mirror of
https://github.com/wassname/pytorch-lightning.git
synced 2026-09-09 11:32:07 +08:00
619f984c36
* Option to provide seed to random generators to ensure reproducibility I added small function in utilities which imports torch, numpy, python random and sets seed for all of the libraries to ensure reproducibility of results. * Apply recommendations from core contributors on seeding 1. Moved the seeding code to another file 2. Make deterministic as a parameter for trainer class 3. Add assertions for seeding numpy 4. Added warnings 5. torch.manual_seed should be enough for seeding torch * Revert "Apply recommendations from core contributors on seeding" This reverts commit a213c8e6882eec8a9e7408b9418926d2db7c5461. * Revert "Revert "Apply recommendations from core contributors on seeding"" This reverts commit 59b2da53c62878de7aab0aa3feb3115e105eea06. * Change in test, for correct seeding * Allow seed equal to 0 * Allow seed to be uint32.max * Added deterministic to benchmarks * Cuda manual seed as in benchmark seeding * Seeding should be done before model initialization * cuda manual_seed is not necessary * Fixing seed test_cpu_lbfgs On some seeds seems like lbfgs doesn't converge. So I fixed the seed during testing. * rebasing issue with old reproducibility.py * Improved documentation and ability to seed before initializing Train class * Change in docs * Removed seed from trainer, update for documentation * Typo in the docs * Added seed_everything to _all_ * Fixing old changes * Model initialization should be earlier then Trainer * Update pytorch_lightning/trainer/__init__.py From Example to testcode Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> * Fixing according to the contributors suggestions * Moving horovod deterministic to Trainer class * deterministic flag affects horovod docs update * Improved static typing * Added deterministic to test runners of horovod It is failing on some versions, not very predictable * static seeds for horovod tests * Change for reset_seed function in tests * Seeding horovod using reset_seed from tutils * Update pytorch_lightning/trainer/__init__.py * chlog * Update trainer.py * change "testcode" to "Example" in trainer init documentation * Update pytorch_lightning/trainer/seed.py, first line in comment Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> Co-authored-by: Jirka <jirka.borovec@seznam.cz> Co-authored-by: William Falcon <waf2107@columbia.edu>
43 lines
1.2 KiB
Python
43 lines
1.2 KiB
Python
"""Helper functions to help with reproducibility of models. """
|
|
|
|
import os
|
|
from typing import Optional
|
|
|
|
import numpy as np
|
|
import random
|
|
import torch
|
|
|
|
from pytorch_lightning import _logger as log
|
|
|
|
|
|
def seed_everything(seed: Optional[int] = None) -> int:
|
|
"""Function that sets seed for pseudo-random number generators in:
|
|
pytorch, numpy, python.random and sets PYTHONHASHSEED environment variable.
|
|
"""
|
|
max_seed_value = np.iinfo(np.uint32).max
|
|
min_seed_value = np.iinfo(np.uint32).min
|
|
|
|
try:
|
|
seed = int(seed)
|
|
except (TypeError, ValueError):
|
|
seed = _select_seed_randomly(min_seed_value, max_seed_value)
|
|
|
|
if (seed > max_seed_value) or (seed < min_seed_value):
|
|
log.warning(
|
|
f"{seed} is not in bounds, \
|
|
numpy accepts from {min_seed_value} to {max_seed_value}"
|
|
)
|
|
seed = _select_seed_randomly(min_seed_value, max_seed_value)
|
|
|
|
os.environ["PYTHONHASHSEED"] = str(seed)
|
|
random.seed(seed)
|
|
np.random.seed(seed)
|
|
torch.manual_seed(seed)
|
|
return seed
|
|
|
|
|
|
def _select_seed_randomly(min_seed_value: int = 0, max_seed_value: int = 255) -> int:
|
|
seed = random.randint(min_seed_value, max_seed_value)
|
|
log.warning(f"No correct seed found, seed set to {seed}")
|
|
return seed
|