To troubleshoot a slow or stuck fine-tuning process, start by verifying hardware utilization and data pipeline efficiency. Use monitoring tools like nvidia-smi (for GPU) or system resource monitors to check if your GPU/CPU, RAM, or VRAM are maxed out. For example, if GPU utilization is low (e.g., below 60%), your data pipeline might be the bottleneck. If using frameworks like PyTorch, ensure your DataLoader uses multiple workers (num_workers>0) and prefetches batches. Large datasets stored on slow disks or complex preprocessing (e.g., on-the-fly augmentations) can stall training—consider caching preprocessed data or switching to a faster storage solution. If VRAM is exhausted, reduce batch size or enable gradient accumulation to simulate larger batches without memory spikes.
Next, inspect hyperparameters and model architecture. A learning rate that’s too low can make progress imperceptibly slow, while a high rate might cause unstable loss values that appear stuck. Try a learning rate finder or adjust based on validation loss trends. Check if the model’s layers are frozen unintentionally (e.g., incorrect requires_grad flags in PyTorch), which would halt weight updates. For large models, consider techniques like mixed-precision training (torch.cuda.amp) or selective unfreezing (e.g., unfreeze later layers first). If training stalls at a specific step, enable logging for gradient norms and parameter updates to detect vanishing/exploding gradients. For example, gradient clipping or normalization might resolve instability.
Finally, rule out software issues and validate data integrity. Ensure you’re using updated versions of libraries (e.g., PyTorch, TensorFlow) and drivers, as bugs in older versions can cause performance issues. Profile code execution with tools like PyTorch’s torch.utils.bottleneck to identify slow functions. Verify that input data is being loaded correctly—corrupted samples or misaligned labels (e.g., all labels set to a single class) can cause the model to plateau. Add periodic validation checks: if loss/metrics on a small validation subset aren’t improving, the model might be stuck in a local minimum. As a last resort, test with a smaller dataset or simpler model (e.g., a pretrained BERT-base instead of a large variant) to isolate whether the issue is systemic or scale-related.