I’m facing performance bottlenecks while training large-scale deep neural networks on GPUs.I’ve implemented mixed precision training to leverage the speed benefits of FP16 arithmetic.However, I’m experiencing accuracy degradation and instability issues.I’m specifically using [Deep Learning Framework: TensorFlow, PyTorch, etc.] and [GPU Architecture: NVIDIA, AMD, etc.].

I’ve tried various mixed precision techniques, including loss scaling, master weights, and gradient accumulation.I’ve experimented with different FP16 data types and precision levels.I’ve adjusted hyperparameters like learning rate and batch size.I expected to achieve significant performance gains without compromising accuracy.