69 lines
2.4 KiB
Python
69 lines
2.4 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Helper functions to help with reproducibility of models. """
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import logging
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import os
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import random
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from typing import Optional
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import numpy as np
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import torch
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from pytorch_lightning.utilities import rank_zero_warn
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log = logging.getLogger(__name__)
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def seed_everything(seed: Optional[int] = None) -> int:
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"""
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Function that sets seed for pseudo-random number generators in:
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pytorch, numpy, python.random
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In addition, sets the env variable `PL_GLOBAL_SEED` which will be passed to
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spawned subprocesses (e.g. ddp_spawn backend).
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Args:
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seed: the integer value seed for global random state in Lightning.
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If `None`, will read seed from `PL_GLOBAL_SEED` env variable
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or select it randomly.
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"""
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max_seed_value = np.iinfo(np.uint32).max
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min_seed_value = np.iinfo(np.uint32).min
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try:
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if seed is None:
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seed = os.environ.get("PL_GLOBAL_SEED")
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seed = int(seed)
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except (TypeError, ValueError):
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seed = _select_seed_randomly(min_seed_value, max_seed_value)
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rank_zero_warn(f"No correct seed found, seed set to {seed}")
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if not (min_seed_value <= seed <= max_seed_value):
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rank_zero_warn(f"{seed} is not in bounds, numpy accepts from {min_seed_value} to {max_seed_value}")
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seed = _select_seed_randomly(min_seed_value, max_seed_value)
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# using `log.info` instead of `rank_zero_info`,
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# so users can verify the seed is properly set in distributed training.
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log.info(f"Global seed set to {seed}")
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os.environ["PL_GLOBAL_SEED"] = str(seed)
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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return seed
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def _select_seed_randomly(min_seed_value: int = 0, max_seed_value: int = 255) -> int:
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return random.randint(min_seed_value, max_seed_value)
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