Troubleshooting "RuntimeError: No CUDA GPUs available" in WSL2 PyTorch with RTX3080

This error message indicates that PyTorch, a deep learning framework, cannot detect your NVIDIA RTX3080 GPU for hardware acceleration. Even though you have a powerful GPU, PyTorch isn't configured to utilize it for computations, potentially leading to slower performance.

Resolving the Issue:

Here are the common causes and solutions to address this error:

1. Missing or Incompatible CUDA Toolkit:

   • CUDA (Compute Unified Device Architecture) is a parallel computing platform from NVIDIA that allows general-purpose computing on GPUs. PyTorch relies on CUDA for GPU acceleration.
   • Solution:
     • Ensure you have the correct CUDA toolkit version installed. Refer to the official PyTorch documentation for compatible CUDA versions with your PyTorch installation method (pip, conda, etc.). You can find this information on the PyTorch website.
     • Install the appropriate CUDA toolkit using the package manager for your WSL2 distribution (e.g., apt install cuda for Ubuntu).

2. Incorrect PyTorch Installation:

   • The PyTorch version you've installed might not be built with CUDA support.

3. NVIDIA Driver Issues:

   • Outdated or incompatible NVIDIA drivers can prevent PyTorch from recognizing the GPU.
   • Solution:

4. WSL2 Configuration:

   • In some cases, WSL2 might require additional configuration to allow applications within the Linux environment to access the GPU.
   • Solution (Advanced):

Verification:

Once you've attempted these solutions, try verifying if PyTorch can detect your GPU:

2. Run the following Python code:

   import torch
   
   if torch.cuda.is_available():
       print("CUDA is available! You can use GPU acceleration.")
   else:
       print("CUDA is not available.")
   

If it outputs "CUDA is available!", you've successfully resolved the issue.

Additional Tips:

• If you continue to face issues, consider searching online forums or communities for solutions specific to your WSL2 distribution, PyTorch version, and CUDA toolkit version.
• Double-check firewall settings or antivirus software that might be interfering with communication between PyTorch and the GPU.

import torch

if torch.cuda.is_available():
    print("CUDA is available! You can use GPU acceleration.")
    # Get the number of available GPUs
    num_gpus = torch.cuda.device_count()
    print(f"Number of GPUs available: {num_gpus}")
else:
    print("CUDA is not available.")

This code first imports the torch library. Then, it checks if torch.cuda.is_available() returns True, indicating CUDA is accessible. If so, it prints a success message and retrieves the number of available GPUs using torch.cuda.device_count(). Otherwise, it prints an error message.

Running a Simple Matrix Multiplication on GPU (if CUDA is available):

import torch

if torch.cuda.is_available():
    device = torch.device("cuda")  # Use GPU for computations
    print("Using GPU for calculations.")
else:
    device = torch.device("cpu")  # Fallback to CPU
    print("Using CPU for calculations.")

# Create some sample tensors
a = torch.randn(1000, 1000, device=device)
b = torch.randn(1000, 1000, device=device)

# Perform matrix multiplication on the chosen device
result = torch.mm(a, b)

print(f"Result tensor is on device: {result.device}")

This code extends the previous one by defining a device variable. If CUDA is available, it sets the device to "cuda" to utilize the GPU. Otherwise, it defaults to "cpu". Then, it creates random tensors (a and b) on the chosen device. Finally, it performs matrix multiplication (torch.mm) using the allocated device and prints the device where the resulting tensor (result) resides.

• If the issue persists with the latest PyTorch version, consider downgrading to a known-compatible version with your CUDA toolkit. Refer to the PyTorch documentation for compatible combinations. You can use pip or conda to install a specific version, specifying the desired CUDA version during installation.

Containerization (Docker):

• If other methods fail, containerization with Docker can provide a more isolated environment. This approach might be helpful if there are conflicts with system-wide configurations or dependencies in your WSL2 setup.
  • Install Docker Desktop for Windows, which includes support for WSL2.
  • Create a Dockerfile that specifies the base image (e.g., Ubuntu with NVIDIA container toolkit) and installs the necessary dependencies (CUDA toolkit, PyTorch with CUDA support).
  • Build and run the Docker container, mounting your code directory for access within the container.
  • Inside the container, PyTorch should be able to detect the GPU if the container is configured correctly.

Cloud-Based GPU Acceleration:

• If your local setup continues to be problematic, consider using cloud-based GPU instances offered by providers like Google Colab, Amazon SageMaker, or Microsoft Azure. These services provide pre-configured environments with powerful GPUs accessible through a web interface or API.

Important Considerations:

• Downgrading PyTorch might limit access to newer features and functionalities.
• Containerization adds complexity but can improve isolation and reproducibility.
• Cloud solutions incur costs depending on usage and resource requirements.