torch.random¶
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torch.random.fork_rng(devices=None, enabled=True, _caller='fork_rng', _devices_kw='devices')[source]¶ Forks the RNG, so that when you return, the RNG is reset to the state that it was previously in.
- Parameters
devices (iterable of CUDA IDs) – CUDA devices for which to fork the RNG. CPU RNG state is always forked. By default,
fork_rng()operates on all devices, but will emit a warning if your machine has a lot of devices, since this function will run very slowly in that case. If you explicitly specify devices, this warning will be suppressedenabled (bool) – if
False, the RNG is not forked. This is a convenience argument for easily disabling the context manager without having to delete it and unindent your Python code under it.
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torch.random.get_rng_state()[source]¶ Returns the random number generator state as a torch.ByteTensor.
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torch.random.initial_seed()[source]¶ Returns the initial seed for generating random numbers as a Python long.
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torch.random.manual_seed(seed)[source]¶ Sets the seed for generating random numbers. Returns a torch.Generator object.
- Parameters
seed (python:int) – The desired seed.
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torch.random.seed()[source]¶ Sets the seed for generating random numbers to a non-deterministic random number. Returns a 64 bit number used to seed the RNG.
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torch.random.set_rng_state(new_state)[source]¶ Sets the random number generator state.
- Parameters
new_state (torch.ByteTensor) – The desired state
Random Number Generator¶
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torch.random.get_rng_state()[source] Returns the random number generator state as a torch.ByteTensor.
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torch.random.set_rng_state(new_state)[source] Sets the random number generator state.
- Parameters
new_state (torch.ByteTensor) – The desired state
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torch.random.manual_seed(seed)[source] Sets the seed for generating random numbers. Returns a torch.Generator object.
- Parameters
seed (python:int) – The desired seed.
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torch.random.seed()[source] Sets the seed for generating random numbers to a non-deterministic random number. Returns a 64 bit number used to seed the RNG.
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torch.random.initial_seed()[source] Returns the initial seed for generating random numbers as a Python long.
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torch.random.fork_rng(devices=None, enabled=True, _caller='fork_rng', _devices_kw='devices')[source] Forks the RNG, so that when you return, the RNG is reset to the state that it was previously in.
- Parameters
devices (iterable of CUDA IDs) – CUDA devices for which to fork the RNG. CPU RNG state is always forked. By default,
fork_rng()operates on all devices, but will emit a warning if your machine has a lot of devices, since this function will run very slowly in that case. If you explicitly specify devices, this warning will be suppressedenabled (bool) – if
False, the RNG is not forked. This is a convenience argument for easily disabling the context manager without having to delete it and unindent your Python code under it.
