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Table of Contents

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

Notes

  * Automatic Mixed Precision examples 
  * [ Autograd mechanics ](autograd.html)
  * [ Broadcasting semantics ](broadcasting.html)
  * [ CPU threading and TorchScript inference ](cpu_threading_torchscript_inference.html)
  * [ CUDA semantics ](cuda.html)
  * [ Distributed Data Parallel ](ddp.html)
  * [ Extending PyTorch ](extending.html)
  * [ Frequently Asked Questions ](faq.html)
  * [ Features for large-scale deployments ](large_scale_deployments.html)
  * [ Multiprocessing best practices ](multiprocessing.html)
  * [ Reproducibility ](randomness.html)
  * [ Serialization semantics ](serialization.html)
  * [ Windows FAQ ](windows.html)

Language Bindings

  * [ C++ ](../cpp_index.html)
  * [ Javadoc ](https://pytorch.org/javadoc/)

Python API

  * [ torch ](../torch.html)
  * [ torch.nn ](../nn.html)
  * [ torch.nn.functional ](../nn.functional.html)
  * [ torch.Tensor ](../tensors.html)
  * [ Tensor Attributes ](../tensor_attributes.html)
  * [ Tensor Views ](../tensor_view.html)
  * [ torch.autograd ](../autograd.html)
  * [ torch.cuda ](../cuda.html)
  * [ torch.cuda.amp ](../amp.html)
  * [ torch.distributed ](../distributed.html)
  * [ torch.distributions ](../distributions.html)
  * [ torch.futures ](../futures.html)
  * [ torch.hub ](../hub.html)
  * [ torch.jit ](../jit.html)
  * [ torch.nn.init ](../nn.init.html)
  * [ torch.onnx ](../onnx.html)
  * [ torch.optim ](../optim.html)
  * [ Complex Numbers ](../complex_numbers.html)
  * [ Quantization ](../quantization.html)
  * [ Distributed RPC Framework ](../rpc.html)
  * [ torch.random ](../random.html)
  * [ torch.sparse ](../sparse.html)
  * [ torch.Storage ](../storage.html)
  * [ torch.utils.bottleneck ](../bottleneck.html)
  * [ torch.utils.checkpoint ](../checkpoint.html)
  * [ torch.utils.cpp_extension ](../cpp_extension.html)
  * [ torch.utils.data ](../data.html)
  * [ torch.utils.dlpack ](../dlpack.html)
  * [ torch.utils.mobile_optimizer ](../mobile_optimizer.html)
  * [ torch.utils.model_zoo ](../model_zoo.html)
  * [ torch.utils.tensorboard ](../tensorboard.html)
  * [ Type Info ](../type_info.html)
  * [ Named Tensors ](../named_tensor.html)
  * [ Named Tensors operator coverage ](../name_inference.html)
  * [ torch.__config__ ](../__config__.html)

Libraries

  * [ torchaudio ](https://pytorch.org/audio)
  * [ torchtext ](https://pytorch.org/text)
  * [ torchvision ](../torchvision/index.html)
  * [ TorchElastic ](https://pytorch.org/elastic/)
  * [ TorchServe ](https://pytorch.org/serve)
  * [ PyTorch on XLA Devices ](http://pytorch.org/xla/)

Community

  * [ PyTorch Contribution Guide ](../community/contribution_guide.html)
  * [ PyTorch Governance ](../community/governance.html)
  * [ PyTorch Governance | Persons of Interest ](../community/persons_of_interest.html)

  * [ Docs ](../index.html) >
  * Automatic Mixed Precision examples 
  * [ ![](../_static/images/view-page-source-icon.svg) ](../_sources/notes/amp_examples.rst.txt)

Shortcuts

#  Automatic Mixed Precision examples  ¶

Ordinarily, “automatic mixed precision training” means training with [ `
torch.cuda.amp.autocast  ` ](../amp.html#torch.cuda.amp.autocast
"torch.cuda.amp.autocast") an