Hugging Face Transformers AssertionError: Torch not compiled with CUDA enabled

What is Hugging Face Transformers AssertionError: Torch not compiled with CUDA enabled

Understanding Hugging Face Transformers

Hugging Face Transformers is a popular library designed for natural language processing (NLP) tasks. It provides pre-trained models and tools to work with state-of-the-art transformer architectures like BERT, GPT, and T5. These models are widely used for tasks such as text classification, translation, and summarization.

Identifying the Symptom

When working with Hugging Face Transformers, you might encounter the following error message: AssertionError: Torch not compiled with CUDA enabled. This error typically arises when attempting to run models on a GPU, but the underlying PyTorch installation does not support CUDA.

Explaining the Issue

What is CUDA?

CUDA (Compute Unified Device Architecture) is a parallel computing platform and application programming interface (API) model created by NVIDIA. It allows developers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing.

Why the Error Occurs

The error AssertionError: Torch not compiled with CUDA enabled indicates that your current PyTorch installation does not have CUDA support. This means that even if you have a CUDA-capable GPU, PyTorch cannot utilize it for computations, leading to this assertion error when attempting to use GPU acceleration.

Steps to Fix the Issue

Step 1: Verify CUDA Compatibility

Ensure that your system has a CUDA-capable GPU and that the appropriate NVIDIA drivers are installed. You can check your GPU compatibility on the NVIDIA CUDA GPUs page.

Step 2: Install PyTorch with CUDA Support

To resolve the issue, you need to install a version of PyTorch that supports CUDA. Follow these steps:

Visit the PyTorch Get Started page. Select your operating system, package manager, and the appropriate CUDA version for your GPU. Copy the provided installation command. For example, for a Linux system with CUDA 11.7, the command might look like this:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117

Step 3: Verify the Installation

After installing, verify that PyTorch can detect your GPU by running the following Python commands:

import torchprint(torch.cuda.is_available()) # Should return Trueprint(torch.cuda.get_device_name(0)) # Should return your GPU name

Conclusion

By ensuring that PyTorch is installed with CUDA support, you can leverage the power of your GPU for faster model training and inference with Hugging Face Transformers. For further assistance, refer to the PyTorch Discussion Forums or the Hugging Face Transformers Documentation.