0.21.6

Author: bartzbeielstein

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotpython"
-version = "0.21.5"
+version = "0.21.6"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]

src/spotpython/utils/seed.py

@@ -0,0 +1,38 @@
+import numpy as np
+import random
+import torch
+import os
+
+
+def set_all_seeds(seed: int):
+    """Set the seed for all relevant random number generators to ensure reproducibility.
+    This function sets the seed for Python's built-in `random` module, NumPy,
+    and PyTorch's CPU and GPU (CUDA) random number generators. It also configures
+    PyTorch's settings to improve the reproducibility of experiments, which is
+    crucial when debugging or comparing model performances.
+
+    Args:
+        seed (int): The seed value to be set for all random number generators.
+
+    Example:
+        >>> from spotpython.utils.seed import set_all_seeds
+        >>> set_all_seeds(42)
+        >>> # Proceed with model initialization or data processing to ensure results can be reproduced
+        >>> model = SomeModel()  # Replace with actual model
+        >>> train_model(model)   # Replace with actual training function
+
+    Notes:
+        - Setting `torch.backends.cudnn.deterministic` to `True` can make computations
+          more reproducible but at the potential cost of performance.
+        - Additional considerations may be necessary for complete reproducibility
+          in distributed or multi-threaded setups.
+    """
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(seed)
+        torch.backends.cudnn.deterministic = True  # Improvements for reproducibility
+        torch.backends.cudnn.benchmark = False
+
+    os.environ["PYTHONHASHSEED"] = str(seed)  # Ensuring hash-based functions use a consistent seed