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@ -90,7 +90,7 @@ XLA is a domain-specific compiler for linear algebra that can accelerate TensorF
orchScript is a way to create serializable and optimizable models from PyTorch code. TorchScript, an intermediate representation of a PyTorch model (subclass of nn.Module) that can then be run in a high-performance environment such as C++.
**Automatic Mixed Precision (AMP)**
Automatic Mixed Precision (AMP) enables mixed precision training on Volta, Turing, and NVIDIA Ampere GPU architectures automatically. The TensorFlow framework code makes all necessary model changes internally. This implementation exploits the TensorFlow Automatic Mixed Precision feature [TF-AMP](https://docs.nvidia.com/deeplearning/dgx/tensorflow-user-guide/index.html#tfamp) To enable AMP, you simply need to supply the `--amp` flag to `run_pretraining.py` or `run_tf_squad.py` script.
Automatic Mixed Precision (AMP) enables mixed precision training on Volta, Turing, and NVIDIA Ampere GPU architectures automatically.
**TensorFloat-32 (TF32)**
TensorFloat-32 (TF32) is the new math mode in [NVIDIA A100](https://www.nvidia.com/en-us/data-center/a100/) GPUs for handling the matrix math also called tensor operations. TF32 running on Tensor Cores in A100 GPUs can provide up to 10x speedups compared to single-precision floating-point math (FP32) on Volta GPUs. TF32 is supported in the NVIDIA Ampere GPU architecture and is enabled by default.