Resolving "version libcublasLt.so.11 not defined" Error in PyTorch with CUDA

error: version libcublasLt.so.11 not defined in file libcublasLt.so.11 with link time reference

Breakdown:

• libcublasLt.so.11: This refers to a shared library (.so on Linux, .dll on Windows) that provides CUDA Linear Algebra Solver (cuBLAS) functionality, specifically version 11.
• version ... not defined: The error indicates that PyTorch is trying to use a function or symbol that exists in cuBLAS version 11, but the actual cuBLAS library loaded doesn't have that function defined.
• link time reference: This suggests that the issue arose during the linking stage when the program was compiled. It was expecting a specific version of cuBLAS to be available, but it wasn't found.

Causes:

• Mismatched CUDA and cuBLAS Versions: PyTorch might be built against a specific CUDA version that requires cuBLAS version 11, but you have a different cuBLAS version installed.
• Incorrect Library Paths: The linker might not be able to find the cuBLAS library (libcublasLt.so.11) because the library path is not set correctly in your environment.
• Virtual Environment Issues: If you're using a virtual environment, cuBLAS might not be installed within that environment.

Solutions:

1. Verify CUDA and cuBLAS Compatibility:

   • Check your installed CUDA version using nvcc --version or cuda-ver.
   • Refer to PyTorch documentation for compatible cuBLAS versions for your CUDA version. You can usually find this information on the PyTorch installation page.
   • If necessary, reinstall cuBLAS with the correct version using tools like conda or apt-get (depending on your system).

2. Set Library Paths (if necessary):

   • Temporary fix (for testing): Add the directory containing libcublasLt.so.11 to your LD_LIBRARY_PATH environment variable before running your Python script. This is not ideal for long-term use, as it can affect other programs.
   • Recommended fix (system-wide): Edit your system's environment configuration file (e.g., .bashrc on Linux) to permanently add the path to LD_LIBRARY_PATH.

3. Address Virtual Environment Issues:

   • Activate your virtual environment.
   • Install the appropriate cuBLAS version compatible with your PyTorch installation within the virtual environment using conda or pip.

Example (using conda):

Assuming you need cuBLAS 11 for PyTorch and have a compatible CUDA version:

conda install cudatoolkit=11 -c pytorch  # Install CUDA toolkit 11 from PyTorch channel

Additional Tips:

• Consider using a package manager like conda to manage CUDA, cuBLAS, and PyTorch installations to ensure compatibility.
• If you're still facing issues, consult the PyTorch documentation or seek help on forums like PyTorch Discuss.

import torch

# Check if CUDA is available
if torch.cuda.is_available():
    device = torch.device("cuda")
    print("Using CUDA device:", device)
else:
    device = torch.device("cpu")
    print("Using CPU device.")

# Create some tensors on the chosen device
x = torch.randn(5, 3, device=device)
y = torch.randn(5, 3, device=device)

# Perform a CUDA-accelerated operation (if CUDA is available)
if device.type == "cuda":
    result = torch.matmul(x, y)
    print(result)
else:
    result = torch.matmul(x, y.cpu())  # Move y to CPU for CPU computation
    print(result)

This code first checks if a CUDA device is available and then allocates tensors on the appropriate device (CPU or GPU). If CUDA is available, it performs a matrix multiplication using torch.matmul on the GPU. Otherwise, it moves one of the tensors to CPU and performs the operation on CPU.

• This approach avoids the need to reinstall or adjust cuBLAS as long as a compatible PyTorch version exists.

Containerized Environment (Docker):

• If you're comfortable with Docker, consider creating a Docker image with the exact versions of CUDA, cuBLAS, and PyTorch that work together. This isolates your project's dependencies and avoids conflicts with your system's libraries.
• This method can be helpful for managing complex environments or sharing your project with others who need the same setup.

Cloud-Based GPU Instances:

• For large-scale computations or if you lack the necessary hardware, consider using cloud platforms like Google Colab, Amazon SageMaker, or Microsoft Azure that offer pre-configured virtual machines with GPUs and compatible software stacks.
• This eliminates the need for local setup and allows you to leverage powerful GPUs without managing hardware.

CPU-Only Execution:

• If your computations aren't highly GPU-dependent, you might be able to run PyTorch on your CPU. While this will be slower than GPU execution, it can be a simpler alternative if GPU support is causing issues.
• In your PyTorch code, explicitly set the device to "cpu":

device = torch.device("cpu")