Seed

Function: seed_everything

This function ensures reproducibility by setting the random seed for various random number generators used in Python, NumPy, and PyTorch.

Parameters:

• SEED (default: 42): The seed value for initializing the random number generators. The default value of 42 is a commonly used arbitrary choice.

''' SEED Everything '''

def seed_everything(SEED=42):

random.seed(SEED)

np.random.seed(SEED)

torch.manual_seed(SEED)

torch.cuda.manual_seed(SEED)

torch.cuda.manual_seed_all(SEED)

torch.backends.cudnn.benchmark = True

SEED=42

seed_everything(SEED=SEED)

''' SEED Everything '''

Steps in the Function:

1. random.seed(SEED)
   Sets the seed for Python's built-in random module. This ensures reproducibility for any random numbers generated using random.

2. np.random.seed(SEED)
   Sets the seed for NumPy's random number generator. This is important for reproducibility in NumPy-based computations.

3. torch.manual_seed(SEED)
   Sets the seed for PyTorch on the CPU. Ensures that operations producing random numbers on the CPU are reproducible.

4. torch.cuda.manual_seed(SEED)
   Sets the seed for PyTorch on the GPU for the current device. Ensures reproducibility for GPU-based computations.

5. torch.cuda.manual_seed_all(SEED)
   Sets the seed for all GPUs (if multiple GPUs are being used).

6. torch.backends.cudnn.benchmark = True
   Enables the CuDNN backend to optimize performance. However, note that this may slightly affect reproducibility in some cases where CuDNN chooses different algorithms for different runs.