torch.random.fork_rng(devices=None, enabled=True, _caller='fork_rng', _devices_kw='devices')
Forks the RNG, so that when you return, the RNG is reset to the state that it was previously in.
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 suppressed
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() → torch.Tensor
Returns the random number generator state as a
torch.random.initial_seed() → int
Returns the initial seed for generating random numbers as a Python
torch.random.manual_seed(seed) → torch._C.Generator
Sets the seed for generating random numbers. Returns a
seed (int) – The desired seed. Value must be within the inclusive range
[-0x8000_0000_0000_0000, 0xffff_ffff_ffff_ffff]. Otherwise, a RuntimeError is raised. Negative inputs are remapped to positive values with the formula
0xffff_ffff_ffff_ffff + seed.
torch.random.seed() → int
Sets the seed for generating random numbers to a non-deterministic random number. Returns a 64 bit number used to seed the RNG.
torch.random.set_rng_state(new_state) → None
Sets the random number generator state.
new_state (torch.ByteTensor) – The desired state
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Licensed under the 3-clause BSD License.