If the Sentence Transformers library throws a PyTorch CUDA error during training or inference, the issue is likely tied to GPU configuration, memory management, or software compatibility. Here’s how to address it:
1. Verify GPU Availability and Configuration
First, confirm that PyTorch recognizes your GPU by running torch.cuda.is_available(). If this returns False, check for:
- Driver/CUDA Toolkit Mismatches: Ensure NVIDIA drivers and the CUDA toolkit version match PyTorch’s requirements. For example, PyTorch 2.0+ often requires CUDA 11.8 or 12.x.
- Incorrect PyTorch Installation: Install the GPU-enabled PyTorch build using the correct command (e.g.,
pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu118for CUDA 11.8). - Hardware Limitations: Older GPUs (e.g., Kepler architecture) may not support newer PyTorch/CUDA versions.
Example Fix: If torch.cuda.is_available() fails, reinstall PyTorch with explicit CUDA support and update drivers.
2. Diagnose Memory Issues
CUDA errors like out of memory occur when the GPU’s VRAM is exhausted. To resolve this:
- Reduce Batch Size: Lower the
batch_sizeinDataLoaderor training arguments. - Free Memory: Use
torch.cuda.empty_cache()after deleting unused tensors. - Mixed Device Errors: Ensure all tensors and the model are on the same device (CPU/GPU). For example, if the model is on GPU (
model.to('cuda')), input data must be moved to GPU viatexts = texts.to('cuda').
Example Fix: If training crashes with CUDA OOM, reduce per_device_train_batch_size in the TrainingArguments of Sentence Transformers.
3. Check Software Compatibility
Incompatibilities between PyTorch, CUDA, and libraries like transformers or sentence-transformers can cause crashes.
- Version Alignment: Use
pip listto ensure PyTorch,transformers, andsentence-transformersversions are compatible. For example, Sentence Transformers 2.3+ requires PyTorch 2.0+. - Kernel Conflicts: Restart the Python process to clear stale CUDA contexts, especially after interrupted runs.
- Update Libraries: Run
pip install --upgrade sentence-transformers torchto resolve known bugs.
Example Fix: If using model.encode() triggers a CUDA error, downgrade to a stable version like sentence-transformers==2.2.2 and torch==1.13.1 to test for regressions.
Final Steps
If the error persists, run the code with CUDA_LAUNCH_BLOCKING=1 to get a detailed stack trace. For inference, test CPU-only mode with model.to('cpu') to isolate GPU-specific issues. Check the PyTorch and Sentence Transformers GitHub issue trackers for similar reports.