Seed Everything for Reproducibility

import random, numpy as np, torch

def seed_everything(seed: int = 42):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False

Why it matters: Same seed + same code → same loss curves. If this fails, you have a hidden source of nondeterminism (data loader workers, CUDA nondet ops, etc.). Non-negotiable for debugging training runs.