lightning/pytorch_lightning/trainer/seed.py

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Option to provide seed to random generators to ensure reproducibility (#1572) * 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>
2020-05-12 11:53:20 +00:00
"""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