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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)
  * [ Extending PyTorch ](notes/extending.html)
  * [ Frequently Asked Questions ](notes/faq.html)
  * [ Features for large-scale deployments ](notes/large_scale_deployments.html)
  * [ Multiprocessing best practices ](notes/multiprocessing.html)
  * [ Reproducibility ](notes/randomness.html)
  * [ Serialization semantics ](notes/serialization.html)
  * [ Windows FAQ ](notes/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 
  * [ 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) >
  * torch.cuda 
  * [ ![](_static/images/view-page-source-icon.svg) ](_sources/cuda.rst.txt)

Shortcuts

#  torch.cuda  ¶

This package adds support for CUDA tensor types, that implement the same
function as CPU tensors, but they utilize GPUs for computation.

It is lazily initialized, so you can always import it, and use  `
is_available()  ` to determine if your system supports CUDA.

[ CUDA