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.. 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.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.
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.is_deterministic` and :func:`torch.set_deterministic`.
.. 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
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