seed

Functions

pl_worker_init_function	The worker_init_fn that Lightning automatically adds to your dataloader if you previously set set the seed with seed_everything(seed, workers=True).
reset_seed	Reset the seed to the value that pytorch_lightning.utilities.seed.seed_everything() previously set.
seed_everything	Function that sets seed for pseudo-random number generators in: pytorch, numpy, python.random In addition, sets the following environment variables:

Helper functions to help with reproducibility of models.

pytorch_lightning.utilities.seed.pl_worker_init_function(worker_id, rank=None)

The worker_init_fn that Lightning automatically adds to your dataloader if you previously set set the seed with seed_everything(seed, workers=True). See also the PyTorch documentation on randomness in DataLoaders.

pytorch_lightning.utilities.seed.reset_seed()

Reset the seed to the value that pytorch_lightning.utilities.seed.seed_everything() previously set. If pytorch_lightning.utilities.seed.seed_everything() is unused, this function will do nothing.

Return type

None

pytorch_lightning.utilities.seed.seed_everything(seed=None, workers=False)

Function that sets seed for pseudo-random number generators in: pytorch, numpy, python.random In addition, sets the following environment variables:

• PL_GLOBAL_SEED: will be passed to spawned subprocesses (e.g. ddp_spawn backend).

• PL_SEED_WORKERS: (optional) is set to 1 if workers=True.

Parameters

• seed (Optional[int]) – the integer value seed for global random state in Lightning. If None, will read seed from PL_GLOBAL_SEED env variable or select it randomly.

• workers (bool) – if set to True, will properly configure all dataloaders passed to the Trainer with a worker_init_fn. If the user already provides such a function for their dataloaders, setting this argument will have no influence. See also: pl_worker_init_function().

Return type

int