"AssertionError: Torch not compiled with CUDA enabled" in spite upgrading to CUDA version

"AssertionError: Torch not compiled with CUDA enabled" in spite upgrading to CUDA version

The error message "AssertionError: Torch not compiled with CUDA enabled" typically occurs when you're trying to use CUDA functionality in PyTorch, but your PyTorch installation wasn't compiled with CUDA support enabled.

Upgrading your CUDA version alone won't fix the issue if your PyTorch installation was not originally compiled with CUDA support. Here's what you can do to resolve this issue:

  1. Check PyTorch Installation: Verify whether your current PyTorch installation has CUDA support enabled or not. You can do this by opening a Python interpreter and importing PyTorch:

    import torch print(torch.cuda.is_available())

    If this code prints False, it indicates that your current PyTorch installation doesn't have CUDA support enabled.

  2. Reinstall PyTorch with CUDA: To enable CUDA support in PyTorch, you need to install or reinstall PyTorch with the appropriate CUDA version. If you've upgraded your CUDA version, you need to ensure that PyTorch is compiled against the same CUDA version.

    Use pip to uninstall the existing PyTorch version:

    pip uninstall torch

    Then, reinstall PyTorch with the desired CUDA version:

    pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html

    Replace cu111 with your specific CUDA version.

  3. Verify CUDA Version Compatibility: Make sure that the CUDA version you're using is compatible with the PyTorch version you're installing. Refer to the official PyTorch website or documentation to check the CUDA version compatibility matrix.

  4. Environment Variables: Ensure that your PATH and LD_LIBRARY_PATH environment variables are correctly configured to point to the CUDA installation directory. This is important for CUDA-enabled libraries to function correctly.

  5. Virtual Environments: If you're using virtual environments, ensure that you activate the virtual environment before installing or upgrading packages. This ensures that the correct packages are installed within the virtual environment.

  6. Clean Installation: If you continue to face issues, consider creating a new virtual environment, reinstalling the appropriate CUDA toolkit, and then reinstalling PyTorch with CUDA support.

Remember that successful CUDA support requires a compatible combination of PyTorch, CUDA toolkit, and GPU drivers. Ensure that all components are compatible and correctly configured for your system.

Examples

  1. "Fix 'Torch not compiled with CUDA enabled' after upgrading CUDA"

    • Verifies if PyTorch is installed with CUDA support. The error may persist if the PyTorch installation isn't compiled with CUDA.
    import torch # Check if PyTorch is built with CUDA support assert torch.cuda.is_available(), "Torch not compiled with CUDA enabled"

  2. "Reinstall PyTorch with CUDA support"

    • Reinstalls PyTorch with the correct CUDA version to resolve the AssertionError.
    # Find out your CUDA version nvcc --version # Example: 11.7 # Reinstall PyTorch with the correct CUDA support pip uninstall torch pip install torch --extra-index-url https://download.pytorch.org/whl/cu117

  3. "Check if CUDA is installed correctly"

    • Determines if the CUDA toolkit is properly installed and accessible.
    # Check CUDA installation nvcc --version # Should display CUDA version nvidia-smi # Should display GPU information

  4. "Ensure correct CUDA version for PyTorch"

    • Ensures that PyTorch is installed with the correct CUDA version, matching the installed toolkit.
    # Find installed CUDA version nvcc --version # Note this version # Ensure PyTorch uses the correct CUDA version import torch assert torch.version.cuda == "11.7", "Incorrect CUDA version for PyTorch"

  5. "Verify GPU drivers for CUDA compatibility"

    • Checks if the GPU drivers are compatible with the installed CUDA version.
    # Check GPU information nvidia-smi # Should display driver version and GPU status # Verify compatibility with CUDA toolkit driver_version=$(nvidia-smi --query-gpu=driver_version --format=csv,noheader) echo "Driver version: $driver_version"

  6. "Reinstall CUDA toolkit for PyTorch"

    • Reinstalls the CUDA toolkit to ensure it is properly configured and compatible with PyTorch.
    # Reinstall CUDA toolkit sudo apt-get update sudo apt-get install nvidia-cuda-toolkit

  7. "Check PyTorch CUDA configuration"

    • Confirms that PyTorch recognizes CUDA and the GPU.
    import torch # Check CUDA availability and GPU count assert torch.cuda.is_available(), "CUDA not available in PyTorch" gpu_count = torch.cuda.device_count() print(f"Number of GPUs: {gpu_count}")

  8. "Resolve 'Torch not compiled with CUDA enabled' after CUDA upgrade"

    • Indicates the potential mismatch between PyTorch and the CUDA toolkit after an upgrade.
    # Uninstall and reinstall PyTorch with correct CUDA version pip uninstall torch pip install torch --extra-index-url https://download.pytorch.org/whl/cu117

  9. "Fix AssertionError due to incorrect CUDA installation in PyTorch"

    • This may occur if the CUDA toolkit is installed incorrectly or is missing necessary components.
    # Reinstall CUDA toolkit with all dependencies sudo apt-get update sudo apt-get install nvidia-cuda-toolkit

  10. "Diagnose 'Torch not compiled with CUDA enabled' error"

    • This involves checking for common issues leading to the AssertionError, such as missing libraries or incorrect environment variables.
    # Check if CUDA environment variables are set echo $PATH # Ensure CUDA paths are in PATH echo $LD_LIBRARY_PATH # Ensure CUDA libraries are in LD_LIBRARY_PATH # Check if PyTorch has CUDA support import torch assert torch.cuda.is_available(), "PyTorch does not have CUDA support"

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