If the Sentence Transformers library throws a PyTorch CUDA error during training or inference, the issue typically stems from GPU-related configuration or resource constraints. These errors often occur due to mismatched CUDA/PyTorch versions, insufficient GPU memory, or incorrect device handling. The first step is to isolate the cause by checking error messages (e.g., “CUDA out of memory” vs. “device-side assert triggered”) and verifying your environment setup.
Start by confirming that CUDA is properly configured. Run torch.cuda.is_available() to ensure PyTorch detects the GPU. If this returns False, reinstall PyTorch with CUDA support using the correct version for your GPU. For example, if your GPU supports CUDA 11.8, install PyTorch via pip install torch==2.0.1+cu118. Next, check for memory issues: Training large models or using high batch sizes can exhaust GPU memory. Reduce the batch size (e.g., per_device_train_batch_size=16 instead of 32) or use mixed precision (fp16=True in TrainingArguments). Free cached memory with torch.cuda.empty_cache() after each training step if needed. Also, ensure data isn’t inadvertently stored on the CPU during GPU training—explicitly move tensors to the device with .to('cuda').
If version mismatches persist, verify compatibility between PyTorch, CUDA toolkit, and NVIDIA drivers. For example, PyTorch 2.0 requires CUDA 11.7/11.8 and driver versions ≥ 450.80.02. Use nvidia-smi to check driver versions and update them if necessary. For device-related errors (e.g., tensors on wrong devices), ensure your model and data are on the same device. A common mistake is loading a CPU-trained checkpoint onto a GPU without proper handling—use model.to('cuda') before inference. If the error remains, test with a minimal example (e.g., a tiny model and dataset) to rule out code-specific issues. Debugging CUDA errors often requires iterative testing, but methodically isolating components (hardware, drivers, code) simplifies resolution.