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# inference_mode

class torch.inference_mode(mode=True)

  Context-manager that enables or disables inference mode

  InferenceMode is a new context manager analogous to `no_grad` to be used when you are certain your operations will have no interactions with autograd (e.g., model training). Code run under this mode gets better performance by disabling view tracking and version counter bumps. Note that unlike some other mechanisms that locally enable or disable grad, entering inference_mode also disables to forward-mode AD.

  This context manager is thread local; it will not affect computation in other threads.

  Also functions as a decorator.

  Note

  Inference mode is one of several mechanisms that can enable or disable gradients locally see Locally disabling gradient computation for more information on how they compare.

  Parameters:

    mode (bool or function) – Either a boolean flag whether to enable or disable inference mode or a Python function to decorate with inference mode enabled

  Example::

        >>> import torch
        >>> x = torch.ones(1, 2, 3, requires_grad=True)
        >>> with torch.inference_mode():
        ...     y = x * x
        >>> y.requires_grad
        False
        >>> y._version
        Traceback (most recent call last):
        File "<stdin>", line 1, in <module>
        RuntimeError: Inference tensors do not track version counter.
        >>> @torch.inference_mode()
        ... def func(x):
        ...     return x * x
        >>> out = func(x)
        >>> out.requires_grad
        False
        >>> @torch.inference_mode
        ... def doubler(x):
        ...     return x * 2
        >>> out = doubler(x)
        >>> out.requires_grad
        False

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