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3e46d6c9e4
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/50048 To reflect the many changes introduced recently. In my mind, CUDAFuture should be considered a "private" subclass, which in practice should always be returned as a downcast pointer to an ivalue::Future. Hence, we should document the CUDA behavior in the superclass, even if it's CUDA-agnostic, since that's the interface the users will see also for CUDA-enabled futures. ghstack-source-id: 128640983 Test Plan: Built locally and looked at them. Reviewed By: mrshenli Differential Revision: D25757474 fbshipit-source-id: c6f66ba88fa6c4fc33601f31136422d6cf147203
21 lines
529 B
ReStructuredText
21 lines
529 B
ReStructuredText
.. currentmodule:: torch.futures
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.. _futures-docs:
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torch.futures
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=============
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This package provides a :class:`~torch.futures.Future` type that encapsulates
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an asynchronous execution and a set of utility functions to simplify operations
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on :class:`~torch.futures.Future` objects. Currently, the
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:class:`~torch.futures.Future` type is primarily used by the
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:ref:`distributed-rpc-framework`.
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.. automodule:: torch.futures
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.. autoclass:: Future
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:inherited-members:
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.. autofunction:: collect_all
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.. autofunction:: wait_all
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