torch.random¶

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.

torch.random.
get_rng_state
()[source]¶ Returns the random number generator state as a torch.ByteTensor.

torch.random.
initial_seed
()[source]¶ Returns the initial seed for generating random numbers as a Python long.

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.

torch.random.
seed
()[source]¶ Sets the seed for generating random numbers to a nondeterministic random number. Returns a 64 bit number used to seed the RNG.

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¶

torch.random.
get_rng_state
()[source] Returns the random number generator state as a torch.ByteTensor.

torch.random.
set_rng_state
(new_state)[source] Sets the random number generator state.
 Parameters
new_state (torch.ByteTensor) – The desired state

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.

torch.random.
seed
()[source] Sets the seed for generating random numbers to a nondeterministic random number. Returns a 64 bit number used to seed the RNG.

torch.random.
initial_seed
()[source] Returns the initial seed for generating random numbers as a Python long.

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.