# torch随机

torch.random.fork_rng(devices=None, enabled=True, _caller='fork_rng', _devices_kw='devices')¶


• 设备(可迭代的 CUDA ID 的）–派生 RNG 的 CUDA 设备。 CPU RNG 状态始终为分叉。 默认情况下， fork_rng() 可在所有设备上运行，但是如果您的计算机上有很多设备，则将发出警告，因为在这种情况下此功能运行非常缓慢。 如果您明确指定设备，该警告将被取消

• 启用 (bool )–如果False，则不分叉 RNG。 这是一个方便的参数，用于轻松禁用上下文管理器，而不必删除它并取消其下的 Python 代码的缩进。

torch.random.get_rng_state()¶


torch.random.initial_seed()¶


torch.random.manual_seed(seed)¶


Parameters

torch.random.seed()¶


torch.random.set_rng_state(new_state)¶


Parameters

new_state (torch.ByteTensor )–所需状态

## 随机数发生器

torch.random.get_rng_state()


Returns the random number generator state as a <cite>torch.ByteTensor</cite>.

torch.random.set_rng_state(new_state)


Sets the random number generator state.

Parameters

new_state (torch.ByteTensor) – The desired state

torch.random.manual_seed(seed)


Sets the seed for generating random numbers. Returns a <cite>torch.Generator</cite> object.

Parameters

seed (python:int) – The desired seed.

torch.random.seed()


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.initial_seed()


Returns the initial seed for generating random numbers as a Python <cite>long</cite>.

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.

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 suppressed

• enabled (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.