This skill introduces the use of mixed precision (FP16 + FP32) to accelerate training while maintaining model accuracy. It focuses on numerical stability, hardware support, and integration with modern ML frameworks.
Requirements
External: Basic understanding of floating point computation
Internal: BDA5.3.1 Pytorch or BDA5.3.2 Tensorflow (recommended)
Learning Outcomes
Define mixed precision training and describe its benefits in performance and memory usage.
Identify hardware and software prerequisites for mixed precision support (e.g., NVIDIA Tensor Cores, AMP).
Apply automatic mixed precision (AMP) in PyTorch and TensorFlow workflows.
Monitor for numerical instability and apply scaling techniques as needed.
Benchmark training speed and accuracy trade-offs using mixed precision.