Chapter 19: Optimization and Performance Tuning with PyTorch
Abstract: Optimization and performance tuning in PyTorch are critical for efficient model training and inference, especially with large models and datasets. This involves a multi-faceted approach addressing various aspects of the training pipeline. 1. Profiling and Bottleneck Identification: PyTorch Autograd Profiler: Use torch.profiler to identify time spent in different operations (CPU, CUDA, memory). TensorBoard: Integrate SummaryWriter to visualize profiling data and track metrics. NVIDIA Nsight Systems: For system-level profiling, analyze CPU, GPU, and memory usage. 2. General Optimizations: Disable Gradients for Inference: Use torch.no_grad() or with torch.inference_mode(): during inference to save memory and computation. torch.compile : Leverage PyTorch's compiler ( torch.compile ) to fuse operations, reduce overhead, and potentially improve performance. Experiment with different ...