👀 Views: 27 💬 Answers: 1 📅 Created: 2025-06-07

django transformers pytorch gpu Python

I'm having trouble with I'm currently trying to integrate Hugging Face's `transformers` library into my Django application for a text generation feature. I'm using Django 3.2 and Python 3.9, and I'm attempting to load a pre-trained GPT-2 model to generate text based on user input. However, I am working with a `RuntimeError` that states `CUDA behavior: device-side assert triggered` when I try to run the model on my GPU. I've already verified that my CUDA setup is correct by running some simple PyTorch tests, and my GPU has enough memory available. Here's the code snippet where I'm loading and using the model: ```python from transformers import GPT2LMHeadModel, GPT2Tokenizer import torch def generate_text(prompt): model_name = 'gpt2' tokenizer = GPT2Tokenizer.from_pretrained(model_name) model = GPT2LMHeadModel.from_pretrained(model_name).to('cuda') # Move model to GPU inputs = tokenizer.encode(prompt, return_tensors='pt').to('cuda') # Inputs on GPU outputs = model.generate(inputs, max_length=50) generated = tokenizer.decode(outputs[0], skip_special_tokens=True) return generated prompt = 'Once upon a time' print(generate_text(prompt)) ``` I've also tried running the model on CPU by changing `.to('cuda')` to `.to('cpu')`, and it works without any issues, but it's significantly slower. I really want to leverage the GPU for performance reasons. I've checked my GPU memory usage, and at the time of running the model, it’s around 5GB, which should be fine. Could anyone guide to identify what might be going wrong with the GPU execution? Are there any specific configurations I should be aware of when using `transformers` with CUDA? Any insights would be greatly appreciated! My development environment is macOS. Any pointers in the right direction?