torch.backends
torch.backends controls the behavior of various backends that PyTorch supports.
These backends include:
torch.backends.cuda
torch.backends.cudnn
torch.backends.mkl
torch.backends.mkldnn
torch.backends.openmp
torch.backends.cuda
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torch.backends.cuda.
is_built
()[source] Returns whether PyTorch is built with CUDA support. Note that this doesn’t necessarily mean CUDA is available; just that if this PyTorch binary were run a machine with working CUDA drivers and devices, we would be able to use it.
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torch.backends.cuda.matmul.
allow_tf32
A
bool
that controls whether TensorFloat-32 tensor cores may be used in matrix multiplications on Ampere or newer GPUs. See TensorFloat-32(TF32) on Ampere devices.
torch.backends.cudnn
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torch.backends.cudnn.
version
()[source] Returns the version of cuDNN
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torch.backends.cudnn.
is_available
()[source] Returns a bool indicating if CUDNN is currently available.
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torch.backends.cudnn.
enabled
A
bool
that controls whether cuDNN is enabled.
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torch.backends.cudnn.
allow_tf32
A
bool
that controls where TensorFloat-32 tensor cores may be used in cuDNN convolutions on Ampere or newer GPUs. See TensorFloat-32(TF32) on Ampere devices.
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torch.backends.cudnn.
deterministic
A
bool
that, if True, causes cuDNN to only use deterministic convolution algorithms. See alsotorch.are_deterministic_algorithms_enabled()
andtorch.use_deterministic_algorithms()
.
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torch.backends.cudnn.
benchmark
A
bool
that, if True, causes cuDNN to benchmark multiple convolution algorithms and select the fastest.
torch.backends.mkl
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torch.backends.mkl.
is_available
()[source] Returns whether PyTorch is built with MKL support.
torch.backends.mkldnn
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torch.backends.mkldnn.
is_available
()[source] Returns whether PyTorch is built with MKL-DNN support.
torch.backends.openmp
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torch.backends.openmp.
is_available
()[source] Returns whether PyTorch is built with OpenMP support.