Introduction to Mixed Precision Training In modern deep learning, mixed precision training has emerged as a game-changer, combining 16-bit floating-point (FP16) and 32-bit floating-point (FP32) arithmetic to optimize computational efficiency. The objective is clear: train faster, use less memory, and maximize hardware utilization—without sacrificing numerical stability or accuracy. However, mixed precision training introduces challenges. Underflows, … Continue reading Autocast and GradScaler in PyTorch: Revolutionizing Mixed Precision Training for the Future