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| """Device selection and reproducibility helpers. | |
| Centralised so the same logic can't drift between trainers and inference. | |
| """ | |
| from __future__ import annotations | |
| import random | |
| import numpy as np | |
| import torch | |
| def pick_device() -> torch.device: | |
| """Return the most capable torch device available. | |
| Order of preference: Apple Silicon MPS, NVIDIA CUDA, CPU. Chosen here | |
| because the project's primary target is M-series Macs; CUDA support is | |
| a free bonus when running on a workstation. | |
| """ | |
| if torch.backends.mps.is_available(): | |
| return torch.device("mps") | |
| if torch.cuda.is_available(): | |
| return torch.device("cuda") | |
| return torch.device("cpu") | |
| def seed_everything(seed: int) -> None: | |
| """Set Python, NumPy and PyTorch RNG seeds for reproducible runs. | |
| Note: full bitwise reproducibility on MPS isn't guaranteed by PyTorch; | |
| this gets us *seed-driven* identical splits and broadly identical training | |
| trajectories, which is what we want for science. | |
| """ | |
| random.seed(seed) | |
| np.random.seed(seed) | |
| torch.manual_seed(seed) | |
| if torch.cuda.is_available(): | |
| torch.cuda.manual_seed_all(seed) | |