Assertionerror: torch not compiled with cuda enabled

The AssertionError: Torch not compiled with CUDA enabled is a common error encountered by users of PyTorch, a popular deep learning framework. This error usually appears when you try to leverage your GPU to accelerate computations, but PyTorch does not recognize CUDA, NVIDIA’s toolkit for parallel computing. In this article, we’ll dive into the causes of this error and provide step-by-step solutions to help you fix it and utilize your GPU effectively.

1. Understanding CUDA and PyTorch Compatibility

CUDA (Compute Unified Device Architecture) is a parallel computing platform developed by NVIDIA. CUDA allows certain programs, including PyTorch, to utilize NVIDIA GPUs, which can significantly speed up the execution of complex computations and deep learning models.

PyTorch, by default, can run on CPUs. To enable GPU support, PyTorch must be compiled with CUDA enabled. If this is not done, an attempt to perform CUDA-related operations will result in errors like the AssertionError: Torch not compiled with CUDA enabled.

2. Causes of Torch Not Compiled with CUDA Enabled Error

The error occurs primarily due to:

• Incorrect PyTorch installation: PyTorch installed without CUDA support cannot access the GPU.
• Missing or incompatible CUDA installation: Even if you have PyTorch compiled with CUDA, you need a compatible CUDA toolkit version on your machine.
• Mismatched PyTorch and CUDA versions: The PyTorch version you installed might not support the CUDA version on your system.

3. Step-by-Step Solution to Resolve the Error

Step 1: Verify CUDA Support

First, ensure that your machine has a CUDA-capable NVIDIA GPU.

1. Check your GPU model: Run the command nvidia-smi in your terminal. If you see details about your GPU, it means your system has a CUDA-capable GPU.
2. Check CUDA installation: If CUDA is installed, running nvcc --version should display the installed CUDA version.

If these commands don’t return expected outputs, you may need to install or update CUDA.

Step 2: Check PyTorch Installation

Verify if your existing PyTorch installation has CUDA support:

If torch.cuda.is_available() returns False, it indicates your current PyTorch version lacks CUDA support.

Step 3: Reinstall PyTorch with CUDA Support

The simplest way to resolve the issue is by reinstalling PyTorch with a version that supports CUDA. Follow these steps:

1. Choose a compatible CUDA version: Visit the PyTorch official website and select the PyTorch version that matches your CUDA toolkit.
2. Uninstall the current PyTorch version:
   bash

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   pip uninstall torch

3. Reinstall PyTorch with CUDA enabled: For instance, if you need PyTorch with CUDA 11.8, use:
   bash

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   pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118


   Replace cu118 with your CUDA version if it’s different.

Additional Verification

After reinstalling, run the following command to confirm CUDA is enabled:

If torch.cuda.is_available() returns True, your setup is now CUDA-enabled.

4. Using PyTorch with CUDA Successfully

Once your environment is correctly configured, here’s how to use your GPU in PyTorch:

• Set Device to GPU:
  python

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  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = MyModel().to(device)
data = data.to(device)

• Use GPU for computations: Transfer tensors and models to the GPU by calling .to(device).

5. Common FAQs

Q1: Do I need to install a specific NVIDIA driver for CUDA?

• Yes, each CUDA version requires a minimum NVIDIA driver version. Check compatibility on NVIDIA’s CUDA Toolkit page.

Q2: How do I know if my PyTorch supports CUDA?

• Run print(torch.version.cuda). If it shows a version, then your PyTorch installation has CUDA support.

Q3: Why am I still getting errors even after reinstalling PyTorch with CUDA?

• Ensure that your PyTorch and CUDA versions are compatible and that your NVIDIA driver supports the installed CUDA version.

Conclusion

The AssertionError: Torch not compiled with CUDA enabled can be frustrating but is typically straightforward to resolve by ensuring you have a compatible CUDA-enabled PyTorch installation. Following these steps, you should be able to harness the full power of your GPU for PyTorch operations.