Hello. I still have some questions. When coding with PyTorch, if we execute half(), we set the module and input in FP16. This is what I get from “Mix-precision for training PyTorch”(https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html#pytorch 109)
My questions are:

When we have trained a model (without Mixed-Precision), can we accelerate the inference by transferring the model to FP16 (by calling .half() ) ?
2.For using Tensor Cores for acceleration of inference, must I also use mixed-precision for training a model?
3.Once we have the model in FP16, the PyTorch will automatically use Tensor Cores? Are there any prerequisites? Like CUDA version >= 9, cuDNN enabled, etc.

Thanks A LOT!