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torch.backends

torch.backends controls the behavior of various backends that PyTorch supports.

These backends include:

torch.backends.cuda

torch.backends.cuda.is_built

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 tf32_on_ampere.

torch.backends.cuda.cufft_plan_cache

cufft_plan_cache caches the cuFFT plans

size

A readonly int that shows the number of plans currently in the cuFFT plan cache.

max_size

A int that controls cache capacity of cuFFT plan.

clear()

Clears the cuFFT plan cache.

torch.backends.cudnn

torch.backends.cudnn.version

torch.backends.cudnn.is_available

torch.backends.cudnn.enabled

A bool that controls whether cuDNN is enabled.

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 tf32_on_ampere.

torch.backends.cudnn.deterministic

A bool that, if True, causes cuDNN to only use deterministic convolution algorithms. See also torch.are_deterministic_algorithms_enabled and torch.use_deterministic_algorithms.

torch.backends.cudnn.benchmark

A bool that, if True, causes cuDNN to benchmark multiple convolution algorithms and select the fastest.

torch.backends.mkl

torch.backends.mkl.is_available

torch.backends.mkldnn

torch.backends.mkldnn.is_available

torch.backends.openmp

torch.backends.openmp.is_available