import torch
from spotoptim.utils.seed import seed_everything
seed_everything(42)
a = torch.rand(3)
seed_everything(42)
b = torch.rand(3)
print(torch.equal(a, b))Trueutils.seed
utils.seed
Global seeding for fully reproducible torch experiments.
Ported from the seed_everything helper of the schu25a compressor-map study (src/rnn/utils.py): seeds Python, NumPy, and torch (CPU, CUDA, and MPS) and switches cuDNN to deterministic mode, so that repeated runs on the same device produce bit-identical results.
Requires the torch optional extra (pip install 'spotoptim[torch]').
Functions
| Name | Description |
|---|---|
| seed_everything | Seed every random number generator relevant to a torch experiment. |
seed_everything
utils.seed.seed_everything(seed)Seed every random number generator relevant to a torch experiment.
Seeds Python’s random, PYTHONHASHSEED, NumPy, and torch (CPU and CUDA unconditionally, MPS when available), and sets torch.backends.cudnn.deterministic = True and torch.backends.cudnn.benchmark = False.
Parameters
| Name | Type | Description | Default |
|---|---|---|---|
| seed | int | The seed value. | required |
Note
- cuDNN determinism and benchmark flags are process-global side effects; they may slow down convolutional workloads.
- Unlike the schu25a original, the MPS generator is only seeded when the MPS backend is available, so the function also works on CPU-only Linux hosts.