mirror of
https://github.com/vale981/ray
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55 lines
1.6 KiB
Python
55 lines
1.6 KiB
Python
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import numpy as np
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import os
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import random
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from typing import Optional
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from ray.rllib.utils.framework import try_import_tf, try_import_torch
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def update_global_seed_if_necessary(
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framework: Optional[str] = None, seed: Optional[int] = None
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) -> None:
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"""Seed global modules such as random, numpy, torch, or tf.
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This is useful for debugging and testing.
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Args:
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framework: The framework specifier (may be None).
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seed: An optional int seed. If None, will not do
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anything.
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"""
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if seed is None:
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return
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# Python random module.
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random.seed(seed)
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# Numpy.
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np.random.seed(seed)
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# Torch.
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if framework == "torch":
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torch, _ = try_import_torch()
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torch.manual_seed(seed)
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# See https://github.com/pytorch/pytorch/issues/47672.
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cuda_version = torch.version.cuda
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if cuda_version is not None and float(torch.version.cuda) >= 10.2:
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os.environ["CUBLAS_WORKSPACE_CONFIG"] = "4096:8"
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else:
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from distutils.version import LooseVersion
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if LooseVersion(torch.__version__) >= LooseVersion("1.8.0"):
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# Not all Operations support this.
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torch.use_deterministic_algorithms(True)
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else:
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torch.set_deterministic(True)
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# This is only for Convolution no problem.
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torch.backends.cudnn.deterministic = True
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elif framework == "tf2" or framework == "tfe":
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tf1, tf, _ = try_import_tf()
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# Tf2.x.
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if framework == "tf2":
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tf.random.set_seed(seed)
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# Tf-eager.
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elif framework == "tfe":
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tf1.set_random_seed(seed)
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