Troubleshooting "AssertionError: Torch not compiled with CUDA enabled" in Python

Error Breakdown:

• AssertionError: This indicates that an assumption made by the program turned out to be false, causing it to halt.
• Torch not compiled with CUDA enabled: The error message signifies that the PyTorch library you're using wasn't built with support for NVIDIA's CUDA parallel computing framework, even though you might have upgraded your CUDA version.

Why It Happens:

1. Separate Compilation: PyTorch offers two installation options: with or without CUDA support. Installing the non-CUDA version won't enable hardware acceleration, even if you have a CUDA-compatible GPU.
2. Mismatched CUDA Versions: Sometimes, even with a CUDA-enabled PyTorch installation, incompatibility between the PyTorch build's CUDA version and your system's installed CUDA version can lead to this error.

Resolving the Issue:

1. Install CUDA-Enabled PyTorch:

   • Using conda:

     conda install -c pytorch pytorch torchvision cudatoolkit=<CUDA_version>
     

     Replace <CUDA_version> with the specific CUDA version you have installed (e.g., cudatoolkit=11.7).
   • Using pip:

     pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu<CUDA_version>/torch_stable.html
     

     Replace <CUDA_version> with the appropriate version string (e.g., cu117).

2. Verify CUDA Availability:

   import torch
   
   if torch.cuda.is_available():
       print("CUDA is available!")
   else:
       print("CUDA is not available.")
   

   Run this code to check if CUDA is now recognized by PyTorch.

Additional Tips:

• Conda Environment Management: If you're using Conda environments, ensure you're installing PyTorch with CUDA support within the correct environment. You can activate the environment using conda activate <environment_name>.
• GPU Selection: In some cases, you might need to explicitly specify which GPU to use with PyTorch. Refer to the PyTorch documentation for details.

By following these steps, you should be able to resolve the "AssertionError" and leverage CUDA acceleration in your PyTorch projects.

Example Codes:

This code snippet demonstrates how to verify if CUDA is available on your system using PyTorch:

import torch

if torch.cuda.is_available():
    print("CUDA is available! You can use GPU acceleration.")
    device = torch.device("cuda")  # Use GPU for tensor operations
else:
    print("CUDA is not available. Calculations will run on CPU.")
    device = torch.device("cpu")  # Use CPU for tensor operations

This code first imports the torch library. Then, it checks if CUDA is available using torch.cuda.is_available(). If CUDA is found, it prints a message and sets the device to "cuda" to utilize the GPU for computations. Otherwise, it sets the device to "cpu" for CPU-based calculations.

Simple CUDA-Enabled PyTorch Example (Matrix Multiplication):

This code demonstrates a basic matrix multiplication example using PyTorch on a GPU (assuming you have a CUDA-enabled PyTorch installation):

import torch

# Create random matrices on GPU (if available)
device = "cuda" if torch.cuda.is_available() else "cpu"
A = torch.randn(500, 1000, device=device)
B = torch.randn(1000, 200, device=device)

# Perform matrix multiplication on the chosen device
C = torch.mm(A, B)

print(C.shape)  # Output: torch.Size([500, 200])

This code first checks for CUDA availability and sets the device accordingly. It then creates two random tensors (matrices) A and B on the chosen device. Finally, it performs matrix multiplication using torch.mm and prints the resulting tensor's shape.

Remember:

• Replace <CUDA_version> in the installation commands with your specific CUDA version.
• Ensure you have a compatible NVIDIA GPU and the necessary drivers installed.

Cloud-Based Solutions:

CPU-Only Execution (if applicable):

• If your project doesn't require significant computational power, or if you don't have a CUDA-compatible GPU, you can continue using PyTorch on your CPU. While this won't benefit from hardware acceleration, it can still be viable for smaller tasks.

Choosing the Right Method:

• For most users, installing a CUDA-enabled PyTorch version using conda or pip is the simplest and recommended approach.
• If you don't have a suitable GPU or prefer a cloud-based environment, Google Colab or cloud TPUs can be suitable alternatives.
• CPU-only execution is a viable option for less computationally intensive tasks.
• Recompiling from source is only recommended for advanced users who have specific compilation requirements.

Remember to consider your project's needs, available resources, and your own technical expertise when selecting the most appropriate method.