Pytorch says that CUDA is not available (on Ubuntu)

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06-10-2024

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"CUDA Not Available" on Ubuntu? Troubleshooting PyTorch GPU Acceleration

Scenario: You're excited to leverage the power of GPUs for your PyTorch deep learning projects on your Ubuntu system. But when you try to use a GPU, PyTorch throws the dreaded "CUDA not available" error. This can be frustrating, but fear not, we'll break down the problem and get you up and running!

Understanding the Problem:

The "CUDA not available" error means PyTorch can't find or access the CUDA toolkit on your system. CUDA is a parallel computing platform and API developed by NVIDIA that enables GPUs for general-purpose processing. Essentially, PyTorch needs CUDA to communicate with your GPU and utilize its power.

Replicating the Scenario and Code:

Let's assume you're trying to use a GPU in a basic PyTorch model training script:

import torch

if torch.cuda.is_available():
    device = torch.device('cuda')
    print("Using GPU")
else:
    device = torch.device('cpu')
    print("Using CPU")

# Your model training code here...

When you run this code and get the "CUDA not available" error, it means the torch.cuda.is_available() function returns False.

Troubleshooting the Issue:

Here's a step-by-step guide to diagnosing and resolving the "CUDA not available" error:

1. Install CUDA Toolkit:

   • Check for NVIDIA Drivers: Ensure you have the correct NVIDIA driver installed for your specific GPU model. You can find the latest drivers on the NVIDIA website.
   • Install CUDA: Download and install the CUDA Toolkit from the NVIDIA website (https://developer.nvidia.com/cuda-downloads). Choose the correct version compatible with your system and GPU.

2. Verify CUDA Installation:

   • Run the CUDA Samples: After installation, navigate to the CUDA Toolkit's samples directory and try running a sample code to confirm CUDA is working properly. This provides a basic sanity check.

3. Confirm PyTorch Installation:

   • Check for CUDA Support: When installing PyTorch, ensure you select the "CUDA" option for your installation. This will install the necessary components for GPU acceleration.

4. Environment Variables:

   • CUDA_PATH: Set the CUDA_PATH environment variable to point to your CUDA toolkit installation directory. This ensures PyTorch can find the necessary CUDA libraries. You can add this to your .bashrc or .zshrc file for a persistent setting.

5. Restart:

   • After making any changes: Restart your system after installing CUDA and setting environment variables to apply the changes.

Additional Insights:

• GPU Driver Compatibility: The NVIDIA driver version must be compatible with your CUDA toolkit version. Ensure both are up-to-date and aligned.
• System Resources: The "CUDA not available" error could also occur if your GPU is fully utilized by other applications or if your system lacks the necessary resources for GPU computing.

Example:

Here's how you might add the environment variable to your .bashrc file:

export CUDA_PATH=/usr/local/cuda-11.7

Replace /usr/local/cuda-11.7 with the actual installation path of your CUDA toolkit.

Conclusion:

Troubleshooting the "CUDA not available" error in PyTorch often boils down to verifying your NVIDIA driver and CUDA installation. Follow the steps provided, ensure compatibility, and double-check environment variables. With these checks, you should be able to leverage your GPU's power and accelerate your PyTorch training!