To troubleshoot a slow or stuck fine-tuning process, start by checking hardware utilization and data pipeline efficiency. If your GPU or CPU usage is low (e.g., below 80-90% for GPUs), there may be bottlenecks in how data is loaded or preprocessed. For example, loading large datasets from disk without proper batching or caching can cause delays. Use tools like nvidia-smi (for GPU monitoring) or system resource monitors to identify underutilized hardware. If data loading is the issue, optimize your pipeline by using memory-mapped files, prefetching (e.g., TensorFlow’s tf.data.DATASET.prefetch), or reducing unnecessary transformations during training. Additionally, ensure your batch size isn’t too small, as this can limit GPU throughput, or too large, which might cause out-of-memory errors and force the system to waste time recovering.
Next, inspect the model architecture and training configuration. Large models with excessive parameters or inefficient layers (e.g., unoptimized custom layers) can slow down training. For example, a model with redundant attention mechanisms or unpruned layers might consume unnecessary computation. Check if gradients are propagating correctly by logging intermediate values—vanishing gradients can cause updates to stall. Verify hyperparameters: a learning rate that’s too low might make loss decrease imperceptibly, while a high rate could cause instability. Tools like PyTorch Lightning’s Profiler or TensorBoard’s trace view can help pinpoint slow operations. If using mixed-precision training, ensure it’s correctly configured (e.g., torch.cuda.amp in PyTorch) to avoid silent failures that degrade performance.
Finally, rule out software and environmental issues. Outdated framework versions (e.g., PyTorch 1.x vs. 2.x) might lack optimizations for your hardware. Check for deadlocks in distributed training setups—for example, mismatched collective operations across GPUs can hang the process. Validate your data: corrupted samples (e.g., malformed images in a vision task) might cause preprocessing to hang indefinitely. If using early stopping, confirm the patience value isn’t excessively high, which could make progress appear stalled. As a last resort, simplify the problem: try training on a tiny dataset or fewer steps to isolate whether the issue is data-dependent. For example, if a BERT fine-tuning job stalls at epoch 3, test it on 10 samples—if it completes, the bottleneck likely lies in data handling or scalability.