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https://github.com/zebrajr/pytorch.git
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e48201c5cf
Summary: cc: ptrblck  Pull Request resolved: https://github.com/pytorch/pytorch/pull/44690 Reviewed By: ngimel Differential Revision: D23727921 Pulled By: mruberry fbshipit-source-id: db7cc8e74cde09c13d6a57683129fd839863b914
88 lines
2.1 KiB
ReStructuredText
88 lines
2.1 KiB
ReStructuredText
.. role:: hidden
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:class: hidden-section
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torch.backends
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==============
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`torch.backends` controls the behavior of various backends that PyTorch supports.
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These backends include:
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- ``torch.backends.cuda``
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- ``torch.backends.cudnn``
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- ``torch.backends.mkl``
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- ``torch.backends.mkldnn``
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- ``torch.backends.openmp``
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torch.backends.cuda
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^^^^^^^^^^^^^^^^^^^
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.. autofunction:: torch.backends.cuda.is_built
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.. attribute:: torch.backends.cuda.matmul.allow_tf32
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A :class:`bool` that controls whether TensorFloat-32 tensor cores may be used in matrix
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multiplications on Ampere or newer GPUs. See :ref:`tf32_on_ampere`.
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.. attribute:: torch.backends.cuda.cufft_plan_cache
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``cufft_plan_cache`` caches the cuFFT plans
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.. attribute:: size
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A readonly :class:`int` that shows the number of plans currently in the cuFFT plan cache.
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.. attribute:: max_size
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A :class:`int` that controls cache capacity of cuFFT plan.
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.. method:: clear()
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Clears the cuFFT plan cache.
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torch.backends.cudnn
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^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: torch.backends.cudnn.version
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.. autofunction:: torch.backends.cudnn.is_available
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.. attribute:: torch.backends.cudnn.enabled
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A :class:`bool` that controls whether cuDNN is enabled.
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.. attribute:: torch.backends.cudnn.allow_tf32
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A :class:`bool` that controls where TensorFloat-32 tensor cores may be used in cuDNN
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convolutions on Ampere or newer GPUs. See :ref:`tf32_on_ampere`.
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.. attribute:: torch.backends.cudnn.deterministic
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A :class:`bool` that, if True, causes cuDNN to only use deterministic convolution algorithms.
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See also :func:`torch.is_deterministic` and :func:`torch.set_deterministic`.
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.. attribute:: torch.backends.cudnn.benchmark
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A :class:`bool` that, if True, causes cuDNN to benchmark multiple convolution algorithms
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and select the fastest.
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torch.backends.mkl
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^^^^^^^^^^^^^^^^^^
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.. autofunction:: torch.backends.mkl.is_available
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torch.backends.mkldnn
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^^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: torch.backends.mkldnn.is_available
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torch.backends.openmp
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^^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: torch.backends.openmp.is_available
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