example: Installing PyTorch with the correct CUDA version
if [ -z "$CUDA_VERSION" ]; then
    echo "CUDA not found. Installing CPU-only version of PyTorch."
    python -m pip install torch torchvision
else
    python -m pip install torch torchvision -f https://download.pytorch.org/whl/torch_stable.html
fi