pytorch - set seed everything

seed_everything.py

def seed_everything(seed: int):
    import random, os
    import numpy as np
    import torch
    
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = True
    
seed_everything(42)

Note that according to the pytorch documentation, torch.backends.cudnn.benchmark should be set to False, not True:

https://pytorch.org/docs/stable/notes/randomness.html#cuda-convolution-benchmarking

Disabling the benchmarking feature with torch.backends.cudnn.benchmark = False causes cuDNN to deterministically select an algorithm, possibly at the cost of reduced performance.
However, if you do not need reproducibility across multiple executions of your application, then performance might improve if the benchmarking feature is enabled with torch.backends.cudnn.benchmark = True.

Apple Chip
torch.mps.manual_seed(seed)

you have forgotten to seed the generator function of the train dataloader. add this:

def seed_worker(worker_id):
    worker_seed = torch.initial_seed() % 2**32
    np.random.seed(worker_seed)
    random.seed(worker_seed)

g = torch.Generator()
g.manual_seed(seed)
train_loader = torch.utils.data.DataLoader(
        eval(dset_string)(root='./data', train=True, transform=transforms.Compose([
            transforms.RandomHorizontalFlip(),
            transforms.RandomCrop(32, 4),
            transforms.ToTensor(),
            normalize,
        ]),download=True),
        batch_size=args.batch_size, shuffle=True,
        num_workers=args.workers, pin_memory=True, worker_init_fn=seed_worker)