How to Improve the Efficiency of Your PyTorch Training Loop

Improving the efficiency of a PyTorch training loop is crucial for avoiding wasted time and resources. Common bottlenecks often arise from the data pipeline, leading to "GPU starvation" where the graphics processing unit sits idle waiting for the CPU to load and preprocess data. To resolve this, PyTorch's DataLoader can be optimized using parameters like `num_workers` for parallel loading and `pin_memory` for faster CPU-to-GPU data transfers. The PyTorch Profiler is a vital tool for diagnosing these inefficiencies, allowing developers to analyze performance and ensure the GPU is utilized to its full potential.