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Table of Contents

[ 1.6.0 ▼ ](http://pytorch.org/docs/versions.html)

Notes

  * [ Automatic Mixed Precision examples ](notes/amp_examples.html)
  * [ Autograd mechanics ](notes/autograd.html)
  * [ Broadcasting semantics ](notes/broadcasting.html)
  * [ CPU threading and TorchScript inference ](notes/cpu_threading_torchscript_inference.html)
  * [ CUDA semantics ](notes/cuda.html)
  * [ Distributed Data Parallel ](notes/ddp.html)
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  * [ Features for large-scale deployments ](notes/large_scale_deployments.html)
  * [ Multiprocessing best practices ](notes/multiprocessing.html)
  * [ Reproducibility ](notes/randomness.html)
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Python API

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  * torch.cuda.amp 
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  * Automatic Mixed Precision package - torch.cuda.amp 
  * [ ![](_static/images/view-page-source-icon.svg) ](_sources/amp.rst.txt)

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#  Automatic Mixed Precision package - torch.cuda.amp  ¶

` torch.cuda.amp  ` provides convenience methods for mixed precision, where
some operations use the ` torch.float32  ` ( ` float  ` ) datatype and other
operations use ` torch.float16  ` ( ` h