.. role:: hidden :class: hidden-section 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 ^^^^^^^^^^^^^^^^^^^ .. autofunction:: torch.backends.cuda.is_built .. attribute:: torch.backends.cuda.matmul.allow_tf32 A :class:`bool` that controls whether TensorFloat-32 tensor cores may be used in matrix multiplications on Ampere or newer GPUs. See :ref:`tf32_on_ampere`. .. attribute:: torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction A :class:`bool` that controls whether reduced precision reductions (e.g., with fp16 accumulation type) are allowed with fp16 GEMMs. .. attribute:: torch.backends.cuda.cufft_plan_cache ``cufft_plan_cache`` caches the cuFFT plans .. attribute:: size A readonly :class:`int` that shows the number of plans currently in the cuFFT plan cache. .. attribute:: max_size A :class:`int` that controls cache capacity of cuFFT plan. .. method:: clear() Clears the cuFFT plan cache. .. autofunction:: torch.backends.cuda.preferred_linalg_library torch.backends.cudnn ^^^^^^^^^^^^^^^^^^^^ .. autofunction:: torch.backends.cudnn.version .. autofunction:: torch.backends.cudnn.is_available .. attribute:: torch.backends.cudnn.enabled A :class:`bool` that controls whether cuDNN is enabled. .. attribute:: torch.backends.cudnn.allow_tf32 A :class:`bool` that controls where TensorFloat-32 tensor cores may be used in cuDNN convolutions on Ampere or newer GPUs. See :ref:`tf32_on_ampere`. .. attribute:: torch.backends.cudnn.deterministic A :class:`bool` that, if True, causes cuDNN to only use deterministic convolution algorithms. See also :func:`torch.are_deterministic_algorithms_enabled` and :func:`torch.use_deterministic_algorithms`. .. attribute:: torch.backends.cudnn.benchmark A :class:`bool` that, if True, causes cuDNN to benchmark multiple convolution algorithms and select the fastest. torch.backends.mkl ^^^^^^^^^^^^^^^^^^ .. autofunction:: torch.backends.mkl.is_available torch.backends.mkldnn ^^^^^^^^^^^^^^^^^^^^^ .. autofunction:: torch.backends.mkldnn.is_available torch.backends.openmp ^^^^^^^^^^^^^^^^^^^^^ .. autofunction:: torch.backends.openmp.is_available